# One campaign, and the rule that runs through it You're in the campaign studio. **What you're doing this lesson:** run one fall relaunch through a check before anyone approves it. You do not need to write a script today. Demo code, if it appears later, is optional. You're the marketer at Fieldnote, a coffee-subscription brand. Fall relaunch in three weeks: a new subscription tier, then a two-week push across email, social, and paid. AI will draft every piece of copy. That part is settled. Nobody is going to pay you to type first drafts by hand. What they will pay you for is this: **nothing publishes without a named approver, and nothing gets in front of an approver that hasn't passed its checks.** Copy has to sound like Fieldnote before Dana sees it. Calendar rows need a yes before they hit the scheduler. Report numbers have to come from the export before anyone upstairs quotes them. The AI drafts; you check; a human named in the file approves. Three distinct jobs, and the middle one is yours. The CMO Survey's Spring 2026 wave puts AI in 24.2% of marketing activities and climbing. The drafting already moved. The checking mostly didn't. ## The brand book problem Every brand has voice guidelines. Almost none of them are checkable. "Warm but confident." "Punchy." "Sounds like a knowledgeable friend." You cannot test whether a sentence is warm — and neither can the model. That's why pasting the brand PDF into a prompt produces copy that gestures at your voice while shipping "game-changing" in line one. The fix is to split the guidelines into two piles: - **Testable rules.** "Sentences under 20 words." "No exclamation points." "One idea per line." You can check every one of these. - **Banned phrases.** The cliches, the superlatives legal can't back, the competitor names. A list. If the phrase is in the draft, it fails. A real **voice pack** (the short list of measurable rules a script can check) looks like this: three testable rules, four banned phrases, a named owner (Dana), a version, a date. You'll see one in the next steps — skip the demo if you want. When Dana tightens a rule in August, the version bumps to v4 and every check downstream picks it up. That's the difference between a voice pack and a PDF nobody opens: the pack is *attached to the campaign*, not filed next to it. ## Why write the pack down, not just paste it in the prompt You'll still put the pack in the prompt — the model drafts better with it. But the prompt is a request, not a check. The model complies most of the time, and "most of the time" across 40 variants means three or four violations sail through per batch. After the model drafts, the same pack flags every violation mechanically. Same source of truth, two jobs — brief the model going in, check the model coming out. ## Where this lesson goes Next you'll watch a linter catch a bad draft, then tell banned phrases from mood words, then write the check yourself and run a five-variant batch through it — the exact pass that runs before anything reaches Dana's approval queue. By the end, "on-voice" stops being a vibe and becomes a list that comes back empty. One placement note now, paid off in lesson 3: in a live stack this pass isn't a file you remember to run. It's a step between the model and the approval queue, so every batch walks through it whether anyone is watching or not.2e:T98f,# The linter: fifteen lines that replace an argument You know the review meeting where someone says "this doesn't sound like us" and someone else says "it sounds fine to me" and twenty minutes evaporate? A linter ends that meeting. Either the draft contains "best-in-class" or it doesn't. Either the sentence runs 27 words or it doesn't. The conversation moves from taste to fact, and fact takes four seconds. Run the editor. The whole check is: 1. **Lowercase the draft once.** Banned phrases are stored lowercase; `"Revolutionary"` and `"revolutionary"` are the same sin. Case-insensitive matching costs one `.lower()` call. 2. **Loop the banned list, substring match.** `phrase in text` — Python's `in` on strings is a substring check. Each hit appends a labeled violation, `banned: revolutionary`, so the report says *what* broke, not just that something did. 3. **Word-count check.** `len(draft.split()) > PACK["max_words"]`. Crude twice over — `split()` counts tokens, not typographer's words, and it measures the whole draft against what the pack states as a per-sentence rule. That's fine here because this draft *is* one sentence; body copy would need a split into sentences first. But crude and consistent beats precise and manual. Same yardstick every draft, every time. Note what the violations *are*: strings, in a list. Not print statements buried in the loop, not a boolean `looks_ok`. A list of labeled problems is something the pipeline can count (`len(problems) == 0` means pass), forward (attach them to the variant that failed, send it back for a redraft), and log (which rules fail most tells Dana which rules the prompt needs to state harder). ## What the linter doesn't catch — on purpose "One idea per line" is in the pack but not in this script. Some rules stay human: the linter can't tell whether a sentence carries one idea or three. That's fine. The point was never full automation — it's that every *mechanically checkable* rule gets checked mechanically, so the human reviewer spends her attention on the rules that actually need a human. Dana shouldn't burn her review pass counting words. She should burn it on "is this true, and is it us?" The division of labor, explicitly: the script catches banned phrases and length. Dana catches meaning. The model catches nothing — it's the thing being checked. Next step: a draft goes through this exact linter, and you call the output before Python does.2f:Ta16,# Variants are records, not paragraphs The old way to review AI copy: the model dumps twelve options into a doc, you scroll, you copy-paste the least bad one, and three weeks later nobody can say which subject line ran on which send or where it came from. The doc was write-only memory. The studio way: every variant is a record with a schema. Run the editor — that's one variant. The fields do real jobs: - **`id` and `channel` and `slot`** — so "E3 won the subject-line test" is a sentence with a referent. When performance data comes back in lesson 3, it joins on these fields. - **`text` and `cta`** — the actual copy, and the ask. `"cta": "none"` is a deliberate value, not a missing one: Fieldnote's pack says a CTA appears only when it earns its place, and the schema makes "no CTA" an explicit choice someone made. - **`source`** — `ai-draft`, `ai-edited`, or `human`. This field looks bureaucratic until the day someone asks what was AI-written in last quarter's campaign — for a rights question, a client dispute, a postmortem — and you can answer with a filter instead of an archaeology dig. - **`lint`** — the linter writes its violations here. Empty list means the variant may proceed to review. Non-empty means it goes back for a redraft, *with the reasons attached*. - **`approved_by`** — empty until a named human fills it. No script in this chapter ever writes to this field. That's not a technical limitation; it's the point. ## Why schema beats prose at batch volume Ask the model for "some subject lines" and you get prose to scroll. Ask it to return variants in this schema — and models are reliable at filling a stated JSON shape — and you get data to *process*: lint all twelve in a loop, sort the survivors by slot, count how many the pack rejected, and hand Dana a clean queue instead of a doc. The schema is what turns "review the copy" from an afternoon into a pass. Batch volume is where this pays. Writer's own customer story about the retailer Adore Me claims a 20-hour monthly product-description batch fell to 20 minutes with drafting agents — a vendor's number about a vendor's client, so treat the figure as marketing. But the shape of the claim is the believable part: batch drafting is where marketing time goes, and batch drafting without a batch *checking* step is how twelve variants of "game-changing" reach your send tool. You've got the linter from step 5. Next you'll wrap it into `lint_copy`, run four drafts through it, and then — checkpoint — run the whole batch and produce the queue that goes to Dana.30:T42e,PACK = { "banned": ["revolutionary", "best-in-class", "#1"], "max_words": 10, # Dana tightened the short-form cap from 12 after the first batch — one edit, every check downstream picks it up } variants = [ {"id": "E1", "channel": "email", "text": "Your Tuesday beans are on the way"}, {"id": "E2", "channel": "email", "text": "The revolutionary roast subscription"}, {"id": "S1", "channel": "social", "text": "Small batches, roasted this week, shipped this week"}, {"id": "S2", "channel": "social", "text": "The #1 best-in-class coffee experience for people who demand so much more"}, {"id": "A1", "channel": "ads", "text": "Skip a week anytime"}, ] # define lint_copy(text, pack) and on_voice_variants(variants, pack) below for v in variants: problems = lint_copy(v["text"], PACK) verdict = "PASS" if not problems else "FAIL " + "; ".join(problems) print(f"{v['id']} {v['channel']:<6} {verdict}") kept = on_voice_variants(variants, PACK) print(f"kept {len(kept)}/{len(variants)} — batch goes to Dana for approval") 31:T56d,PACK = { "banned": ["revolutionary", "best-in-class", "#1"], "max_words": 10, # Dana tightened the short-form cap from 12 after the first batch — one edit, every check downstream picks it up } variants = [ {"id": "E1", "channel": "email", "text": "Your Tuesday beans are on the way"}, {"id": "E2", "channel": "email", "text": "The revolutionary roast subscription"}, {"id": "S1", "channel": "social", "text": "Small batches, roasted this week, shipped this week"}, {"id": "S2", "channel": "social", "text": "The #1 best-in-class coffee experience for people who demand so much more"}, {"id": "A1", "channel": "ads", "text": "Skip a week anytime"}, ] def lint_copy(text, pack): problems = [] lowered = text.lower() for phrase in pack["banned"]: if phrase in lowered: problems.append(f"banned: {phrase}") if len(text.split()) > pack["max_words"]: problems.append("too long") return problems def on_voice_variants(variants, pack): return [v for v in variants if not lint_copy(v["text"], pack)] for v in variants: problems = lint_copy(v["text"], PACK) verdict = "PASS" if not problems else "FAIL " + "; ".join(problems) print(f"{v['id']} {v['channel']:<6} {verdict}") kept = on_voice_variants(variants, PACK) print(f"kept {len(kept)}/{len(variants)} — batch goes to Dana for approval") 32:Tc66,# Slots before prompts You're in the campaign studio. **What you're doing this lesson:** list every asset the Fieldnote fall relaunch needs — where it lives, how many versions, and the words on the image — before anyone opens an image tool. That list is the **batch plan**. You do not need the voice-pack lesson to start. The amateur move with image models is opening the tool and typing "cozy coffee vibes autumn." Forty regenerations later there's a folder called `final-final-v3` and nothing that fits the story format. The variable that was never pinned down: *where does this asset live?* Professionals plan the batch backwards from the **slots** (the destinations: email header, story frame, feed square, ad banner). The relaunch needs those four slots, four sizes, four different jobs. Write them as a list of records before you generate anything. Every record answers the questions that otherwise surface as rework: - **Slot and size** — the destination, named before generation starts. A tall story crop is not a wide banner with ambitions. Composition depends on the frame; the frame comes from the slot. - **Variants** — how many candidates to generate per slot. You generate several and *select*, because selection with a checklist beats regenerate-until-pretty on both cost and sanity. More variants for the ad banner (it carries the budget) than the email header (it's decorative). - **Text on the asset** — the exact words, or the explicit value "none." This field earns its keep in the next step, so clock it now: it's either precise copy — already checked against the brand voice — or a deliberate "no text." Fieldnote's plan for this relaunch, in English: six email-header candidates with no text on the image; eight story frames carrying "Fall tier. Same beans, better math."; eight feed squares with no text; ten ad banners carrying "Skip a week anytime." That's 32 generations, costed before anyone hits generate. ## Why the counts are in the plan Because counts are what turn "make some assets" into something you can cost and defend. At your image tool's per-image rate, 32 is a number — and a number means you can walk into the budget conversation with "the relaunch creative costs this much at this batch depth" instead of a shrug. When someone wants more ad variants, the plan shows exactly what the extra depth costs. You'll write that calculation two steps from now. The plan is also the review sheet's spine. When the batch comes back, every variant gets scored against its slot's brief — fits the frame, brand constraints met, text legible at target size, nothing weird in the corners. Same checks for every variant in the slot. A batch where everything passes review is a checklist that checks nothing; expect to reject, and keep the rejects scored so the next brief learns from them. One thing the plan deliberately does *not* contain: model names. Tools churn monthly; slots, sizes, variant counts, and text-on-asset are the stable layer. Write the plan against the work, point it at whatever generator you're using this quarter. First, though — a drill on the field people forget, and what forgetting it costs.33:Ta30,# The calendar is data, and the approval column is the gate Ask AI to expand the relaunch brief into a two-week calendar and it does a genuinely good job: sensible cadence, channels alternating, launch-day cluster, a winback email at the end. Expanding a brief into a grid is exactly the kind of work models are built for. Then look at what it produced: a list of things that will be *published under your brand's name*, generated in eight seconds, reviewed by no one. The grid is not the dangerous part. The dangerous part is that every scheduling tool on earth will accept the grid as-is. So the calendar gets a schema, and the schema gets a gate: ``` date, channel, piece, owner, approved_by, status ``` Two of those columns do the safety work: - **`approved_by`** — a name, or empty. Not "marketing." Not "the team." A person who read the piece and put their name in the row. Scripts fill every other column; this one only ever gets filled by a human. When the FTC, a journalist, or your CEO asks who signed off on the post, this column is the answer — which is precisely why no script writes to it. - **`status`** — `approved` or `rejected`. And here's the counterintuitive rule: **rejected rows stay in the file.** Delete a rejected row and you've deleted the record of what the team said no to. Next month, when the model proposes the same meme-post again — it will — the rejected row is how you know it was already considered and killed, and why. Run the editor. The teaser story has `status: approved` but an empty `approved_by` — someone flipped the status in a hurry without putting a name on it. The gate holds it. Status is an opinion; a named approver is accountability. The gate demands both. ## The rule with teeth Only rows that pass the gate reach the scheduling tool. This is the chapter's spine applied to publishing: the AI proposes, your script filters, a named human approves, and *then* the scheduler gets its rows. Courses and vendors mostly treat generation and publication as one continuous motion — draft it, schedule it, done. The gate is you cutting that motion in half and putting a human in the seam. Two smaller rules ride along, both learned the embarrassing way. The timezone is stated once at the top of the file — a 9am send in the wrong timezone is a 3am send in your customers' inboxes. And no channel posts twice in one day without a stated reason; if the model stacks three stories on launch day, either write the reason into the row or reject two. Next: the gate runs on four rows, and you predict what survives it before Python tells you.34:T44f,rows = [ {"date": "09-02", "channel": "email", "piece": "launch-email", "approved_by": "dana", "status": "approved"}, {"date": "09-02", "channel": "social", "piece": "teaser-story", "approved_by": "", "status": "approved"}, {"date": "09-03", "channel": "social", "piece": "meme-post", "approved_by": "dana", "status": "rejected"}, {"date": "09-04", "channel": "ads", "piece": "ad-set-a", "approved_by": "sam", "status": "approved"}, {"date": "09-05", "channel": "email", "piece": "winback", "approved_by": "sam", "status": "approved"}, ] # define gate(rows) below result = gate(rows) for r in result["schedule"]: print(f"SCHEDULE {r['date']} {r['channel']:<6} {r['piece']} ({r['approved_by']})") for r in result["blocked"]: print(f"BLOCKED {r['date']} {r['channel']:<6} {r['piece']} (no approver)") for r in result["rejected"]: print(f"KEPT {r['date']} {r['channel']:<6} {r['piece']} (rejected, stays in the file)") print(f"{len(result['schedule'])} scheduled, {len(result['blocked'])} blocked, {len(result['rejected'])} rejected-and-kept") 35:T592,rows = [ {"date": "09-02", "channel": "email", "piece": "launch-email", "approved_by": "dana", "status": "approved"}, {"date": "09-02", "channel": "social", "piece": "teaser-story", "approved_by": "", "status": "approved"}, {"date": "09-03", "channel": "social", "piece": "meme-post", "approved_by": "dana", "status": "rejected"}, {"date": "09-04", "channel": "ads", "piece": "ad-set-a", "approved_by": "sam", "status": "approved"}, {"date": "09-05", "channel": "email", "piece": "winback", "approved_by": "sam", "status": "approved"}, ] def gate(rows): result = {"schedule": [], "blocked": [], "rejected": []} for r in rows: if r["status"] == "rejected": result["rejected"].append(r) elif r["status"] == "approved" and r["approved_by"]: result["schedule"].append(r) else: result["blocked"].append(r) return result result = gate(rows) for r in result["schedule"]: print(f"SCHEDULE {r['date']} {r['channel']:<6} {r['piece']} ({r['approved_by']})") for r in result["blocked"]: print(f"BLOCKED {r['date']} {r['channel']:<6} {r['piece']} (no approver)") for r in result["rejected"]: print(f"KEPT {r['date']} {r['channel']:<6} {r['piece']} (rejected, stays in the file)") print(f"{len(result['schedule'])} scheduled, {len(result['blocked'])} blocked, {len(result['rejected'])} rejected-and-kept") 36:Tcf3,# A report is a draft over data You're in the campaign studio. **What you're doing this lesson:** check every number in the launch-week report against the **export** (the spreadsheet of actual counts the email platform downloaded). If you can't point at the cell — or show the math — the number doesn't ship. You do not need the earlier campaign lessons to start. Launch week is done. The relaunch email went to 18,200 people, and the platform export is sitting in your downloads folder. You paste it into the model and ask for a performance summary, because writing "open rate improved meaningfully week over week" by hand is nobody's favorite job and the model's narrative will be smoother than yours. The summary comes back in eight seconds and it reads great. That's the problem. It reads *equally great whether the numbers in it are real or not.* A language model producing a results paragraph is doing sentence-completion over your data, and when a rounder, more flattering number makes a better sentence, you sometimes get the better sentence. A claimed 3.6% when the export says 3.4%. An open count that appeared nowhere in the file. The failure is quiet, plausible, and wearing your formatting. So the studio rule: **every figure in an AI-drafted report is either traced to the export or it doesn't ship.** Traced means one of exactly two things: - **A lookup.** The report says 618 clicks; the export's clicks cell says 618. Match. - **A shown calculation.** The report says 3.4% **CTR** (click-through rate — clicks divided by a named base). You can point at 618 / 18,200. The formula is visible, the inputs are export cells. Anything else — however confident, however nicely phrased — is invented, and invented numbers in a campaign report are how teams make confident wrong decisions. Nobody re-checks a number that's already in a deck. The verification pass happens *before* the deck, or it never happens — which is why it runs as a scheduled check, not a heroic one-off: end of launch week, every campaign, pointed at that campaign's fresh export. Campaigns recur; so does the check. ## Watch the denominator **Open rate** is opens over *delivered*. CTR on this platform is clicks over *sent*. Same export, different **denominators** (the bottom of the fraction), and each printed line has to say which one it used. Worth knowing before you compare against a benchmark: most email platforms compute CTR over *delivered* (and click-to-open over opens), so an unlabeled "CTR" can mean three different numbers. This platform's export reports it over sent — the stated denominator is what keeps that from being a trap. A percentage that doesn't state its denominator isn't a fact yet — hold that thought, because the next read step shows the marketing industry doing this to itself with its own AI-adoption statistics. ## Why this is your check to run, not the model's You can ask the model to double-check its own summary, and you should — it catches some slips. But the model checking the model is the intern proofreading their own essay. The pass that counts is mechanical: walk every claim, compare it to the export, and mark TRACED or INVENTED with no opinion about narrative flow. You'll build exactly that in the next two steps — first by eye, then as a small script.37:T118a,# Denominators, and the list with legal teeth Two things kill more marketing reports than bad math: percentages that don't say what they're a percentage of, and claims that were never allowed to exist in the first place. ## The denominator trap Run the editor. Both statistics are real, current, and about "AI in marketing" — and they differ by a factor of three because they count different things. Salesforce's own State of Marketing 2026 report headlines 75% *of marketers* having adopted AI: any use, by anyone, counts once. The CMO Survey's Spring 2026 wave reports AI in 24.2% *of marketing activities*, up from 13.1% — activity-weighted, so one marketer using AI for one task out of twenty barely moves it. A report that says "75% of marketing is AI now" has silently swapped denominators, and it's the kind of error that survives every read-through because the sentence *sounds* fine. The rule for your own reports is the one you just built into `verify_numbers`, extended one level: every percentage states its denominator. "3.4% CTR (clicks / sent)." "27.8% open rate (opens / delivered)." A share of activities and a share of users are different facts; a report that can't tell them apart isn't a report, it's mood. ## Three lists and a fourth category Lesson 2's gate answered *who approves*. The brand-safety policy answers *what needs approving*. Three lists: - **AI may draft** — social posts, email subject lines, ad copy. Low blast radius, fully linted by lesson 1's pass, still gated by lesson 2's calendar before publishing. - **Named approver required** — pricing claims, press quotes, partner mentions, anything with a competitor's name in it. Draftable, but a specific human signs before it moves. - **Human-only** — crisis comms, apologies, anything responding to a customer's bad day. The model doesn't draft these because the cost of sounding synthetic exceeds the cost of writing it yourself. And then a fourth category that's not a list, because it's not a choice: **banned outright.** The FTC's Consumer Reviews and Testimonials Rule — 16 CFR Part 465, effective October 21, 2024 — prohibits reviews and testimonials "by someone who does not exist, such as AI-generated fake reviews," with civil penalties in the tens of thousands of dollars per knowing violation (the figure is inflation-adjusted every year — look up the current year's amount before you ever quote a specific number). Note the unit: *per violation*. A batch of twelve invented five-star reviews is not one mistake. An AI-drafted testimonial from a customer the model made up goes on no list — not draftable, not approvable-with-sign-off, gone. And more broadly, the FTC's deceptive-practices standard doesn't care who wrote the sentence; "the AI drafted it" has the same legal weight as "the intern drafted it," which is none. ## Where these checks live You now hold three working checks, and none of them belongs in a notebook you remember to open. Each has a slot in a real marketing stack: - **`lint_copy`** runs where drafts are born: as a code step in your automation tool, sitting between "the model returns variants" and "variants land in Dana's approval queue." If your drafting workflow is a script rather than an automation-platform step, the linter is the last function that script calls. Either way, no human ferries copy to the checker — the pipeline walks every batch through it, watched or not. - **`gate`** is the pre-send gate: it runs over the calendar file at the last moment before rows load into the scheduling tool. Not weekly, not "when someone remembers" — it's the doorway. Rows pass through it or they don't reach the scheduler at all. - **`verify_numbers`** is a scheduled script: end of launch week, every campaign, pointed at that campaign's platform CSV export, before the report leaves the team. Recurring, because campaigns recur — same pass, next export, forever. No vendor names in that list, for the same reason the batch plan carries no model names: tools churn quarterly, but the stack *roles* — the automation step, the pre-send gate, the scheduled report script — are stable. Whatever your team runs, each check has a slot waiting for it. A check without a slot is a check that quietly stops running by October. The next two steps drill both halves of the policy layer: first you'll spot which drafted line can't ship at any approval level, then you'll build the router that sends every piece to its correct list.38:T4b0,Checkpoint: the final pass before the relaunch results go out. Two things want to publish — a results-recap email and a victory-lap social post — and both ride on the report's numbers. Run the whole spine at once. Write `campaign_audit(claims, export, queue)`: 1. For each claim `{"metric", "value"}`, in order, print: - `f"{c['metric']:<8} {c['value']:>6} TRACED"` when the value equals `export[c["metric"]]` - `f"{c['metric']:<8} {c['value']:>6} INVENTED (export: {actual})"` otherwise 2. For each queue row `{"piece", "approved_by"}`, in order, print: - `f"{r['piece']}: OK ({r['approved_by']})"` when `approved_by` is non-empty - `f"{r['piece']}: BLOCKED (no approver)"` otherwise 3. Verdict line: if every claim traced AND every row has an approver, print `verdict: CLEARED FOR PUBLISH`. Otherwise print `f"verdict: HELD ({invented} invented number(s), {blocked} unapproved row(s))"`. Expected output: ``` sent 18200 TRACED opens 4914 TRACED clicks 618 TRACED signups 230 INVENTED (export: 212) results-recap email: OK (dana) we-hit-record post: BLOCKED (no approver) verdict: HELD (1 invented number(s), 1 unapproved row(s)) ``` 39:T4a1,export = {"sent": 18200, "opens": 4914, "clicks": 618, "signups": 212} report_claims = [ {"metric": "sent", "value": 18200}, {"metric": "opens", "value": 4914}, {"metric": "clicks", "value": 618}, {"metric": "signups", "value": 230}, ] queue = [ {"piece": "results-recap email", "approved_by": "dana"}, {"piece": "we-hit-record post", "approved_by": ""}, ] def campaign_audit(claims, export, queue): invented = 0 for c in claims: actual = export[c["metric"]] if c["value"] == actual: print(f"{c['metric']:<8} {c['value']:>6} TRACED") else: print(f"{c['metric']:<8} {c['value']:>6} INVENTED (export: {actual})") invented += 1 blocked = 0 for r in queue: if r["approved_by"]: print(f"{r['piece']}: OK ({r['approved_by']})") else: print(f"{r['piece']}: BLOCKED (no approver)") blocked += 1 if invented == 0 and blocked == 0: print("verdict: CLEARED FOR PUBLISH") else: print(f"verdict: HELD ({invented} invented number(s), {blocked} unapproved row(s))") campaign_audit(report_claims, export, queue) 3a:Tac8,# mass ugc-looking as a channel You're in the campaign studio. **What you're doing this lesson:** treat mass UGC-looking content — including brand-owned or disclosed synthetic creators — as a channel you can run on purpose, with a ship checklist. **synthetic ugc channel** — shipping storytime / creator-shaped posts at volume from accounts your brand owns or discloses, so reach and conversion sit on a plan you control. Terms, once: - **mass ugc-looking** — creator-shaped posts produced in batches (templates + fleet), not one-off "hope it goes viral" briefs - **brand-owned synthetic creator** — a software-assisted or fully synthetic profile your brand operates and discloses as such - **disclosed synthetic / paid partnership** — the label that makes the relationship material and shippable; no label, no ship - **campaign receipt** — the record you'd show your team (approver, disclosure text, batch plan, CTA) proving this drop was a deliberate channel, not a plant you copied from a competitor ## What changed AI made creator-shaped attention cheap enough to plan like media. One operator can run a narrative template across a fleet of brand-owned or disclosed accounts, park the product in the bio, and treat the batch as a channel — same way you plan email slots or paid placements. Reach can look like demand because the feed still reads it as a person. That is the lever. Your job is to run it with gates, not to pretend it is random organic. Public product pages and reporting already describe the infrastructure. Doublespeed pitches "Automated Attention" / synthetic creator stacks ([doublespeed.ai](https://doublespeed.ai/); [a16z Speedrun](https://speedrun.a16z.com/companies/doublespeed)). *404 Media* (October 24, 2025) covered a16z backing and synthetic influencers promoting products. You do not need those cites to run the drills; they are context for why this is now a real channel, not the exhibit. ## The workflow, not the farm Five beats you'll encode later: 1. **Template** — one reusable story arc (city, conflict, lesson, product beat). 2. **Fleet or batch** — how many brand-owned / disclosed accounts ship that arc this week. 3. **Owned CTA** — bio or link is the conversion path you control. 4. **Disclosure + approval** — material connection labeled; a named human clears the drop. 5. **Campaign receipt** — log what shipped so the next brief has a trail. Detection of *other people's* undisclosed plants stays useful as competitive intel and a quality bar. It is a supporting beat in this lesson, not the spine. This lesson is not a playbook for covert fake-organic or undisclosed plants. The drills use a Fieldnote campaign fixture (`fieldnote_fall_ugc`), not a takedown of a real person.3b:T808,# run the ugc channel The stack is five moves. None of them requires a named vendor to understand. 1. **Narrative template.** Storytime is cheap to templatize: a setting, a conflict, a rebound, a product beat. Write the arc once. The teaching fixture on this step (`fieldnote_fall_ugc`) uses `day_in_life_coffee` — a brand arc, not a random traveler's diary. 2. **Fleet or batch.** Decide how many brand-owned or disclosed accounts ship that arc this week. Volume is the channel; one heroic post is still a hope. 3. **Owned CTA.** While the viewer is inside the story, the bio (or link) points at your app, shop, or waitlist. The product path is yours; the drama is the wrapper. 4. **Disclosure + approval.** Label the material connection. A named human clears the drop before it ships. No label, no ship. 5. **Campaign receipt.** Log template, fleet size, CTA, disclosure text, and approver so the next brief has a trail — same habit as the numbers pass, feed instead of spreadsheet. ## Why this is a channel Paid media buys a slot. Mass UGC-looking buys *creator-shaped* attention at batch depth. When you own or disclose the accounts, you can plan it like other channels: template → fleet → CTA → gate → receipt. A marketer who only watches CPM can miss that this is now a lever, not just a competitor trick. ## Competitive detection (supporting beat) You will still see undisclosed plants in competitive scans. Use the same checklist as a quality bar and intel filter: new account + bio plant + no disclosure + cloned arc across siblings → do not brief or buy lookalikes of that "organic." That HOLD is useful. It is not the lesson spine. Do not turn detection into a covert farm you copy without disclosure. Three verdicts you'll use in the drills: - **READY_TO_SHIP** — disclosure, approver, receipt, owned CTA, and batch depth all clear - **NEEDS_WORK** — exactly one gate miss (fix and resubmit) - **BLOCKED** — two or more misses, or missing disclosure (hard gate) Next you encode the ship checklist as a small script.3c:T4d6,campaigns = [ { "id": "fieldnote_fall_ugc", "template_arc": "day_in_life_coffee", "fleet_size": 8, "bio_cta": "fieldnote.com/fall", "disclosure": "brand-owned synthetic · paid partnership", "approver": "Dana", "receipt_logged": True, }, { "id": "draft_missing_gate", "template_arc": "day_in_life_coffee", "fleet_size": 8, "bio_cta": "fieldnote.com/fall", "disclosure": "", "approver": "", "receipt_logged": False, }, ] def flags(c): hits = [] if not c["disclosure"]: hits.append("missing_disclosure") if not c["approver"]: hits.append("missing_approver") if not c["receipt_logged"]: hits.append("no_campaign_receipt") if not c["bio_cta"]: hits.append("no_owned_cta") if c["fleet_size"] < 3: hits.append("underbatched") return hits for c in campaigns: hits = flags(c) if (not c["disclosure"]) or len(hits) >= 2: verdict = "BLOCKED" elif hits: verdict = "NEEDS_WORK" else: verdict = "READY_TO_SHIP" detail = ", ".join(hits) if hits else "gates clear" print(f"{c['id']}: {verdict} ({detail})") 3d:T55a,campaigns = [ { "id": "fieldnote_fall_ugc", "template_arc": "day_in_life_coffee", "fleet_size": 8, "bio_cta": "fieldnote.com/fall", "disclosure": "brand-owned synthetic · paid partnership", "approver": "Dana", "receipt_logged": True, }, { "id": "draft_missing_gate", "template_arc": "day_in_life_coffee", "fleet_size": 8, "bio_cta": "fieldnote.com/fall", "disclosure": "", "approver": "", "receipt_logged": False, }, ] def ship_audit(campaigns): blocked = 0 for c in campaigns: hits = [] if not c["disclosure"]: hits.append("missing_disclosure") if not c["approver"]: hits.append("missing_approver") if not c["receipt_logged"]: hits.append("no_campaign_receipt") if not c["bio_cta"]: hits.append("no_owned_cta") if c["fleet_size"] < 3: hits.append("underbatched") if (not c["disclosure"]) or len(hits) >= 2: print(f"{c['id']}: BLOCKED") blocked += 1 elif hits: print(f"{c['id']}: NEEDS_WORK") else: print(f"{c['id']}: READY_TO_SHIP") print(f"blocked: {blocked}") print("calendar: HELD" if blocked else "calendar: CLEAR") ship_audit(campaigns) 28:["$","$L2b",null,{"tree":{"toc":{"version":"0.1.0","generatedAt":"1970-01-01T00:00:00.000Z","chapters":[{"number":0,"slug":"before-you-build","title":"from worker to builder","blurb":"stop doing repeated work by hand. this chapter is the map: the builder loop, old workplace deliverables turned into site artifacts, and the reusable tool card. if you are starting here, those moves are the lesson.{{#path.not:marketer}} if you already ran a studio, you already used the loop.{{/path.not}}{{#path.is:marketer}} the labs come next. campaign studio is 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/s08-08-checkpoint"}]},{"slug":"the-claim-check","steps":[{"id":"copy-studio/the-claim-check/s01-01-forty-one-of-seventy-seven"},{"id":"copy-studio/the-claim-check/s02-02-what-counts-as-checkable"},{"id":"copy-studio/the-claim-check/s03-03-predict-the-claim-list"},{"id":"copy-studio/the-claim-check/s04-04-what-closes-a-row"},{"id":"copy-studio/the-claim-check/s05-05-fill-the-ship-decision"},{"id":"copy-studio/the-claim-check/s06-06-the-corrections-log"},{"id":"copy-studio/the-claim-check/s07-07-write-ship-gate"},{"id":"copy-studio/the-claim-check/s08-08-checkpoint"}]}]},{"slug":"analytics-studio","lessons":[{"slug":"the-cleaning-log","steps":[{"id":"analytics-studio/the-cleaning-log/s01-01-where-did-those-rows-go"},{"id":"analytics-studio/the-cleaning-log/s02-02-the-log-as-data"},{"id":"analytics-studio/the-cleaning-log/s03-03-refuse-the-silent-step"},{"id":"analytics-studio/the-cleaning-log/s04-04-predict-the-receipts"},{"id":"analytics-studio/the-cleaning-log/s05-05-fill-the-null-decision"},{"id":"analytics-studio/the-cleaning-log/s06-06-fix-the-vanishing-rows"},{"id":"analytics-studio/the-cleaning-log/s07-07-write-the-null-ledger"},{"id":"analytics-studio/the-cleaning-log/s08-08-checkpoint"}]},{"slug":"every-number-has-an-address","steps":[{"id":"analytics-studio/every-number-has-an-address/s01-01-the-plausible-wrong-number"},{"id":"analytics-studio/every-number-has-an-address/s02-02-sample-first-then-full"},{"id":"analytics-studio/every-number-has-an-address/s03-03-which-check-catches-it"},{"id":"analytics-studio/every-number-has-an-address/s04-04-predict-the-fan-out"},{"id":"analytics-studio/every-number-has-an-address/s05-05-fill-the-trace"},{"id":"analytics-studio/every-number-has-an-address/s06-06-write-audit-the-memo"},{"id":"analytics-studio/every-number-has-an-address/s07-07-checkpoint"}]},{"slug":"challenge-then-rerun","steps":[{"id":"analytics-studio/challenge-then-rerun/s01-01-the-confident-recommendation"},{"id":"analytics-studio/challenge-then-rerun/s02-02-the-triad-as-a-checklist"},{"id":"analytics-studio/challenge-then-rerun/s03-03-name-the-leg"},{"id":"analytics-studio/challenge-then-rerun/s04-04-predict-the-rerun"},{"id":"analytics-studio/challenge-then-rerun/s05-05-fix-the-drifting-memo"},{"id":"analytics-studio/challenge-then-rerun/s06-06-write-score-the-challenge"},{"id":"analytics-studio/challenge-then-rerun/s07-07-checkpoint"}]}]},{"slug":"delivery-studio","lessons":[{"slug":"the-spec-is-the-build","steps":[{"id":"delivery-studio/the-spec-is-the-build/s01-01-the-clarifying-question-is-gone"},{"id":"delivery-studio/the-spec-is-the-build/s02-02-the-ticket-as-data"},{"id":"delivery-studio/the-spec-is-the-build/s03-03-spot-the-untestable-check"},{"id":"delivery-studio/the-spec-is-the-build/s04-04-predict-the-spec-lint"},{"id":"delivery-studio/the-spec-is-the-build/s05-05-fill-the-scope-fence"},{"id":"delivery-studio/the-spec-is-the-build/s06-06-inside-or-outside-the-frontier"},{"id":"delivery-studio/the-spec-is-the-build/s07-07-write-lint-spec"},{"id":"delivery-studio/the-spec-is-the-build/s08-08-checkpoint"}]},{"slug":"acceptance-before-the-build","steps":[{"id":"delivery-studio/acceptance-before-the-build/s01-01-the-ninety-seven-thousand-dollar-skip"},{"id":"delivery-studio/acceptance-before-the-build/s02-02-evidence-against-the-checks"},{"id":"delivery-studio/acceptance-before-the-build/s03-03-which-check-catches-it"},{"id":"delivery-studio/acceptance-before-the-build/s04-04-fix-the-vibes-review"},{"id":"delivery-studio/acceptance-before-the-build/s05-05-predict-the-review-run"},{"id":"delivery-studio/acceptance-before-the-build/s06-06-rework-is-a-list-not-a-feeling"},{"id":"delivery-studio/acceptance-before-the-build/s07-07-write-review-deliverable"},{"id":"delivery-studio/acceptance-before-the-build/s08-08-checkpoint"}]},{"slug":"status-from-artifacts","steps":[{"id":"delivery-studio/status-from-artifacts/s01-01-watermelon-status"},{"id":"delivery-studio/status-from-artifacts/s02-02-derive-the-rag"},{"id":"delivery-studio/status-from-artifacts/s03-03-spot-the-artifact"},{"id":"delivery-studio/status-from-artifacts/s04-04-fill-the-worst-of"},{"id":"delivery-studio/status-from-artifacts/s05-05-predict-the-rollup"},{"id":"delivery-studio/status-from-artifacts/s06-06-the-not-ready-call"},{"id":"delivery-studio/status-from-artifacts/s07-07-write-readiness-gate"},{"id":"delivery-studio/status-from-artifacts/s08-08-checkpoint"}]},{"slug":"the-hours-saved-receipt","steps":[{"id":"delivery-studio/the-hours-saved-receipt/s01-01-the-token-chart-is-not-a-receipt"},{"id":"delivery-studio/the-hours-saved-receipt/s02-02-the-receipt-as-data"},{"id":"delivery-studio/the-hours-saved-receipt/s03-03-which-line-survives-staff-meeting"},{"id":"delivery-studio/the-hours-saved-receipt/s04-04-fix-the-vanity-chart"},{"id":"delivery-studio/the-hours-saved-receipt/s05-05-predict-the-receipt-run"},{"id":"delivery-studio/the-hours-saved-receipt/s06-06-only-accepted-ships-count"},{"id":"delivery-studio/the-hours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use-extraction-with-pinpoints/s01-01-a-claim-without-a-pinpoint-is-a-rumor"},{"id":"legal-studio/clause-extraction-with-pinpoints/s02-02-the-playbook-as-data"},{"id":"legal-studio/clause-extraction-with-pinpoints/s03-03-which-row-is-unusable"},{"id":"legal-studio/clause-extraction-with-pinpoints/s04-04-predict-the-triage"},{"id":"legal-studio/clause-extraction-with-pinpoints/s05-05-fill-the-walk-away-route"},{"id":"legal-studio/clause-extraction-with-pinpoints/s06-06-write-triage-extraction"},{"id":"legal-studio/clause-extraction-with-pinpoints/s07-07-checkpoint"}]},{"slug":"privilege-rules-and-the-review-log","steps":[{"id":"legal-studio/privilege-rules-and-the-review-log/s01-01-privilege-is-a-routing-question"},{"id":"legal-studio/privilege-rules-and-the-review-log/s02-02-the-routing-table"},{"id":"legal-studio/privilege-rules-and-the-review-log/s03-03-which-request-is-the-waiver-risk"},{"id":"legal-studio/privilege-rules-and-the-review-log/s04-04-predict-the-router"},{"id":"legal-studio/privilege-rules-and-the-review-log/s05-05-fix-the-privilege-hole"},{"id":"legal-studio/privilege-rules-and-the-review-log/s06-06-the-log-that-survives-a-motion"},{"id":"legal-studio/privilege-rules-and-the-review-log/s07-07-write-log-ready"},{"id":"legal-studio/privilege-rules-and-the-review-log/s08-08-checkpoint"}]}]},{"slug":"agent-studio","lessons":[{"slug":"the-tool-contract","steps":[{"id":"agent-studio/the-tool-contract/s01-01-the-feature-you-ship-today"},{"id":"agent-studio/the-tool-contract/s02-02-the-schema-is-the-blast-radius"},{"id":"agent-studio/the-tool-contract/s03-03-spot-the-loose-tool"},{"id":"agent-studio/the-tool-contract/s04-04-predict-the-validation"},{"id":"agent-studio/the-tool-contract/s05-05-fix-the-yolo-rerun"},{"id":"agent-studio/the-tool-contract/s06-06-write-validate-call"},{"id":"agent-studio/the-tool-contract/s07-07-checkpoint"}]},{"slug":"the-loop-with-a-meter","steps":[{"id":"agent-studio/the-loop-with-a-meter/s01-01-a-loop-that-cannot-stop-is-an-invoice"},{"id":"agent-studio/the-loop-with-a-meter/s02-02-caps-are-part-of-the-loop"},{"id":"agent-studio/the-loop-with-a-meter/s03-03-why-did-it-stop"},{"id":"agent-studio/the-loop-with-a-meter/s04-04-predict-the-stop-reason"},{"id":"agent-studio/the-loop-with-a-meter/s05-05-fill-the-budget-check"},{"id":"agent-studio/the-loop-with-a-meter/s06-06-the-trace-is-the-receipt"},{"id":"agent-studio/the-loop-with-a-meter/s07-07-write-run-loop"},{"id":"agent-studio/the-loop-with-a-meter/s08-08-checkpoint"}]},{"slug":"evals-before-it-works","steps":[{"id":"agent-studio/evals-before-it-works/s01-01-green-before-it-runs"},{"id":"agent-studio/evals-before-it-works/s02-02-a-golden-case-is-a-past-incident"},{"id":"agent-studio/evals-before-it-works/s03-03-which-case-catches-it"},{"id":"agent-studio/evals-before-it-works/s04-04-predict-the-eval-run"},{"id":"agent-studio/evals-before-it-works/s05-05-fix-the-router-to-green"},{"id":"agent-studio/evals-before-it-works/s06-06-the-ratchet-entry"},{"id":"agent-studio/evals-before-it-works/s07-07-write-lint-the-ratchet"},{"id":"agent-studio/evals-before-it-works/s08-08-checkpoint"}]}]}]},"detail":{"number":49,"slug":"campaign-studio","title":"campaign studio — one campaign, end to end, with the gate on","blurb":"brief to voice-locked copy variants to batch plan to calendar to the numbers pass, then synthetic creator detection for hyperscale fake-organic. the whole campaign as data your scripts can check — and an approval gate nothing skips. built for the person who owns the send button.","overview":"$2c","status":"live","lessons":[{"slug":"voice-pack-as-data","title":"The voice pack is data — on-voice copy at batch volume","estMinutes":21,"prerequisites":[],"status":"live","steps":[{"type":"read","id":"campaign-studio/voice-pack-as-data/s01-01-the-campaign-and-the-gate","xp":1,"hint":[],"personalize":false,"phase":"warmup","estSeconds":200,"concept":"campaign-studio-setup","body":"$2d","cta":"Got it","runnable":true},{"type":"read","id":"campaign-studio/voice-pack-as-data/s02-02-lint-the-draft","xp":1,"hint":[],"personalize":false,"phase":"warmup","estSeconds":170,"concept":"voice-linting","body":"$2e","cta":"Got it","code":"PACK = {\n \"banned\": [\"best-in-class\", \"revolutionary\", \"game-changing\", \"#1\"],\n \"max_words\": 20,\n}\n\ndraft = \"Our revolutionary subscription is the best-in-class way to buy coffee.\"\n\nproblems = []\ntext = draft.lower()\nfor phrase in PACK[\"banned\"]:\n if phrase in text:\n problems.append(f\"banned: {phrase}\")\nif len(draft.split()) > PACK[\"max_words\"]:\n problems.append(\"too long\")\n\nprint(f\"{len(problems)} violation(s)\")\nfor p in problems:\n print(f\"- {p}\")\n","runnable":true},{"type":"predict","id":"campaign-studio/voice-pack-as-data/s03-03-predict-the-violations","xp":3,"hint":[{"level":1,"body":"Check each of the four banned phrases against the lowercased draft. `best-in-class` is the only one that doesn't appear. The list comprehension keeps BANNED's order, so `revolutionary` comes first even though `#1` might feel more prominent in the sentence.","cost":0},{"level":2,"body":"Three hits, in BANNED order: first line `violations: 3`, then `- revolutionary`, `- game-changing`, `- #1`.","cost":0}],"personalize":false,"phase":"build","estSeconds":70,"concept":"banned-phrase-scan","code":"BANNED = [\"best-in-class\", \"revolutionary\", \"game-changing\", \"#1\"]\n\ndraft = \"Our revolutionary roaster is the #1 choice for game-changing mornings.\"\n\nhits = [b for b in BANNED if b in draft.lower()]\nprint(f\"violations: {len(hits)}\")\nfor b in hits:\n print(f\"- {b}\")\n","prompt":"Fieldnote's fall email is in draft. The subject line the model wrote\nis exactly as bad as you fear — \"revolutionary,\" \"game-changing,\"\na \"#1\" legal would never clear. The scan walks the banned list against\nthe lowercased draft, in list order. What prints?\n\n(Watch the order: the output follows the BANNED list, not the order\nthe phrases appear in the draft.)\n","grader":{"kind":"stdout-equality","expected":"violations: 3\n- revolutionary\n- game-changing\n- #1\n","normalize":"collapse-trailing-newline"}},{"type":"mc","id":"campaign-studio/voice-pack-as-data/s04-04-testable-or-mood","xp":2,"hint":[{"level":1,"body":"The test for a testable rule: could you write an `if` statement that fails a draft against it? Exactly one candidate survives that question — it involves counting.","cost":0}],"personalize":false,"phase":"build","estSeconds":60,"concept":"testable-vs-mood-rules","prompt":"You're migrating Fieldnote's brand PDF into the voice pack. Only rules\na script can check go in the `rules` list — the rest either become\nbanned phrases or stay as human-judgment notes. Which candidate\nbelongs in `rules`?\n","options":[{"id":"under_20","label":"2 — sentences under 20 words"},{"id":"warm_prof","label":"1 — warm but professional","explain":"Warmth isn't measurable by a script. This is a mood word — it can live in the prompt to steer the draft, but it can't gate anything, because no check can fail it."},{"id":"punchy","label":"3 — punchy, thumb-stopping copy","explain":"'Punchy' has no test. Ask three marketers whether a line is punchy and you'll get four answers. Mood words go in the prompt, not the rules list."},{"id":"friend","label":"4 — sounds like a knowledgeable friend","explain":"A persona note, and a useful one — for the `reader` field or the prompt. But there's no substring match or word count that verifies 'knowledgeable friend.' Human judgment territory."},{"id":"bold","label":"5 — authentically bold","explain":"Two mood words stapled together. 'Authentic' may be the least testable word in marketing — no substring match, count, or regex measures it. Prompt material at best; it can't gate anything."}],"answerIds":["under_20"],"shuffle":false,"code":"# five lines pulled from Fieldnote's old brand PDF.\ncandidates = [\n \"warm but professional\",\n \"sentences under 20 words\",\n \"punchy, thumb-stopping copy\",\n \"sounds like a knowledgeable friend\",\n \"authentically bold\",\n]\nfor i, c in enumerate(candidates, 1):\n print(f\"{i}. {c}\")\n","runnable":true},{"type":"fill","id":"campaign-studio/voice-pack-as-data/s05-05-fill-the-linter","xp":3,"hint":[{"level":1,"body":"Python's `in` on two strings is a substring check. You want: is this banned phrase anywhere inside the lowercased draft?","cost":0},{"level":2,"body":"`phrase in text` — reads left to right as 'phrase appears in text.' Note the order: `text in phrase` would ask whether the whole draft fits inside the banned phrase, which is backwards.","cost":0}],"personalize":false,"phase":"build","estSeconds":80,"concept":"substring-banned-check","prompt":"The linter is one blank away from working. `text` is the lowercased\ndraft; `phrase` is the current banned phrase from the pack. Fill in\nthe condition that catches a banned phrase anywhere in the draft.\n\nExpected output:\n```\n['banned: revolutionary', 'banned: synergy']\n[]\n```\n","code":"PACK = {\n \"banned\": [\"best-in-class\", \"revolutionary\", \"synergy\"],\n \"max_words\": 20,\n}\n\ndef lint(line):\n problems = []\n text = line.lower()\n for phrase in PACK[\"banned\"]:\n if ___:\n problems.append(f\"banned: {phrase}\")\n if len(line.split()) > PACK[\"max_words\"]:\n problems.append(\"too long\")\n return problems\n\nprint(lint(\"A revolutionary blend with true synergy\"))\nprint(lint(\"Fresh beans, every Tuesday\"))\n","blanks":[{"id":"banned_check","accept":["phrase in text"],"caseSensitive":false,"normalize":"trim"}]},{"type":"read","id":"campaign-studio/voice-pack-as-data/s06-06-variants-as-records","xp":1,"hint":[],"personalize":false,"phase":"build","estSeconds":180,"concept":"copy-variants-as-records","body":"$2f","cta":"Got it","code":"# one drafted variant. not a paragraph in a doc — a record.\nvariant = {\n \"id\": \"E3\",\n \"channel\": \"email\",\n \"slot\": \"subject-line\",\n \"text\": \"Your Tuesday beans are on the way\",\n \"cta\": \"none\",\n \"source\": \"ai-draft\",\n \"lint\": [], # filled by the linter\n \"approved_by\": \"\", # filled by a human, never by a script\n}\n\nfor field in (\"id\", \"channel\", \"slot\", \"text\"):\n print(f\"{field:<9} {variant[field]}\")\nprint(f\"lint {'clean' if not variant['lint'] else variant['lint']}\")\n","runnable":true},{"type":"write","id":"campaign-studio/voice-pack-as-data/s07-07-write-lint-copy","xp":5,"hint":[{"level":1,"body":"Same shape as the fill step: lowercase once, loop `pack['banned']` with a substring check, then the word-count check LAST. No draft here trips both — but the caller joins violations with `; `, and when a draft does trip both (the checkpoint batch has one), the banned phrases must come before `too long`. Build the list in that order now.","cost":0},{"level":2,"body":"```python\ndef lint_copy(text, pack):\n problems = []\n lowered = text.lower()\n for phrase in pack[\"banned\"]:\n if phrase in lowered:\n problems.append(f\"banned: {phrase}\")\n if len(text.split()) > pack[\"max_words\"]:\n problems.append(\"too long\")\n return problems\n```\nV3 is 15 words against a max of 12 — that's the `too long`. V2 trips `game-changing`.\n","cost":0}],"personalize":false,"phase":"build","estSeconds":240,"concept":"write-lint-copy","prompt":"Write `lint_copy(text, pack)` — the voice check that runs on every\nAI draft before it reaches review.\n\nThese drafts are subject lines, and subject lines carry a tighter\ncap than the pack's 20-word body-sentence rule — Dana set\n`max_words` to 12 for this slot. The linter doesn't care: it reads\nwhatever cap the pack states. That's the point of pack-as-data.\n\nRules, in order:\n\n1. For each phrase in `pack[\"banned\"]` (in list order): if it appears\n in the lowercased `text`, add the violation string\n `\"banned: \"`.\n2. If the word count (`len(text.split())`) is greater than\n `pack[\"max_words\"]`, add `\"too long\"`.\n3. Return the list of violations (empty list = on-voice).\n\nFour AI drafts for the Fieldnote relaunch run through it. Expected\noutput:\n```\nV1: PASS\nV2: FAIL (banned: game-changing)\nV3: FAIL (too long)\nV4: PASS\n```\n","starter":"PACK = {\n \"banned\": [\"game-changing\", \"best-in-class\", \"revolutionary\"],\n \"max_words\": 12, # subject-line cap — tighter than the 20-word body rule\n}\n\ndrafts = [\n (\"V1\", \"Fresh beans at your door every Tuesday\"),\n (\"V2\", \"The game-changing subscription your mornings deserve\"),\n (\"V3\", \"We roast small batches weekly and ship them straight to your kitchen door for you\"),\n (\"V4\", \"Skip a week anytime, no questions\"),\n]\n\n# define lint_copy(text, pack) below\n\nfor vid, text in drafts:\n problems = lint_copy(text, PACK)\n if problems:\n print(f\"{vid}: FAIL ({'; '.join(problems)})\")\n else:\n print(f\"{vid}: PASS\")\n","grader":{"kind":"stdout-equality","expected":"V1: PASS\nV2: FAIL (banned: game-changing)\nV3: FAIL (too long)\nV4: PASS\n","normalize":"collapse-trailing-newline"},"solution":"PACK = {\n \"banned\": [\"game-changing\", \"best-in-class\", \"revolutionary\"],\n \"max_words\": 12, # subject-line cap — tighter than the 20-word body rule\n}\n\ndrafts = [\n (\"V1\", \"Fresh beans at your door every Tuesday\"),\n (\"V2\", \"The game-changing subscription your mornings deserve\"),\n (\"V3\", \"We roast small batches weekly and ship them straight to your kitchen door for you\"),\n (\"V4\", \"Skip a week anytime, no questions\"),\n]\n\ndef lint_copy(text, pack):\n problems = []\n lowered = text.lower()\n for phrase in pack[\"banned\"]:\n if phrase in lowered:\n problems.append(f\"banned: {phrase}\")\n if len(text.split()) > pack[\"max_words\"]:\n problems.append(\"too long\")\n return problems\n\nfor vid, text in drafts:\n problems = lint_copy(text, PACK)\n if problems:\n print(f\"{vid}: FAIL ({'; '.join(problems)})\")\n else:\n print(f\"{vid}: PASS\")\n","hiddenTests":[]},{"type":"checkpoint","id":"campaign-studio/voice-pack-as-data/s08-08-checkpoint","xp":8,"hint":[],"personalize":false,"phase":"check","estSeconds":280,"concept":"on-voice-batch-pass","prompt":"Checkpoint: the full voice pass. The AI drafted a five-variant batch\nfor the Fieldnote relaunch. Lint every variant, print the per-variant\nverdicts, and build the on-voice queue that goes to Dana.\n\nWrite BOTH functions:\n\n`lint_copy(text, pack)` — as before:\nbanned phrases from `pack[\"banned\"]` (in list order, case-insensitive,\nviolation string `\"banned: \"`), then `\"too long\"` if word\ncount exceeds `pack[\"max_words\"]`. Returns the violation list.\n\n`on_voice_variants(variants, pack)` — returns the list of variant\ndicts whose `lint_copy` result is empty, preserving order.\n\nThe harness at the bottom prints one line per variant and the final\nqueue count. Expected output:\n```\nE1 email PASS\nE2 email FAIL banned: revolutionary\nS1 social PASS\nS2 social FAIL banned: best-in-class; banned: #1; too long\nA1 ads PASS\nkept 3/5 — batch goes to Dana for approval\n```\n","starter":"$30","grader":{"kind":"stdout-equality","expected":"E1 email PASS\nE2 email FAIL banned: revolutionary\nS1 social PASS\nS2 social FAIL banned: best-in-class; banned: #1; too long\nA1 ads PASS\nkept 3/5 — batch goes to Dana for approval\n","normalize":"collapse-trailing-newline"},"solution":"$31","passThreshold":0.66}],"xpTotal":24},{"slug":"batch-plan-and-calendar","title":"Batch plan and calendar — creative at volume, gated before it ships","estMinutes":19,"prerequisites":["voice-pack-as-data"],"status":"live","steps":[{"type":"read","id":"campaign-studio/batch-plan-and-calendar/s01-01-slots-before-prompts","xp":1,"hint":[],"personalize":false,"phase":"warmup","estSeconds":190,"concept":"creative-batch-planning","body":"$32","cta":"Got it","runnable":true},{"type":"mc","id":"campaign-studio/batch-plan-and-calendar/s02-02-which-field-is-missing","xp":2,"hint":[{"level":1,"body":"Story frames in the wild usually carry a headline, so an unbriefed model helpfully invents one — in letters from no alphabet. Which field in lesson 1's batch plan made that decision explicit, even when the answer was 'none'?","cost":0}],"personalize":false,"phase":"build","estSeconds":70,"concept":"generation-brief-fields","prompt":"The story batch comes back and half the variants have garbled\npseudo-words baked into the image — melted letterforms where a\nheadline would go. The model was left to guess about something the\nbrief should have pinned down. Which missing field caused the rework?\n","options":[{"id":"text_on_asset","label":"text_on_asset — the exact words to render, or the explicit value \"none\""},{"id":"subject","label":"subject — the model didn't know what to draw","explain":"The subject is right there in the brief — box, counter, morning light — and the variants show it. The failure is specifically the invented text, and no field in this brief says whether text belongs on the asset at all."},{"id":"size","label":"size — without dimensions the model picked a random crop","explain":"1080x1920 is in the brief, and the variants came back vertical. Garbled letterforms aren't a crop problem — they're what image models produce when they decide, unbriefed, that a story frame usually has a headline."},{"id":"brand_constraints","label":"brand_constraints — the palette rules were too loose","explain":"The constraints line is present and reasonably tight. Palette drift would show as off-brand colors, not melted fake words. The text decision was never made, so the model made it."}],"answerIds":["text_on_asset"],"shuffle":false,"code":"# the brief that went to the image model for the story slot.\nbrief = {\n \"slot\": \"story\",\n \"size\": \"1080x1920\",\n \"subject\": \"the fall subscription box on a kitchen counter, morning light\",\n \"composition\": \"vertical, product in lower half, negative space top third\",\n \"brand_constraints\": \"warm palette, no faces, no competitor mugs\",\n}\nfor k, v in brief.items():\n print(f\"{k}: {v}\")\n","runnable":true},{"type":"write","id":"campaign-studio/batch-plan-and-calendar/s03-03-write-batch-plan","xp":4,"hint":[{"level":1,"body":"Loop the slots, accumulate two running totals, print the given f-string per slot, and `return total_assets, total_cost` at the end. The harness prints the total line for you — don't print it inside the function or it'll appear twice.","cost":0},{"level":2,"body":"```python\ndef batch_plan(slots, cost_per_image):\n total_assets = 0\n total_cost = 0.0\n for s in slots:\n line_cost = s[\"variants\"] * cost_per_image\n print(f\"{s['slot']:<13} x{s['variants']:<3} ${line_cost:.2f}\")\n total_assets += s[\"variants\"]\n total_cost += line_cost\n return total_assets, total_cost\n```\n","cost":0}],"personalize":false,"phase":"build","estSeconds":220,"concept":"batch-plan-cost","prompt":"Write `batch_plan(slots, cost_per_image)` — the function that turns\nthe creative plan into two totals. Fair warning: the dollar figure\ncomes out comically small. Generation is cheap; nobody defends $1.28\nin a budget meeting. The number that scales is the asset count —\n32 variants is 32 things a human has to review before anything\nreaches the calendar gate, and that review time is the real cost\nyou're planning here.\n\nFor each slot dict (in order), print one line:\n```\n x $\n```\ni.e. `f\"{s['slot']:<13} x{s['variants']:<3} ${s['variants'] * cost_per_image:.2f}\"`\n\nThen return a tuple `(total_assets, total_cost)` — the sum of all\nvariant counts and the sum of all line costs.\n\nExpected output:\n```\nemail-header x6 $0.24\nstory x8 $0.32\nfeed-square x8 $0.32\nad-banner x10 $0.40\ntotal: 32 assets, $1.28\n```\n","starter":"SLOTS = [\n {\"slot\": \"email-header\", \"variants\": 6},\n {\"slot\": \"story\", \"variants\": 8},\n {\"slot\": \"feed-square\", \"variants\": 8},\n {\"slot\": \"ad-banner\", \"variants\": 10},\n]\nCOST_PER_IMAGE = 0.04 # your image tool's current per-image rate\n\n# define batch_plan(slots, cost_per_image) below\n\ntotal_assets, total_cost = batch_plan(SLOTS, COST_PER_IMAGE)\nprint(f\"total: {total_assets} assets, ${total_cost:.2f}\")\n","grader":{"kind":"stdout-equality","expected":"email-header x6 $0.24\nstory x8 $0.32\nfeed-square x8 $0.32\nad-banner x10 $0.40\ntotal: 32 assets, $1.28\n","normalize":"collapse-trailing-newline"},"solution":"SLOTS = [\n {\"slot\": \"email-header\", \"variants\": 6},\n {\"slot\": \"story\", \"variants\": 8},\n {\"slot\": \"feed-square\", \"variants\": 8},\n {\"slot\": \"ad-banner\", \"variants\": 10},\n]\nCOST_PER_IMAGE = 0.04 # your image tool's current per-image rate\n\ndef batch_plan(slots, cost_per_image):\n total_assets = 0\n total_cost = 0.0\n for s in slots:\n line_cost = s[\"variants\"] * cost_per_image\n print(f\"{s['slot']:<13} x{s['variants']:<3} ${line_cost:.2f}\")\n total_assets += s[\"variants\"]\n total_cost += line_cost\n return total_assets, total_cost\n\ntotal_assets, total_cost = batch_plan(SLOTS, COST_PER_IMAGE)\nprint(f\"total: {total_assets} assets, ${total_cost:.2f}\")\n","hiddenTests":[]},{"type":"read","id":"campaign-studio/batch-plan-and-calendar/s04-04-the-calendar-is-data","xp":1,"hint":[],"personalize":false,"phase":"build","estSeconds":190,"concept":"calendar-approval-column","body":"$33","cta":"Got it","code":"# two-week relaunch calendar, first rows. timezone: America/Chicago.\nrows = [\n {\"date\": \"09-02\", \"channel\": \"email\", \"piece\": \"launch-email\",\n \"owner\": \"you\", \"approved_by\": \"dana\", \"status\": \"approved\"},\n {\"date\": \"09-02\", \"channel\": \"social\", \"piece\": \"teaser-story\",\n \"owner\": \"you\", \"approved_by\": \"\", \"status\": \"approved\"},\n {\"date\": \"09-03\", \"channel\": \"social\", \"piece\": \"meme-post\",\n \"owner\": \"you\", \"approved_by\": \"dana\", \"status\": \"rejected\"},\n]\n\nfor r in rows:\n gate = \"OK\" if r[\"status\"] == \"approved\" and r[\"approved_by\"] else \"HOLD\"\n print(f\"{r['date']} {r['channel']:<6} {r['piece']:<13} {gate}\")\n","runnable":true},{"type":"predict","id":"campaign-studio/batch-plan-and-calendar/s05-05-predict-the-queue","xp":3,"hint":[{"level":1,"body":"Walk the four rows against BOTH conditions. launch-email: approved + dana, passes. teaser-story: approved but `approved_by` is `\"\"` — empty string is falsy, fails. meme-post: has a name but status is rejected, fails. ad-set-a: approved + sam, passes.","cost":0},{"level":2,"body":"Two rows survive: `scheduled: 2`, then `09-02 launch-email` and `09-04 ad-set-a`. The teaser-story is the trap — status flipped to approved, but no human put a name in the row, and the empty string fails the gate.","cost":0}],"personalize":false,"phase":"build","estSeconds":80,"concept":"approval-gate-filter","code":"rows = [\n {\"date\": \"09-02\", \"piece\": \"launch-email\", \"approved_by\": \"dana\", \"status\": \"approved\"},\n {\"date\": \"09-02\", \"piece\": \"teaser-story\", \"approved_by\": \"\", \"status\": \"approved\"},\n {\"date\": \"09-03\", \"piece\": \"meme-post\", \"approved_by\": \"dana\", \"status\": \"rejected\"},\n {\"date\": \"09-04\", \"piece\": \"ad-set-a\", \"approved_by\": \"sam\", \"status\": \"approved\"},\n]\n\nqueue = [r for r in rows if r[\"status\"] == \"approved\" and r[\"approved_by\"]]\n\nprint(f\"scheduled: {len(queue)}\")\nfor r in queue:\n print(f\"{r['date']} {r['piece']}\")\n","prompt":"The gate runs on four calendar rows. Remember: an empty string is\nfalsy in Python, so `r[\"approved_by\"]` alone fails the `and` for a\nrow nobody put their name on. What prints?\n","grader":{"kind":"stdout-equality","expected":"scheduled: 2\n09-02 launch-email\n09-04 ad-set-a\n","normalize":"collapse-trailing-newline"}},{"type":"fix","id":"campaign-studio/batch-plan-and-calendar/s06-06-fix-the-gate","xp":3,"hint":[{"level":1,"body":"The `if` checks one condition; the gate needs two. Add the approver check with `and` — an empty string is falsy, so `r[\"approved_by\"]` by itself is the test for 'a human put a name here.'","cost":0},{"level":2,"body":"Change line 12 to: `if r[\"status\"] == \"approved\" and r[\"approved_by\"]:` — the teaser-story row then fails the second condition and the queue drops from 3 rows to 2.","cost":0}],"personalize":false,"phase":"build","estSeconds":110,"concept":"fix-approval-gate","brokenCode":"rows = [\n {\"date\": \"09-02\", \"channel\": \"email\", \"piece\": \"launch-email\", \"approved_by\": \"dana\", \"status\": \"approved\"},\n {\"date\": \"09-02\", \"channel\": \"social\", \"piece\": \"teaser-story\", \"approved_by\": \"\", \"status\": \"approved\"},\n {\"date\": \"09-03\", \"channel\": \"social\", \"piece\": \"meme-post\", \"approved_by\": \"dana\", \"status\": \"rejected\"},\n {\"date\": \"09-05\", \"channel\": \"email\", \"piece\": \"winback\", \"approved_by\": \"sam\", \"status\": \"approved\"},\n]\n\ndef publish_queue(rows):\n queue = []\n for r in rows:\n # bug: status alone is not approval — no-name rows slip through\n if r[\"status\"] == \"approved\":\n queue.append(r)\n return queue\n\nq = publish_queue(rows)\nprint(f\"queue: {len(q)} rows\")\nfor r in q:\n print(f\"{r['date']} {r['piece']} -> approved by {r['approved_by']}\")\n","prompt":"This gate is leaking. The teaser-story row has `status: approved` but\nan empty `approved_by` — someone flipped the status without putting a\nname on it — and the current gate schedules it anyway. In September\nthat's an unreviewed AI draft going out under the brand's name.\n\nFix `publish_queue` so a row needs BOTH `status == \"approved\"` AND a\nnon-empty `approved_by` to reach the queue.\n\nExpected output:\n```\nqueue: 2 rows\n09-02 launch-email -> approved by dana\n09-05 winback -> approved by sam\n```\n","grader":{"kind":"stdout-equality","expected":"queue: 2 rows\n09-02 launch-email -> approved by dana\n09-05 winback -> approved by sam\n","normalize":"collapse-trailing-newline"},"bugLines":[11,12],"revealAfter":6},{"type":"checkpoint","id":"campaign-studio/batch-plan-and-calendar/s07-07-checkpoint","xp":8,"hint":[],"personalize":false,"phase":"check","estSeconds":280,"concept":"full-calendar-gate","prompt":"Checkpoint: the full calendar gate, the one that runs before anything\ntouches the scheduling tool.\n\nWrite `gate(rows)` returning a dict with three lists, each preserving\nrow order:\n\n- `\"schedule\"` — rows with `status == \"approved\"` AND a non-empty\n `approved_by`. These may enter the scheduler.\n- `\"blocked\"` — rows with `status == \"approved\"` but NO approver.\n These go back for a named sign-off, not into the scheduler.\n- `\"rejected\"` — rows with `status == \"rejected\"`. These stay in the\n file as the record of what the team said no to.\n\nThe harness prints each list and a summary. The rows are the full\nrelaunch calendar so far — including the teaser-story that's been\nsitting nameless since the gate first caught it. Expected output:\n```\nSCHEDULE 09-02 email launch-email (dana)\nSCHEDULE 09-04 ads ad-set-a (sam)\nSCHEDULE 09-05 email winback (sam)\nBLOCKED 09-02 social teaser-story (no approver)\nKEPT 09-03 social meme-post (rejected, stays in the file)\n3 scheduled, 1 blocked, 1 rejected-and-kept\n```\n","starter":"$34","grader":{"kind":"stdout-equality","expected":"SCHEDULE 09-02 email launch-email (dana)\nSCHEDULE 09-04 ads ad-set-a (sam)\nSCHEDULE 09-05 email winback (sam)\nBLOCKED 09-02 social teaser-story (no approver)\nKEPT 09-03 social meme-post (rejected, stays in the file)\n3 scheduled, 1 blocked, 1 rejected-and-kept\n","normalize":"collapse-trailing-newline"},"solution":"$35","passThreshold":0.66}],"xpTotal":22},{"slug":"the-numbers-pass","title":"The numbers pass — every claimed metric traced to the export","estMinutes":19,"prerequisites":["voice-pack-as-data","batch-plan-and-calendar"],"status":"live","steps":[{"type":"read","id":"campaign-studio/the-numbers-pass/s01-01-a-report-is-a-draft-over-data","xp":1,"hint":[],"personalize":false,"phase":"warmup","estSeconds":190,"concept":"report-over-export","body":"$36","cta":"Got it","runnable":true},{"type":"predict","id":"campaign-studio/the-numbers-pass/s02-02-predict-the-verdict","xp":3,"hint":[{"level":1,"body":"618 / 18200 * 100 = 3.3956..., which formats to `3.4%` at one decimal. Compare that string to the claimed `3.6%`.","cost":0},{"level":2,"body":"Three lines: `claimed CTR: 3.6%`, `export CTR: 3.4%` (note the two spaces after the colon — the strings are aligned), and since 3.4 != 3.6, `verdict: invented`. The model rounded up because 3.6 made a nicer sentence. The export disagrees.","cost":0}],"personalize":false,"phase":"build","estSeconds":70,"concept":"trace-one-claim","code":"export = {\"sent\": 18200, \"opens\": 4914, \"clicks\": 618}\n\nclaimed = \"3.6%\"\nactual = export[\"clicks\"] / export[\"sent\"] * 100\n\nprint(f\"claimed CTR: {claimed}\")\nprint(f\"export CTR: {actual:.1f}%\")\nprint(\"verdict:\", \"traced\" if f\"{actual:.1f}%\" == claimed else \"invented\")\n","prompt":"The AI-drafted summary claims a 3.6% CTR for the launch email. The\nscript recomputes CTR from the export (clicks over sent — this\nplatform's convention; many tools compute CTR over delivered, which\nis exactly why the check states its denominator) at one decimal\nplace and compares. What prints?\n\n(618 / 18200 = 0.03395..., then x100.)\n","grader":{"kind":"stdout-equality","expected":"claimed CTR: 3.6%\nexport CTR: 3.4%\nverdict: invented\n","normalize":"collapse-trailing-newline"}},{"type":"write","id":"campaign-studio/the-numbers-pass/s03-03-write-verify-numbers","xp":5,"hint":[{"level":1,"body":"Loop the claims, look up `export[c['metric']]`, compare to `c['value']`, print the matching f-string from the prompt, count the matches, return the count. The clicks claim (655 vs the export's 618) is the invented one — the model inflated it.","cost":0},{"level":2,"body":"```python\ndef verify_numbers(claims, export):\n traced = 0\n for c in claims:\n actual = export[c[\"metric\"]]\n if c[\"value\"] == actual:\n print(f\"{c['label']:<13} {c['value']:>6} TRACED\")\n traced += 1\n else:\n print(f\"{c['label']:<13} {c['value']:>6} INVENTED (export: {actual})\")\n return traced\n```\n","cost":0}],"personalize":false,"phase":"build","estSeconds":240,"concept":"write-verify-numbers","prompt":"Write `verify_numbers(claims, export)` — the pass that runs on every\nAI-drafted report before it leaves the team.\n\nEach claim is a dict: `{\"label\", \"metric\", \"value\"}`. For each claim,\nin order, print one line:\n\n- if `claim[\"value\"] == export[claim[\"metric\"]]`:\n `f\"{c['label']:<13} {c['value']:>6} TRACED\"`\n- otherwise:\n `f\"{c['label']:<13} {c['value']:>6} INVENTED (export: {export[c['metric']]})\"`\n\nReturn the count of traced claims. The harness prints the summary\nline. Expected output:\n```\nemails sent 18200 TRACED\nopens 4914 TRACED\nclicks 655 INVENTED (export: 618)\nunsubscribes 43 TRACED\n3/4 traced — report is NOT approval-ready\n```\n","starter":"export = {\"sent\": 18200, \"delivered\": 17654, \"opens\": 4914, \"clicks\": 618, \"unsubs\": 43}\n\nclaims = [\n {\"label\": \"emails sent\", \"metric\": \"sent\", \"value\": 18200},\n {\"label\": \"opens\", \"metric\": \"opens\", \"value\": 4914},\n {\"label\": \"clicks\", \"metric\": \"clicks\", \"value\": 655},\n {\"label\": \"unsubscribes\", \"metric\": \"unsubs\", \"value\": 43},\n]\n\n# define verify_numbers(claims, export) below\n\ntraced = verify_numbers(claims, export)\nready = \"approval-ready\" if traced == len(claims) else \"NOT approval-ready\"\nprint(f\"{traced}/{len(claims)} traced — report is {ready}\")\n","grader":{"kind":"stdout-equality","expected":"emails sent 18200 TRACED\nopens 4914 TRACED\nclicks 655 INVENTED (export: 618)\nunsubscribes 43 TRACED\n3/4 traced — report is NOT approval-ready\n","normalize":"collapse-trailing-newline"},"solution":"export = {\"sent\": 18200, \"delivered\": 17654, \"opens\": 4914, \"clicks\": 618, \"unsubs\": 43}\n\nclaims = [\n {\"label\": \"emails sent\", \"metric\": \"sent\", \"value\": 18200},\n {\"label\": \"opens\", \"metric\": \"opens\", \"value\": 4914},\n {\"label\": \"clicks\", \"metric\": \"clicks\", \"value\": 655},\n {\"label\": \"unsubscribes\", \"metric\": \"unsubs\", \"value\": 43},\n]\n\ndef verify_numbers(claims, export):\n traced = 0\n for c in claims:\n actual = export[c[\"metric\"]]\n if c[\"value\"] == actual:\n print(f\"{c['label']:<13} {c['value']:>6} TRACED\")\n traced += 1\n else:\n print(f\"{c['label']:<13} {c['value']:>6} INVENTED (export: {actual})\")\n return traced\n\ntraced = verify_numbers(claims, export)\nready = \"approval-ready\" if traced == len(claims) else \"NOT approval-ready\"\nprint(f\"{traced}/{len(claims)} traced — report is {ready}\")\n","hiddenTests":[]},{"type":"read","id":"campaign-studio/the-numbers-pass/s04-04-denominators-and-the-ftc","xp":1,"hint":[],"personalize":false,"phase":"build","estSeconds":240,"concept":"denominators-and-banned-outright","body":"$37","cta":"Got it","code":"# two real 2026 statistics about AI in marketing. both true. not comparable.\nstats = [\n {\"figure\": \"75%\", \"of_what\": \"marketers who have adopted AI at all\",\n \"source\": \"Salesforce State of Marketing 2026 (vendor survey)\"},\n {\"figure\": \"24.2%\", \"of_what\": \"marketing activities AI is used in\",\n \"source\": \"The CMO Survey, Spring 2026 wave\"},\n]\nfor s in stats:\n print(f\"{s['figure']:>6} of {s['of_what']}\")\n print(f\" ({s['source']})\")\n","runnable":true},{"type":"mc","id":"campaign-studio/the-numbers-pass/s05-05-what-may-not-ship","xp":2,"hint":[{"level":1,"body":"Three of these are claims a human can check and sign. One is a testimonial from a person who does not exist — and 16 CFR Part 465 has a specific phrase, and a specific per-violation penalty, for exactly that.","cost":0}],"personalize":false,"phase":"build","estSeconds":70,"concept":"banned-outright-vs-reviewable","prompt":"Four AI drafts are queued for review. Three can move through the\nstudio's gates — lint, then a named approver. One may not ship at any\napproval level, with sign-off from anyone, ever. Which?\n","options":[{"id":"fake_review","label":"C — the five-star review from 'Sarah K.'"},{"id":"subject","label":"A — the email subject line","explain":"Squarely in AI-may-draft territory: lint it against the voice pack, put it in the calendar, get an approver on the row. That's what the whole pipeline is for."},{"id":"ad_copy","label":"B — 'Roasted weekly in small batches'","explain":"A factual claim, so it needs a fact-check — is the roasting actually weekly? — and an approver. But it's a checkable claim about your own product, which makes it reviewable, not banned."},{"id":"pricing","label":"D — 'The most affordable subscription in the US'","explain":"Close — an unsubstantiated superlative is deceptive if false, and this one probably dies in review. But it dies BY review: it needs substantiation and a named approver, and with real price data behind it, a version of it could ship. A category apart from the fabricated reviewer."}],"answerIds":["fake_review"],"shuffle":false,"code":"# four pieces the AI drafted for relaunch week.\ndrafted = [\n \"A: email subject — 'Your Tuesday beans, now with a fall tier'\",\n \"B: ad copy — 'Roasted weekly in small batches'\",\n \"C: five-star review from 'Sarah K., Austin' — 'Fieldnote changed my mornings!'\",\n \"D: pricing claim — 'The most affordable subscription in the US'\",\n]\nfor line in drafted:\n print(line)\n","runnable":true},{"type":"fill","id":"campaign-studio/the-numbers-pass/s06-06-fill-the-policy-router","xp":3,"hint":[{"level":1,"body":"You're asking: does this piece type appear in the current tier's list? `in` on a list is a membership check — same operator as the banned-phrase linter in the voice-pack lesson, different container.","cost":0},{"level":2,"body":"`piece in kinds` — `kinds` is the list for the current tier. 'ad copy' hits the first tier, 'testimonial' falls through to banned_outright, and 'case study' is in no list, so the function falls out of the loop to the safe default.","cost":0}],"personalize":false,"phase":"build","estSeconds":80,"concept":"policy-router-membership","prompt":"The brand-safety policy is data, so routing a piece to its list is a\nmembership check. `route` walks the policy tiers in order and returns\nthe first tier whose list contains the piece; anything unlisted\ndefaults to `named_approver` — when in doubt, a human signs.\n\nFill in the membership test.\n\nExpected output:\n```\nai_may_draft\nbanned_outright\nnamed_approver\n```\n","code":"POLICY = {\n \"ai_may_draft\": [\"social post\", \"email subject\", \"ad copy\"],\n \"named_approver\": [\"press quote\", \"pricing claim\", \"partner mention\"],\n # AI-drafted only: an AI-drafted review is fabricated by definition —\n # the FTC rule. Genuine customer voices, disclosed, are a different artifact.\n \"banned_outright\": [\"customer review\", \"testimonial\"],\n}\n\ndef route(piece):\n for tier, kinds in POLICY.items():\n if ___:\n return tier\n return \"named_approver\"\n\nprint(route(\"ad copy\"))\nprint(route(\"testimonial\"))\nprint(route(\"case study\"))\n","blanks":[{"id":"membership","accept":["piece in kinds"],"caseSensitive":false,"normalize":"trim"}]},{"type":"checkpoint","id":"campaign-studio/the-numbers-pass/s07-07-checkpoint","xp":8,"hint":[],"personalize":false,"phase":"check","estSeconds":290,"concept":"full-campaign-audit","prompt":"$38","starter":"export = {\"sent\": 18200, \"opens\": 4914, \"clicks\": 618, \"signups\": 212}\n\nreport_claims = [\n {\"metric\": \"sent\", \"value\": 18200},\n {\"metric\": \"opens\", \"value\": 4914},\n {\"metric\": \"clicks\", \"value\": 618},\n {\"metric\": \"signups\", \"value\": 230},\n]\n\nqueue = [\n {\"piece\": \"results-recap email\", \"approved_by\": \"dana\"},\n {\"piece\": \"we-hit-record post\", \"approved_by\": \"\"},\n]\n\n# define campaign_audit(claims, export, queue) below\n\ncampaign_audit(report_claims, export, queue)\n","grader":{"kind":"stdout-equality","expected":"sent 18200 TRACED\nopens 4914 TRACED\nclicks 618 TRACED\nsignups 230 INVENTED (export: 212)\nresults-recap email: OK (dana)\nwe-hit-record post: BLOCKED (no approver)\nverdict: HELD (1 invented number(s), 1 unapproved row(s))\n","normalize":"collapse-trailing-newline"},"solution":"$39","passThreshold":0.66}],"xpTotal":23},{"slug":"synthetic-creator-detection","title":"mass ugc-looking as a channel","estMinutes":20,"prerequisites":["the-numbers-pass"],"status":"live","steps":[{"type":"read","id":"campaign-studio/synthetic-creator-detection/s01-01-mass-ugc-as-a-channel","xp":1,"hint":[],"personalize":false,"phase":"warmup","estSeconds":210,"concept":"mass-ugc-as-channel","body":"$3a","cta":"Got it","runnable":true},{"type":"mc","id":"campaign-studio/synthetic-creator-detection/s02-02-what-the-job-is","xp":2,"hint":[{"level":1,"body":"Leverage the channel with disclosure and a ship checklist. Detection of competitors is supporting; an undisclosed farm is not the answer.","cost":0}],"personalize":false,"phase":"warmup","estSeconds":70,"concept":"leverage-channel-not-detection-hobby","prompt":"Mass UGC-looking / synthetic creators are in the studio as a channel\ncase. What should you take from this lesson?\n","options":[{"id":"run_the_channel","label":"How to run mass UGC-looking as a brand channel — templates, fleet/batch, owned CTA, disclosure + approval, and a campaign receipt"},{"id":"detection_only","label":"Only how to HOLD competitor fake-organic until receipts — detection is the whole job","explain":"Receipts and competitor holds are a supporting beat (intel / quality bar). The spine is running the channel yourself with gates."},{"id":"how_to_farm","label":"The steps to stand up undisclosed agentic accounts and ship fake-organic that looks like random demand","explain":"Covert / undisclosed plants are out of scope. Brand-owned or disclosed synthetic UGC only — no farm-to-deceive playbook."},{"id":"sermon","label":"Write the moral case that synthetic creators are wrong, then add an ethics slide and skip the channel","explain":"A sermon does not replace a ship checklist. If the team runs this channel, they need the operator workflow and the gates."}],"answerIds":["run_the_channel"],"shuffle":false,"runnable":true},{"type":"read","id":"campaign-studio/synthetic-creator-detection/s03-03-run-the-ugc-channel","xp":1,"hint":[],"personalize":false,"phase":"build","estSeconds":230,"concept":"ugc-channel-operator-playbook","body":"$3b","cta":"Got it","code":"# brand campaign fixture — Fieldnote fall UGC drop, not a real person.\ncampaign = {\n \"id\": \"fieldnote_fall_ugc\",\n \"template_arc\": \"day_in_life_coffee\",\n \"fleet_size\": 8,\n \"bio_cta\": \"fieldnote.com/fall\",\n \"disclosure\": \"brand-owned synthetic · paid partnership\",\n \"approver\": \"Dana\",\n \"receipt_logged\": True,\n}\nprint(campaign[\"id\"], \"fleet\", campaign[\"fleet_size\"], \"cta\", campaign[\"bio_cta\"])\n","runnable":true},{"type":"predict","id":"campaign-studio/synthetic-creator-detection/s04-04-predict-the-ship","xp":3,"hint":[{"level":1,"body":"Zero flags is READY_TO_SHIP. Missing disclosure or two-plus flags is BLOCKED. The first campaign clears; the second trips three gates.","cost":0},{"level":2,"body":"First line is `fieldnote_fall_ugc: READY_TO_SHIP (gates clear)`. Second line is `draft_missing_gate: BLOCKED (missing_disclosure, missing_approver, no_campaign_receipt)`.","cost":0}],"personalize":false,"phase":"build","estSeconds":80,"concept":"predict-ready-vs-blocked","code":"$3c","prompt":"Two Fieldnote campaign fixtures run through the same ship checklist.\n`fieldnote_fall_ugc` has disclosure, approver, receipt, CTA, and batch\ndepth. `draft_missing_gate` is the same plan with those three gates\nempty. What prints?\n","grader":{"kind":"stdout-equality","expected":"fieldnote_fall_ugc: READY_TO_SHIP (gates clear)\ndraft_missing_gate: BLOCKED (missing_disclosure, missing_approver, no_campaign_receipt)\n","normalize":"collapse-trailing-newline"}},{"type":"mc","id":"campaign-studio/synthetic-creator-detection/s05-05-what-clears-the-gate","xp":2,"hint":[{"level":1,"body":"Missing disclosure is a hard BLOCK. Fleet size and receipts do not clear an empty disclosure field.","cost":0}],"personalize":false,"phase":"build","estSeconds":70,"concept":"disclosure-required-to-ship","prompt":"Your team wants to ship an eight-account UGC-looking batch for the\nFieldnote fall drop: shared template, product in every bio, same CTA.\nApprover is Dana. Campaign receipt is logged. Disclosure field is still\nblank because \"it looks more organic that way.\" What do you do first?\n","options":[{"id":"block_for_disclosure","label":"BLOCKED — require a disclosure label (brand-owned synthetic / paid partnership) before anything ships"},{"id":"ship_organic","label":"Ship dark. Looking organic is the whole point of the channel.","explain":"Looking creator-shaped is fine. Hiding the material connection is not. Undisclosed plants are out of scope for this lesson."},{"id":"views_first","label":"Post two accounts as a test. If views clear 100k, backfill disclosure later.","explain":"Disclosure is a hard gate before ship, not a patch after reach. The checklist exists so reach never outruns the label."},{"id":"detect_competitors","label":"Ignore your batch. Spend the week HOLDing competitor posts until receipts show real people.","explain":"Competitor detection is a supporting beat. Your job here is to clear *your* ship gates — starting with disclosure."}],"answerIds":["block_for_disclosure"],"shuffle":false,"runnable":true},{"type":"fill","id":"campaign-studio/synthetic-creator-detection/s06-06-fill-the-batch-line","xp":3,"hint":[{"level":1,"body":"The second read set the batch floor at three accounts. The blank is that integer.","cost":0},{"level":2,"body":"`3` — `fleet_size < 3` is True for 1, False for 3 and 8.","cost":0}],"personalize":false,"phase":"build","estSeconds":80,"concept":"batch-floor-is-three","prompt":"Underbatched means the fleet is too thin to treat as a channel. Fill\nthe floor so a one-account hope flags `underbatched`, a three-account\nbatch clears that flag, and the rest of the ship checklist still runs.\n","code":"def underbatched(fleet_size):\n return fleet_size < ___\n\nprint(underbatched(1))\nprint(underbatched(3))\nprint(underbatched(8))\n","blanks":[{"id":"batch_floor","accept":["3"],"caseSensitive":false,"normalize":"trim"}]},{"type":"write","id":"campaign-studio/synthetic-creator-detection/s07-07-write-ship-checklist","xp":5,"hint":[{"level":1,"body":"Walk the five ifs in the prompt order, append flag names, print each as `flag: …`, then pick BLOCKED / NEEDS_WORK / READY_TO_SHIP from disclosure + count.","cost":0},{"level":2,"body":"```python\ndef ship_checklist(campaign):\n hits = []\n if not campaign[\"disclosure\"]:\n hits.append(\"missing_disclosure\")\n if not campaign[\"approver\"]:\n hits.append(\"missing_approver\")\n if not campaign[\"receipt_logged\"]:\n hits.append(\"no_campaign_receipt\")\n if not campaign[\"bio_cta\"]:\n hits.append(\"no_owned_cta\")\n if campaign[\"fleet_size\"] < 3:\n hits.append(\"underbatched\")\n for name in hits:\n print(f\"flag: {name}\")\n if (not campaign[\"disclosure\"]) or len(hits) >= 2:\n print(\"verdict: BLOCKED\")\n elif hits:\n print(\"verdict: NEEDS_WORK\")\n else:\n print(\"verdict: READY_TO_SHIP\")\n return hits\n```\n","cost":0}],"personalize":false,"phase":"build","estSeconds":240,"concept":"write-ship-checklist","prompt":"Write `ship_checklist(campaign)` — the pass that runs before a mass\nUGC-looking batch ships.\n\n`campaign` is a dict with `id`, `template_arc`, `fleet_size`,\n`bio_cta`, `disclosure`, `approver`, and `receipt_logged`.\n\nCollect flags in this order, then print them:\n- `missing_disclosure` when `disclosure` is empty\n- `missing_approver` when `approver` is empty\n- `no_campaign_receipt` when `receipt_logged` is false\n- `no_owned_cta` when `bio_cta` is empty\n- `underbatched` when `fleet_size < 3`\n\nFor each flag, print `f\"flag: {name}\"`. Then print one verdict line:\n- `verdict: BLOCKED` when disclosure is missing OR there are two or\n more flags\n- `verdict: NEEDS_WORK` when there is exactly one flag (and disclosure\n is present)\n- `verdict: READY_TO_SHIP` when there are none\n\nReturn the flag list. The harness prints the campaign id. Expected\noutput:\n```\ndraft_missing_gate\nflag: missing_disclosure\nflag: missing_approver\nflag: no_campaign_receipt\nverdict: BLOCKED\n```\n","starter":"campaign = {\n \"id\": \"draft_missing_gate\",\n \"template_arc\": \"day_in_life_coffee\",\n \"fleet_size\": 8,\n \"bio_cta\": \"fieldnote.com/fall\",\n \"disclosure\": \"\",\n \"approver\": \"\",\n \"receipt_logged\": False,\n}\n\n# define ship_checklist(campaign) below\n\nprint(campaign[\"id\"])\nship_checklist(campaign)\n","grader":{"kind":"stdout-equality","expected":"draft_missing_gate\nflag: missing_disclosure\nflag: missing_approver\nflag: no_campaign_receipt\nverdict: BLOCKED\n","normalize":"collapse-trailing-newline"},"solution":"campaign = {\n \"id\": \"draft_missing_gate\",\n \"template_arc\": \"day_in_life_coffee\",\n \"fleet_size\": 8,\n \"bio_cta\": \"fieldnote.com/fall\",\n \"disclosure\": \"\",\n \"approver\": \"\",\n \"receipt_logged\": False,\n}\n\ndef ship_checklist(campaign):\n hits = []\n if not campaign[\"disclosure\"]:\n hits.append(\"missing_disclosure\")\n if not campaign[\"approver\"]:\n hits.append(\"missing_approver\")\n if not campaign[\"receipt_logged\"]:\n hits.append(\"no_campaign_receipt\")\n if not campaign[\"bio_cta\"]:\n hits.append(\"no_owned_cta\")\n if campaign[\"fleet_size\"] < 3:\n hits.append(\"underbatched\")\n for name in hits:\n print(f\"flag: {name}\")\n if (not campaign[\"disclosure\"]) or len(hits) >= 2:\n print(\"verdict: BLOCKED\")\n elif hits:\n print(\"verdict: NEEDS_WORK\")\n else:\n print(\"verdict: READY_TO_SHIP\")\n return hits\n\nprint(campaign[\"id\"])\nship_checklist(campaign)\n","hiddenTests":[]},{"type":"checkpoint","id":"campaign-studio/synthetic-creator-detection/s08-08-checkpoint","xp":8,"hint":[],"personalize":false,"phase":"check","estSeconds":280,"concept":"two-campaign-ship-audit","prompt":"Checkpoint: two Fieldnote UGC-looking batches want onto next week's\ncalendar. Run the ship checklist on both, then decide whether the\ncalendar may treat the drop as cleared.\n\nWrite `ship_audit(campaigns)`:\n\n1. For each campaign, in order, run the same flags as\n `ship_checklist` (`missing_disclosure`, `missing_approver`,\n `no_campaign_receipt`, `no_owned_cta`, `underbatched`).\n2. Print `f\"{c['id']}: BLOCKED\"` when disclosure is missing OR there\n are two or more flags, `f\"{c['id']}: NEEDS_WORK\"` when there is\n exactly one flag (and disclosure is present), and\n `f\"{c['id']}: READY_TO_SHIP\"` when there are none.\n3. After the loop, print `f\"blocked: {n}\"` where `n` is the BLOCKED\n count, then `calendar: HELD` if any campaign blocked, otherwise\n `calendar: CLEAR`.\n\nExpected output:\n```\nfieldnote_fall_ugc: READY_TO_SHIP\ndraft_missing_gate: BLOCKED\nblocked: 1\ncalendar: HELD\n```\n","starter":"campaigns = [\n {\n \"id\": \"fieldnote_fall_ugc\",\n \"template_arc\": \"day_in_life_coffee\",\n \"fleet_size\": 8,\n \"bio_cta\": \"fieldnote.com/fall\",\n \"disclosure\": \"brand-owned synthetic · paid partnership\",\n \"approver\": \"Dana\",\n \"receipt_logged\": True,\n },\n {\n \"id\": \"draft_missing_gate\",\n \"template_arc\": \"day_in_life_coffee\",\n \"fleet_size\": 8,\n \"bio_cta\": \"fieldnote.com/fall\",\n \"disclosure\": \"\",\n \"approver\": \"\",\n \"receipt_logged\": False,\n },\n]\n\n# define ship_audit(campaigns) below\n\nship_audit(campaigns)\n","grader":{"kind":"stdout-equality","expected":"fieldnote_fall_ugc: READY_TO_SHIP\ndraft_missing_gate: BLOCKED\nblocked: 1\ncalendar: HELD\n","normalize":"collapse-trailing-newline"},"solution":"$3d","passThreshold":0.66}],"xpTotal":25}],"liveLessonCount":4,"liveStepCount":30,"xpTotal":94}},"chapter":"$28:props:tree:detail","lesson":"$28:props:tree:detail:lessons:3","step":"$28:props:tree:detail:lessons:3:steps:4","stepIndex":4,"nav":{"tocNext":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":5},"tocPrev":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":3},"nextByPath":{"developer":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":5},"marketer":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":5},"designer":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":5},"customer-service":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":5},"copywriter":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":5},"data-analyst":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":5},"project-manager":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":5},"hr-specialist":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":5},"operations":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":5},"lawyer":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":5}},"prevByPath":{"developer":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":3},"marketer":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":3},"designer":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":3},"customer-service":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":3},"copywriter":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":3},"data-analyst":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":3},"project-manager":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":3},"hr-specialist":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":3},"operations":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":3},"lawyer":{"chapterSlug":"campaign-studio","lessonSlug":"synthetic-creator-detection","stepIndex":3}}}}]

mass ugc-looking as a channel — step 5 of 8