A library of Claude Skills and slash commands that turns a chat agent into a marketing operator. Seven disciplines and a shared protocol layer, at a glance:

/aaron-marketing:auto routes any natural-language goal across all of it. Skills and commands are plain Markdown; small Bash/Python-stdlib runtimes provide hooks, validation, scoring, registry events, private operational run events, connectors, and CI checks (no pip, no build step). Every skill runs at Tier 1 with data you provide; connectors only automate retrieval or an explicitly approved mutation.

The authoritative typed topology is references/system-catalog.json; see the generated system architecture for the readable four-layer map, all 120 paths, registry owners, auditor sinks, and distribution profiles.

The pre-merge standalone repos are now signpost repos pointing here — seo-geo-claude-skills (final 20-skill line preserved at tag v9.9.12) and influencer-marketing-agent-skills (final IMPACT line at tag standalone-final). Sibling-repo policy: docs/repo-family.md.

Contents

• Why this library
• Install
• First run
• Capability profiles
• Architecture
  • The shared skill contract
  • The system: a four-layer marketing operating system
  • Quality system: eight frameworks, eight gates
  • The protocol layer
  • Memory & automation hooks
• Skill catalog
  • Narrative — TALE (16)
  • SEO/GEO — SITE (16)
  • Social — ECHO (16)
  • Email — SEND (16)
  • Paid Ads — ROAS (16)
  • Influencer — STAR (16)
  • Launch — RAMP (16)
  • Protocol layer (8)
• Commands
• Connectors & enhancement tiers
• Recommended workflows
• Repository layout
• Design philosophy
• Quality guards (CI)
• Contributing & project docs
• Disclaimer
• License

Why this library

Install

Use it with Claude Code, any Agent Skills-compatible host, or a plain git clone:

In Claude Code, marketplace add only registers the catalog — run /plugin install aaron-marketing@aaron (or pick it from /plugin) to actually enable the skills and commands. To pull a single skill on a generic host: npx skills add aaron-he-zhu/aaron-marketing-skills -s keyword-research. Browse the bundle on the skills.sh registry. Per-agent directories, frontmatter quirks, and what degrades outside the plugin: docs/agent-compatibility.md (verified 120/120 installable, 2026-07).

Installing the plugin adds nothing to your /mcp list — the MCP catalogue lives in docs/mcp-catalog.json, deliberately outside the plugin-root .mcp.json path that Claude Code auto-registers, so it is a copy-paste reference only (see Connectors).

First run

If your host supports automatic skill routing, just describe the goal:

Research keywords for my SaaS product targeting small teams

Find TikTok creators for a skincare launch and score their fit

Audit this Google Ads account before I scale — exports attached

Or use the slash commands — /auto for routing, or a discipline entrypoint:

/aaron-marketing:auto turn our pricing page into an AI-citable comparison hub

/aaron-marketing:seo-geo https://example.com/blog/my-article --phase tune

/aaron-marketing:auto infers intent and runs the smallest useful workflow, stopping only at blocking decisions. Every skill works with pasted data; optional tools are documented in CONNECTORS.md.

Capability profiles

A fresh project runs Lite, including when you installed the full Governed-ceiling plugin. Choose a larger profile only when the job needs its mechanisms:

v19 validation status: this is an engineering-validated release. The exact source passes CI, reproducible package checks, and real model/provider execution over simulated semantic fixtures. Those runs do not constitute real-project outcome evidence; real-project outcomes remain unvalidated. Lite remains the fresh-project default. Governed is available as an explicit capability choice, but availability does not validate Governed outcomes or Governed-by-default. That promotion requires the post-release cohort of 14 pilots + 70 paired Lite/Governed projects + 28 shadow projects.

The installed archive is a hard ceiling; the effective project profile may be lower. Select the archive in the installer/admin surface, or select the logical profile through a one-invocation --profile, AARON_MARKETING_PROFILE, or the closed .aaron-marketing/profile.json project config. These controls do not add a slash command: the public grammar remains exactly /auto plus the seven discipline commands, and every profile preserves the seven 4×4 loops and their TALE/SITE/ECHO/SEND/ROAS/STAR/RAMP acronyms.

Profiles never disable consent, claims, PII/secret handling, external-mutation approval, audit-verdict integrity, or release provenance. Switching profiles also never deletes existing state. Standalone one-folder installs have a Lite physical ceiling and degrade fail-closed when a root runtime is absent. Configuration, archive selection, legacy-project migration, and LEGACY_RUN_BLOCKED recovery are documented in references/capability-profiles.md.

Architecture

The shared skill contract

Every skill follows the same activation contract — seven sections in a fixed order:

1. Trigger / when-to-use — when the skill should fire.
2. Quick Start — copy-paste prompts.
3. Skill Contract — Expected output · Reads · Writes · Promotes · Done-when · Primary next skill.
4. Handoff Summary — the standard hand-off shape so the next skill picks up cleanly.
5. Data Sources — ~~category placeholders, each with a keyless Tier-1 path.
6. Instructions — the numbered method (treats all exports as untrusted input).
7. Next Best Skill — where to go next (with visited-set + max-depth termination rules).

Every skill also self-declares metadata.discipline (narrative / seo-geo / influencer / ad / email / launch / social / protocol, where ad is Paid Ads/ROAS) and metadata.phase, so routing and clustering work uniformly. The contract is documented once in skill-contract.md; the shared cross-skill state lives in state-model.md.

The system: a four-layer marketing operating system

One brand voice, expressed through five always-on channels, concentrated into launch moments, all reading and writing a shared system of record. Seven disciplines, four altitudes — a system, not a pile.

Narrative is the message; the channels are the mediums that express it. Each core builder records the exact canon ID/version and claims-projection offset it used, or an explicit approved fallback/block. Each discipline's 4-phase loop lives inside its layer (Narrative = Trace → Architect → Land → Evaluate).

All seven use phase directories in the catalog order: Narrative → SEO/GEO → Social → Email → Paid → Influencer → Launch. Note "activate" means creator outreach for influencer but account-gating for paid ads — same word, discipline-specific scope.

Quality system: eight frameworks, eight gates

Eight benchmarks make "good" measurable. Each defines dimensions, a rollup method, and a small set of veto items (hard fails that cap or block a score regardless of the rest):

Each framework is enforced by an auditor-class gate — a skill that emits a typed artifact (class: auditor-output) checked by the deterministic validator and bounded lifecycle hooks. Repository CI regression-tests the validator and contract; it does not inspect ignored host-runtime artifacts. Gates are workflow steps, so each lives in its discipline and is counted there:

Shared veto policy: one verified veto caps the final score at min(raw, 59); two or more verified vetoes produce status: DONE + verdict: BLOCK and no final score. Missing evidence is Unknown, resulting in NEEDS_INPUT/UNDECIDED, never an automatic failure. Gate mechanics live in auditor-runbook.md and are executed by the typed scorer.

The protocol layer

The protocol/ directory holds the shared truth & memory machinery that sits outside the discipline phase-flows — 8 skills, counted separately:

The registries follow a sole-writer rule (other skills submit via registry-events.py proposal events), and they curate — the gates judge. The genuinely horizontal layer beneath everything is the references/ protocols (auditor-runbook, state-model, skill-contract, context-resolution, runtime-protocol, audit-loop, humanizer-slop, measurement-protocol) — shared by design as documents, not skills.

Memory & automation hooks

Memory is temperature-tiered, so context survives across skills and sessions without bloating the prompt:

Opt-in run evidence lives separately under memory/runs/<run-id>/: append-only metadata events, a derived session tree, turn snapshots, save points, and run envelopes. It is Git-ignored, retention-bounded, and explicitly non-authoritative — it cannot accept a registry proposal or authorize an external action. The stdlib run-events.py runtime and Runtime Protocol enforce this boundary.

Before a turn snapshot, the stdlib context-resolver.py can turn an explicit candidate request into a hash-bound context manifest. It deterministically applies required/optional/forbidden policy, authority and freshness order, conflict/supersession rules, sensitivity and byte budgets, content dedupe, and typed omission reasons. It does not perform semantic retrieval or copy source content into the manifest. /auto likewise reads a generated index plus at most three discipline/cross-discipline routing shards, while one maintenance source preserves all 88 cases.

Manifest capacity is not treated as model-visible context. context-assembly.py projects a verified manifest into separate controller, model, tool, and deferred resources using the typed context-modules.json catalog. The safe explicit profile remains the default. Evidence-gated balanced keeps the full selected Skill but substitutes the compact policy kernel for repeated shared policy; lean additionally substitutes a generated Skill capsule. Both reduced profiles may run uncertified only as explicitly non-deployable ablations. Deployment requires a Governed binding backed by canonical real cases, stored protocol-v3 provenance for every arm, immutable SUT and independent judge revisions, and current hash-bound paired evidence. The repository currently has 700 simulated cases, the bundled adapter reports no immutable SUT or judge revision, and certified_bindings is empty, so explicit remains the only deployable default. See Context Engineering Architecture for measurements, host profiles, disclosure rules, and certification boundaries.

The optional stdlib audit-loop.py adds a bounded proposal-only outer loop around validated FIX audits. It records immutable proposal, owner-review, non-empty intervention-evidence, and re-audit steps under the same ignored run namespace, with leases, idempotency, deadlines, deterministic retry timing, and 1–3 improvement cycles. Each v2 transition reserves its selected-ancestry event anchor before materializing the exact step; a missing step is recoverable only by replaying the same request, while the public loop-step command is verification-only. The final event slot stays reserved for terminal sealing. Runtime-derived loop_closure isolates sibling branches: success requires exact terminal loops; waiting/needs-input/blocked may retain exactly covered active loops; failed/aborted may preserve bounded unresolved evidence without claiming convergence. The loop never edits the audited target, performs an intervention, accepts registry truth, or authorizes an external mutation; only a distinct medium/high-confidence SHIP re-audit that passes the observation-time provenance floor can converge. audit-trends.py deduplicates exact artifact bytes, links all exact-hash edges through the verified loop graph (including non-adjacent series entries), enforces aggregate scan budgets, escapes terminal controls in human output, and reports relapse, oscillation, context/catalog drift, evidence growth, and confidence changes without guessing causality from display order.

Semantic conformance is also layered. The strict corpus projects 572 authored cases, 88 generated routing cases, and 40 generated auditor prompt-contract variants into one 700-case runner model. A fixed 24-case smoke profile, change-aware impact selection, and full nightly profile are credential-free to plan in CI. Protocol v2 remains the current-source real-provider smoke and engineering-maturity evidence path. Protocol v3 adds blind selection from the complete 120-skill index, loads selected-skill context only after routing, and uses an independent judge for explicit-versus-balanced/lean paired evaluation. A real adapter run over a simulated case remains simulated case evidence. Deterministic UTF-8 bytes are always available and provider usage is nullable. Complete provider usage alone does not prove token savings: protocol v3 keeps token-savings permission false until paired input-token reduction and uncertainty gates exist; cost-savings claims are unsupported. The eight generated auditor prompt contracts bind their skill, catalogs, framework rules, and runtime sources without becoming a second topology inventory; see the semantic evaluation docs for the source-only maintenance workflow.

Hooks (hooks/hooks.json, runner hooks/claude-hook.sh) wire seven Claude Code events:

The Artifact Gate is framework-agnostic — the same hook validates TALE, CORE-EEAT, CITE, STAR, ROAS, SEND, RAMP, and ECHO artifacts with no per-framework code.

Skill catalog

Skill links open each SKILL.md. Expand the Details under each discipline for a one-line purpose per skill. Catalog order follows the four-layer strata — Narrative (L1 · Strategy) first, the five always-on channels next, Launch (L3 · Orchestration), then the Protocol layer.

Narrative — TALE (16) · 📖 Discipline guide

Four phase directories under narrative/ (4 skills each) follow the TALE loop (Trace → Architect → Land → Evaluate); the gate (⛩ narrative-quality-auditor) sits in Evaluate. It runs truth, system, and effectiveness profiles separately; a full review links three results and never averages them. Narrative is the L1 · Strategy layer: one brand voice the five always-on channels inherit. It absorbs positioning — positioning-mapper stays physically in launch/ but reads logically as the front of TALE Trace.

SEO/GEO — SITE (16) · 📖 Discipline guide

Four phase directories (4 skills each) plus the discipline's two quality gates (marked ⛩).

Social — ECHO (16) · 📖 Discipline guide

Four phase directories under social/ (4 skills each) follow the ECHO loop; the gate (⛩ social-quality-auditor) sits in Host. Each run selects the asset gate or one program-maturity profile; those constructs are never combined. The discipline ships no posting, engagement, or DM automation of any kind.

Email — SEND (16) · 📖 Discipline guide

Four phase directories under email/ (4 skills each) follow the SEND loop; the gate (⛩ email-quality-auditor) sits in Deliver. Only the gate computes EQS from the declared promotional, retention, cold-outbound, or newsletter profile — every other skill works one lever and hands off.

Paid Ads — ROAS (16) · 📖 Discipline guide

Four phase directories under ad/ (4 skills each) follow the ROAS loop; the gate (⛩ ad-account-auditor) sits in Activate. Only the gate computes the profile-weighted RQS — every other skill works one lever and hands off.

Influencer — STAR (16) · 📖 Discipline guide

Four phase directories (4 skills each); the discipline's gate (⛩ creator-content-auditor) sits in Activate.

Launch — RAMP (16) · 📖 Discipline guide

Four phase directories under launch/ (4 skills each) follow the RAMP loop; the gate (⛩ launch-readiness-auditor) sits in Mobilize. Each run selects one preflight, execution, or outcome profile; scores from different lifecycle reads are linked but never averaged.

Protocol layer (8)

The shared truth & memory machinery — see Architecture § The protocol layer for roles and sole-writer rules.

Commands

Eight commands: /aaron-marketing:auto routes any goal across all seven disciplines, and each discipline has exactly one explicit entrypoint. Source: commands/.

Daily work normally starts with /aaron-marketing:auto; the other seven are explicit discipline entrypoints, with --phase to narrow the stage.

Connectors & enhancement tiers

Skills name tools with ~~category placeholders (~~SEO tool, ~~web analytics, ~~ad platform, ~~email platform, …) instead of specific vendors, and every category has a keyless Tier-1 path. Full recipes — including the free/first-party endpoint for each category — are in CONNECTORS.md.

The connector layer is a product in itself

100+ documented integration paths across three engineered layers — and every one of them earns its place:

What makes them trustworthy rather than just numerous:

• Three safety classes, engineered gates (SECURITY.md): hosted fetchers run a local robots.txt pre-flight before every delegated fetch and refuse on Disallow; anything that mutates external state (email sends, index pushes) is dry-run by default behind an explicit --live flag, with idempotency keys where the vendor supports them and no auto-retry where it doesn't.
• Verified, then re-verified: endpoints are checked against primary vendor docs with dates, keyless paths are live-tested, a CI guard enforces version/tracking sync, and a pre-release live smoke catches endpoint drift (it has already caught real API changes — twice).
• Facts, not verdicts: connectors report record presence, parsed tags, and raw series; the auditor gates do the judging, and skills label every number Measured / User-provided / Estimated.
• A written playbook (docs/connector-playbook.md) governs every addition — qualify, verify, implement, test, wire, document, track, regress, record — so quality holds as the catalog grows.

• Bundled zero-dependency helpers under scripts/connectors/ (Python stdlib only) pull public/own data locally — e.g. PageSpeed/CrUX, Open PageRank, page crawl, Wayback CDX, Wikidata SPARQL, Common Crawl, advertools recipes — plus resend.py, direct Resend ESP automation for the email skills (free-tier key: domain-auth status, seed-test sends, suppression sync, broadcast scheduling; mutating subcommands dry-run by default and require --live), and firecrawl.py + tavily.py, keyless hosted-fetcher automation for the research skills (Firecrawl: live web SERP + JS-rendered page markdown + site maps; Tavily: scored search + an AI answer engine's cited-sources probe for GEO + URL extract — both free with no key at all, both with a local robots.txt pre-flight built in).
• Free/keyless sources documented per category: Google Search Console & GA4 (own data), PageSpeed/CrUX, Wikidata, Common Crawl, Open PageRank, Firecrawl keyless SERP/scrape, Tavily keyless AI-search, DNS-over-HTTPS email-auth records (doh.py), Wikipedia attention series (pageviews.py), GDELT news mentions (gdelt.py), YouTube creator metrics on a free key (youtube.py), IndexNow + Baidu index push (indexpush.py, dry-run gated), the ad-transparency libraries (Meta/Google/TikTok), and recipe rows for crt.sh, the W3C validator, oEmbed, and HN Algolia.
• Opt-in MCP servers (Ahrefs, Semrush, SE Ranking, SISTRIX, SimilarWeb, the self-hosted free OpenSEO suite, Cloudflare, Vercel, HubSpot, Amplitude, Notion, Webflow, Sanity, Contentful, Slack, Resend, the keyless Firecrawl and Tavily) are catalogued in docs/mcp-catalog.json as a copy-paste reference only — the catalog sits outside the auto-registered plugin-root .mcp.json path, so nothing is registered for you. Copy the entries you want into your own MCP config.

Paid-ads skills score from your own-account manual export (native ad-manager CSV, GA4, ecommerce). Keyed ad-platform APIs (Google Ads SDK, Meta Marketing API) are opt-in Tier-2/3 only and never a Tier-1 requirement. Email skills score the same way — from your own ESP export — and every deliverability signal is keyless (DNS lookups, a DMARC RUA report, and a seed-list inbox test), so a keyed ESP API is never a Tier-1 requirement either; when Resend is your ESP, the bundled resend.py automates the same loop on the free tier.

Recommended workflows

Most real goals span disciplines. /aaron-marketing:auto routes a natural-language goal to the smallest useful chain across all seven — a product launch, for instance, pulls Launch, Email, Social, and Paid at once:

/aaron-marketing:auto launch our v2 on Product Hunt in 3 weeks — 1,200 on the waitlist; we need the page, the emails, and a launch-day plan

Or drive one discipline's loop end to end — each discipline's 📖 Discipline guide (linked from the skill catalog) adds scenario-level plays:

Narrative (TALE loop)

1. Trace — narrative-baseline-mapper → category-narrative-mapper → audience-belief-mapper → positioning-truth-tracer
2. Architect — strategic-narrative-designer → message-system-architect → brand-language-codifier → story-bank-builder
3. Land — narrative-cascade-planner → pitch-narrative-builder → narrative-enablement-kit → proof-point-packager
4. Evaluate — narrative-quality-auditor (⛩ TALE gate) → message-test-designer → narrative-resonance-monitor → narrative-drift-monitor

SEO/GEO (SITE loop)

1. Survey — keyword-research → competitor-analysis → content-gap-analysis
2. Implement — content-writer → geo-content-optimizer → serp-markup-builder / page-play-builder
3. Tune — content-quality-auditor (⛩ publish gate) → on-page-seo-checker → technical-seo-checker → site-structure-optimizer
4. Evaluate — rank-tracker → performance-monitor → offsite-signal-analyzer; domain-authority-auditor (⛩) for the trust review

Social (ECHO loop)

1. Explore — channel-portfolio-planner → voice-dossier-builder → platform-norm-profiler → participation-warmup-planner
2. Craft — social-calendar-builder → social-creative-builder → short-video-scripter → advocacy-program-designer
3. Host — social-quality-auditor (⛩ ECHO gate) → engagement-inbox-manager → social-selling-planner → crisis-response-planner
4. Observe — social-pulse-monitor → share-of-voice-tracker → dark-social-attributor → social-measurement-loop

Email (SEND loop)

1. Setup — deliverability-qa → list-segment-builder
2. Engage — email-creative-builder
3. Nurture — email-sequence-designer → newsletter-monetization-planner
4. Deliver — send-experiment-designer → email-quality-auditor (⛩ EQS gate) before send

Paid Ads (ROAS loop)

1. Research — audience-segment-builder → campaign-architect
2. Orchestrate — ad-creative-builder → ad-test-designer (+ landing-optimizer for the page)
3. Activate — conversion-signal-qa → ad-account-auditor (⛩ RQS gate) before any budget goes live
4. Scale — paid-measurement-loop → attribution-reconciler → roi-calculator → report-generator

Influencer (STAR loop)

1. Scout — audience-mapper → trend-spotter → influencer-discovery → fit-scorer (STAR Suitability)
2. Target — competitor-tracker → campaign-planner → brief-generator → budget-optimizer
3. Activate — outreach-manager → creator-content-auditor (⛩ STAR gate) → contract-helper → content-amplifier
4. Report — landing-optimizer → performance-analyzer → roi-calculator → report-generator

Launch (RAMP loop)

1. Research — positioning-mapper → launch-tier-planner → launch-window-planner → early-access-designer
2. Assemble — message-house-builder → launch-asset-packager → pricing-packaging-planner → sales-enablement-kit
3. Mobilize — launch-readiness-auditor (⛩ RAMP gate) → launch-day-conductor → community-launch-runner → press-media-relations
4. Prove — launch-monitor → launch-feedback-synthesizer → launch-retro-analyzer → momentum-planner

For a full trust review, pair content-quality-auditor with domain-authority-auditor for a combined 120-item assessment. With memory-management active, handoffs and open loops persist in HOT/WARM/COLD memory automatically.

Repository layout

narrative/{trace,architect,land,evaluate}/                  # Narrative — TALE (16, incl. its gate)
seo-geo/{survey,implement,tune,evaluate}/                  # SEO/GEO (16, incl. its 2 gates)
influencer/{scout,target,activate,report}/                   # Influencer (16, incl. its gate)
ad/research|orchestrate|activate|scale/            # Paid Ads — ROAS (16, incl. its gate)
email/setup|engage|nurture|deliver/                  # Email — SEND (16, incl. its gate)
launch/research|assemble|mobilize|prove/             # Launch — RAMP (16, incl. its gate)
social/explore|craft|host|observe/                   # Social — ECHO (16, incl. its gate)
protocol/                                            # Protocol layer (8) — truth registries + memory
commands/        # 8 slash commands (auto, narrative, seo-geo, influencer, ad, email, launch, social)
references/      # shared contract, state model, the 8 benchmarks, auditor runbook, platform packs
evals/           # per-skill structural eval cases + structure-manifest.json
hooks/           # Claude lifecycle wiring + bounded privacy/artifact/run-context runner
scripts/         # stdlib deterministic runtimes, validators/generators, connectors, and CI guards
memory/          # HOT/WARM/COLD scaffolding + registry stores + ignored non-authoritative run evidence
docs/            # 9 localized READMEs + contributor docs (connector playbook, agent compatibility, …)
.claude-plugin/  # plugin.json + marketplace.json mirror

This source repository contains both runtime and maintenance assets. User distributions are allowlisted by references/distribution-files.json and built with python3 scripts/build-distribution.py --output <dir> --plugin --profile lite|pro|governed; tests, evals, CI, generators, and contributor-only documentation are intentionally excluded. A standalone skill build uses --skill <catalog-path>, contains only that skill directory, and declares a Lite ceiling. Bare --plugin remains a deprecated Governed-ceiling alias through v20; release automation selects the profile explicitly. Every output carries a per-file SHA-256 distribution-manifest.json; the builder rejects symlinks, special files, and multiply linked files, and --verify-manifest <dir> revalidates the complete payload. Every live release publisher/projector enforces clean, freshly refreshed pushed provenance; per-skill and built-package registry payloads come from a private export of that commit and bind its repository/commit identity into the verified manifest.

Design philosophy

• Skills are content. Core plugin/distribution and repository-maintenance code stays in small Bash/Python-stdlib validators, deterministic runtimes, hooks, connectors, and maintenance guards. No third-party / pip dependencies are permitted on those shipped or guard-scanned Python surfaces — enforced by a dependency-creep guard. Isolated probes under evals/ are excluded from distributions and may carry a locked, path-scoped development toolchain.
• Keyless first. Every ~~category has a free/own-data recipe; MCP and paid tools are pure convenience.
• Surgical & MECE. Each skill owns one job with a crisp scope boundary; overlapping work ships as a mode of an existing skill rather than a new thin skill. Registries curate, gates judge, analyzers feed gates.
• No invented numbers. Skills label every figure Measured / User-provided / Estimated and ship an AI-slop / banned-phrase detector.
• Compliance is guidance, not law. FTC-disclosure and claim-integrity checks flag risk; they are not legal advice.

Quality guards (CI)

Every change runs against a set of fail-closed guards (all in scripts/ and tests/):

Live endpoint drift is sampled separately by the manual scripts/connectors/smoke-live.sh — one minimal real call per hosted connector listed in that script, with shape assertions (rate-limit answers count as SKIP); run it before a release, never in CI.

Contributing & project docs

• CONTRIBUTING.md — authoring rules, the contribution checklist, and the authoritative 10-surface tracking list.

• VERSIONS.md — per-skill versions + changelog (current bundle: 19.1.0).

• SECURITY.md · PRIVACY.md · CODE_OF_CONDUCT.md — security, privacy, and community policy.
• CLAUDE.md / AGENTS.md — agent-facing context for this repo.

Disclaimer

These skills assist brand-narrative, SEO/GEO, influencer-marketing, paid-ads, email-marketing, product-launch, and organic-social workflows but do not guarantee rankings, AI citations, traffic, engagement, conversions, ROAS, deliverability, or business outcomes. Influencer-, ad-, email-, and social-compliance checks (FTC disclosure, claim integrity, platform policy, consent/opt-in, material-connection disclosure) are guidance, not legal advice. Verify recommendations with qualified professionals before relying on them for major strategy, financial, or legal decisions.

License

Apache License 2.0 — see LICENSE.

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