v2.10.0 — attribution (new, 1.1.0): models, reconciliation, and first-party attribution (incl. Tessa Kriesel's production feedback)

attribution (new, 1.1.0): a dedicated skill for the hardest question in marketing — which efforts actually drive conversions and revenue. Fills a real gap: attribution was scattered across analytics (UTM setup), ads (platform pixels), revops (pipeline), and ai-seo (the AI blind spot), but no skill owned the models or the reconciliation problem. Two pillars. (A) Interpretation — the six attribution models and when each lies; MTA vs. MMM vs. incrementality and how to choose; self-reported attribution; reconciling the platform-vs-GA-vs-CRM-vs-survey disagreement (never sum across platforms; pick one source of truth; read directional trends); and the direct / branded-search / dark-social / AI blind spots. (B) Own your attribution (first-party) — a build runbook for instrumenting attribution yourself when you control the site/app: the identity graph, closing the identify() gap (adapted from Tessa Kriesel's PostHog approach), stitching conversions on third-party domains you don't own (SavvyCal/Calendly/Stripe) via metadata passthrough + webhook identity-merge, fail-closed anonymity guards, and first-touch data-quality cleanup — distilled from real production builds (Conversion Factory + Truelist). Four references (attribution-models.md, measurement-paradigms.md, by-business-type.md with B2B/DTC playbooks, first-party-tracking.md) and 7 evals covering reconciliation, model choice, the third-party stitch, ROAS incrementality, the direct/branded blind spot, the analytics boundary, and the DTC measurement stack. Pillar B also folds in production feedback from Tessa Kriesel (credited): the CRM last mile (sync a source field with confidence + basis and a paid-vs-non-paid read onto the account → revops), storing the full ordered touch path so the build track can run Pillar A's multi-touch models on real data, the "expect ~zero until the cross-subdomain stitch is verified in prod" window with a narrowly-scoped campaign-window fallback + pre-stitch backfill, and account-level rollup (excluding free-mail domains) for B2B.
analytics (2.0.0 → 2.0.1): description now hands off attribution modeling and reconciliation to the new attribution skill, keeping analytics scoped to tracking setup, event taxonomy, and UTMs.

v2.9.1 — pricing (2.0.1 → 2.1.0): Pricing Page Teardown with an AI-agent-readiness axis

pricing (2.0.1 → 2.1.0): added a Pricing Page Teardown — a two-axis audit of a live pricing page. Axis 1 is the classic human buyer experience (value-prop clarity, plan differentiation, cognitive load, trust signals, psychology, transparency); Axis 2 is the novel AI-agent readiness lens: whether the LLMs/agents that increasingly shortlist and compare tools can actually read and quote your pricing — machine-readable prices (not locked in an image or behind "Contact us"), extractable FAQ/objection coverage, per-tier depth in text, and structured data. Includes the memorable "paste test" (give the URL to a browsing-capable AI and ask for the plans and prices — if it can't answer, an AI shopping for your buyer can't either), a full 10-dimension rubric + scoring/report template in references/pricing-page-teardown.md, and hand-offs to schema (Product/Offer JSON-LD) and ai-seo (extractability, AI search-bot access, llms.txt). New eval (id 7). AI-agent-readiness lens adapted from Kyle Poyar / Growth Unhinged (learn-from-only, credited).

v2.9.0 — influencer-marketing (new, 1.0.0): influencer, creator, and ambassador partnerships end to end

Added influencer-marketing skill — influencer, creator, and ambassador partnerships end to end. Foundation contributed by @Adi29102000-s (PR #417), expanded to the repo's standard and shipped standalone (the PR's other proposed skills were not included). Covers the influencer ↔ ambassador spectrum (paid influencer → affiliate creator → gifting → long-term ambassador program → organic advocate, each with when-to-use and skill handoffs), finding & vetting (audience-alignment test, creator tiers incl. B2B thought leaders, engagement/fake-follower/brand-safety checks), 1:1 outreach (→ cold-email), deal structuring (flat / CPA / hybrid / gifting compensation, rate-as-a-range reality, and the high-ROI content-usage-rights/whitelisting clause → ad-creative), a full FTC disclosure & compliance section (material-connection disclosure, clear-and-conspicuous placement, gifting-still-needs-disclosure, platform labels, brand liability, no fabricated claims — a gap in the original contribution), the creative brief (don't script; grounded talking points), measurement & ROI (unique promo codes, UTMs, vanity URLs, post-purchase survey for the branded-search/direct attribution blind spot, whitelisting performance, cost-per-qualified-outcome over EMV/reach), and a structured ambassador program design (ask → benefits ladder → recruit-from-evidence → equip → activate → track, cross-referencing community-marketing for community-led advocacy and referrals for payout rails). Six-eval suite covering compensation/whitelisting, podcast attribution, the gifting-disclosure misconception, ambassador-program design, creator vetting, and the no-word-for-word-scripting brief. New skill = repo y release. Total skills: 48.

v2.8.12 — product-marketing (2.0.0 → 2.1.0): the context document now carries its own version histor

• product-marketing (2.0.0 → 2.1.0): the context document now carries its own version history. .agents/product-marketing.md gets a Document version: header (v1, v2 …) and a ## Changelog section at the bottom — a newest-first, dated, one-line-per-revision paper trail of what changed and why. The update flow now reads the current version and recent changelog on open, and on save bumps the version, updates Last updated, and prepends a new changelog entry naming the sections touched and the reason (never rewriting past entries; skipped only for pure typo fixes). Since this doc is the shared context every other marketing skill reads, the changelog makes positioning/ICP changes traceable across a project — you can see how the positioning evolved and what a downstream skill was generating against. New eval (id 7) covers the version-bump + prepend-changelog behavior on a repositioning update.

v2.8.11 — video (2.0.1 → 2.1.0): added reverse-engineering a viral edit into a reusable edit spec (t

• video (2.0.1 → 2.1.0): added reverse-engineering a viral edit into a reusable edit spec (the tool-agnostic decomposition behind "copy any viral edit" — pattern from Arcads' skill, whose Omni generation model is proprietary/MCP-gated; we build only the decomposition, credited). New references/edit-anatomy.md: pull the reference with watch-video (visual/multimodal to read frames + caption style + cut timing) or social-fetch, extract the edit anatomy beat by beat across nine dimensions (shot/framing, cut rhythm/cuts-per-second, on-screen text, caption style, motion/punch-ins, b-roll/overlays, sound design, the first-2s hook, pacing curve), and output a per-beat beat sheet table plus a style summary of the 3–5 signature moves that make the edit recognizable (patterns over instance-logging). Includes the review-once gate (approve the beat sheet — on-screen text + scene-change placement — before executing in Remotion/Hyperframes, CapCut, or an AI restyle tool) and a hard originality guardrail (copy the editing grammar applied to your own footage/message, never the reference's footage, script, voiceover, or music). SKILL.md adds a Reverse-Engineer a Viral Edit workflow and 'copy this edit,' 'match this video style,' 'reverse-engineer this video,' 'edit like this reference' triggers. New eval (id 7). Closes #456.

v2.8.10 — ad-creative (2.7.0 → 2.8.0): added AirDrop as a fourth surface in the iOS-native reveal vi

• ad-creative (2.7.0 → 2.8.0): added AirDrop as a fourth surface in the iOS-native reveal video ad family (references/imessage-video-ads.md, alongside iMessage / ChatGPT / Apple Notes; format popularized by Shiv Sakhuja / Gooseworks' goose-video Airdrop Ads, re-expressed originally — their runtime is MIT but the recipe is private, so this is original craft). The one interaction-native format in the family: the hook is an incoming AirDrop request and the Accept tap is the reveal — a translucent AirDrop card (sender device name, preview thumbnail, red Decline / blue Accept) from the receiver's POV, where iOS's own accept/decline choice is a built-in micro-conversion beat. New ### AirDrop Reveal subsection: the preview thumbnail as the hook that must earn the tap, sender-name casting (relatable human name reads native; brand-as-sender reads like an ad), the transfer progress-ring as the signature motion, the AirDrop swoosh sound (not iMessage tritones) with the same CC0-Apple + trade-dress caveat, receiver-POV-default vs sender-POV-flex, and the family grounding/disclosure rules (a dramatization of a share, never a real endorsement). Updates the H1, intro, surface-selection table, and choosing-signal line to four surfaces. SKILL.md pointer names AirDrop and adds the 'AirDrop ad' trigger. New eval (id 11).

v2.8.9 — prospecting (1.0.0 → 1.1.0): added a fourth branch — Demand-signal discovery (find your fir

• prospecting (1.0.0 → 1.1.0): added a fourth branch — Demand-signal discovery (find your first customers), the early-stage motion that finds first customers / design partners / beta users from recent public pain-demand-timing signals rather than firmographic list-building (re-expressed from the open-source first-customer-finder Codex skill, Kappaemme/MIT, credited; extended with our live-recency tooling). New references/demand-signals.md: how the branch differs from list-building (starts from a described problem, sources public discourse, wins on 10 strong evidence-backed matches over coverage), a product brief gate, the five signal query buckets (explicit demand / pain / workaround / switching / timing) with our tooling edge called out (last30days for Reddit/HN/X/web recency, social-fetch to read original threads, scraping/Firecrawl/Browserbase over snippets, deep-research, competitor-profiling, customer-research), a public-only source mix, a demand-fit scoring rubric (pain 25% / product fit 25% / timing 20% / reachability 15% / evidence quality 15%, 0-100 with bands — distinct from the ICP-fit Hot/Warm/Cold), prospect stages (high intent / problem aware / trigger present / potential fit), a per-prospect evidence ledger, manual-only source-based openers (never auto-send), the evidence-report structure (verdict → ICP → top prospect → shortlist → repeated patterns → 7-day manual outreach plan → limits), and the honesty rules (a cited signal is the entry ticket; label 'potential customer based on public signals,' never 'will buy'). SKILL.md adds the branch to the Pick-the-Branch table + deep-dive pointers and 'find my first customers,' 'early adopters,' 'design partners,' 'beta users,' 'who has this problem' triggers. Also closes two compliance-guardrail gaps across all branches: no data brokers / leaked datasets, and never target or infer sensitive/protected traits (health, financial hardship, political belief, sexuality, religion). New eval (id 7) covers branch selection, signal mining, demand-fit scoring, and the never-auto-send rule. Closes #447.

v2.8.8 — ad-creative (2.6.0 → 2.7.0): added the creative review page — a shareable, self-contained H

• ad-creative (2.6.0 → 2.7.0): added the creative review page — a shareable, self-contained HTML artifact that presents generated concepts for a client or stakeholder to review and pick (the visual upgrade to INDEX.md; pattern reverse-engineered from a real agency creative-approval page, re-expressed originally). New assets/creative-review-template.html: one file, inline CSS+JS, no build or dependencies, driven entirely by a DATA object — renders concept tabs (each a strategic angle), a pixel-accurate in-feed Instagram/Facebook preview with a whitelist-handle toggle, a labeled frame-by-frame storyboard (tap to jump), selectable headline variations that overlay the preview, primary text, destination/CTA/offer, optional rollout mechanics, and a required grounding disclosure; frames render real images (URL / relative path / data URI) or styled label+prompt placeholders for concepts not yet rendered. New references/creative-review-page.md: when to produce one, the full data model, the frame-storyboard-as-carousel-arc link to carousel-frameworks.md, the hard grounding rule (every concept discloses what's real; illustrative proof is labeled illustrative — never launder fiction as fact), and how to populate/verify/deliver (open locally, host on any static host, or hand off the file). SKILL.md adds a Creative Review Page output-format section, a Mode 3 pointer, and 'creative review page' + 'present ad creative for approval' triggers. New eval (id 10) covers template use over markdown, narrative-job frame labels, and the grounding disclosure. Browser-verified: concept switching, platform/handle toggles, and frame navigation all render correctly.

v2.8.7 — ad-creative (2.5.0 → 2.6.0): added Mode 4 — the Creative Strategy Loop, the upstream workfl

• ad-creative (2.5.0 → 2.6.0): added Mode 4 — the Creative Strategy Loop, the upstream workflow layer for deciding which ads are worth making (built so the same capability ships in any agent running the library, incl. Magister; workflow shapes informed by public creative-strategist practice, expressed originally). New references/creative-roadmap.md: three-signal synthesis (account performance via the platform CLIs; customer/brand language via the Grounded Inputs corpus + customer-research; external organic via scraping, social listening, and competitor-profiling) on a monthly-deep-dive + weekly-refresh cadence with a receipts rule (no insight enters the icebox without its source); concepts as segment × motivation × angle × format hypotheses ranked on a six-tier evidence scale; account-state branching (exploration state: wide net-new mix, per-metric win redefinition, iterate only on hits, four root causes to check; scaling state: winner-variation-heavy mix with visually-distinct variations, sub-angle probes, a standing exploration allocation); the roadmap artifact (icebox → quarterly themes → monthly slate with T1 iteration / T2 remix / T3 production tiers and a hard capacity check); per-concept briefs handed to Modes 1–3; and the monthly creative retro (winners/losers/metric-wins/learnings/kills, judged at concept level, every learning landing as an icebox update, re-rank, or kill). New references/hook-system.md: hooks as three components (visual action / spoken line / caption) with the no-duplication rule; the segment → motivation → format → hook generation pipeline output as a hook matrix; an eight-move opening menu; the diagnostic funnel mapping thumbstop/hold/CTR/CVR to which component to fix; the on-ramp rule (every hook test is an on-ramp test); fidelity laddering tied to production tiers; and inherited grounding rules plus organic language mining. SKILL.md adds the Mode 4 section and 'creative strategy,' 'creative roadmap,' 'creative retro,' 'hook writing' triggers. New eval (id 9) covers exploration-state diagnosis, evidence-ranked slates, and the receipts rule.
• customer-research (2.0.0 → 2.0.1): added the zero-review persona fallback to Persona Generation — a four-step proxy ladder (own differentiator → direct competitors' reviews → marketplace comparables → adjacent brands sharing the audience) with provisional-persona tagging and replacement as first-party evidence arrives.
• marketing-loops (1.1.0 → 1.2.0): added the monthly-creative-retro loop to the Paid section of references/loop-catalog.md — closes ad-creative's Mode 4 loop on a schedule: pulls last month's performance, drafts the retro artifact and next month's capacity-checked slate, flags the account-state call for human confirmation, one-retro-per-month idempotency, and never launches or pauses ads. Closes #434.

v2.8.6 — ai-seo (2.1.0 → 2.2.0): added citations vs. recommendations — the correction to citation-ce

• ai-seo (2.1.0 → 2.2.0): added citations vs. recommendations — the correction to citation-centric AEO strategy. New references/citations-vs-recommendations.md (grounded in Lily Ray's (Amsive) 100-query B2B study, Scrunch and SimilarWeb behavioral studies, and Growth Plays commentary): the AI visibility ladder (retrieved → cited → mentioned → recommended, each governed by different criteria, plus the shadow rung of being recommended against on requirements-heavy prompts), the self-promotional listicle risk (69% of the AI Overview citations earned by self-promotional "best [category]" listicles — 224 of 323 — appeared in answers that excluded the publisher from the recommendations; for emerging brands a self-ranked guide can act as a vote for competitors), stage-dependent buyer's-guide strategy (leaders get cited and recommended; emerging brands get citation and category framing — still worth publishing, with rebalanced expectations), what earns recommendations (offsite consensus: reviews, analysts, communities, earned media, video — with the test "if a model ignored everything on our domain, would the rest of the web still shortlist us?"), and what a recommendation is worth (Scrunch, observational: ~2× behavior lift vs. a passing mention; for users with no recent observed brand engagement, +182% branded searches / +117% site visits / +185% product views within a week; SimilarWeb: ~2.5× more new visitors — with only ~9% visible as AI traffic, the attribution blind spot) plus the measurement triad (prompt tracking with mention framing, self-reported attribution, call recordings). SKILL.md adds a compact citation-≠-recommendation pointer, a Recommendation-rate row in Monitoring, and caveats on the Listicle Block (content-patterns.md) and educational-content goals (content-types.md).
• competitors (2.0.0 → 2.0.1): added an AI-answer expectations-by-stage caveat to the alternatives-page format — these pages often earn AI citations, but recommendation depends on offsite consensus; for emerging brands a self-ranked list can surface competitors in the answer. Cross-references ai-seo's citations-vs-recommendations reference. Closes #427.