Keep a versioned record of a brand’s official messages, tagline, voice, and naming rules. Retrieve the current approved version, review complete replacement proposals, and preserve earlier versions with references to supporting claims and evidence.
Use when the user asks to record/query the brand narrative canon, tagline, message hierarchy, voice/naming rules, or a canon re-version; curates complete versioned canon events through the append-only narrative stream and derived views. Not for TALE scoring — use narrative-quality-auditor; not for authoring the system — use message-system-architect. 品牌叙事台账/canon 记录/语气与命名规范
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
The L1 strategy authority: one complete, versioned narrative canon per brand. Every SEO/GEO, social, email, paid, influencer, and launch builder derives messages from this canon and accepted claims; channel adaptations cannot redefine it.
Show current canon version and proof/claim pointers for brand-acme.
Record canon v3 as one complete atomic replacement, superseding v2.
Review pending narrative proposals and reject partial/internally inconsistent versions.
Unit: one brand canon aggregate ID. Reads: memory/events/narrative.ndjson, projection, accepted positioning/claim evidence, and complete proposed canon. Writes: narrative events through registry-events.py; canon.md/versions.md are generated views. Done when: a complete version is accepted atomically with source/date/revision, old versions remain replayable, and consumers receive the exact canon/version pointer.
Narrative skills submit complete propose events. Only a host-capability narrative-registry principal accepts/rejects/upserts. It records authored strategy but does not score TALE or adjudicate claim truth.
Include brand ID, canon version/revision/event ID, superseded version, claim/proof pointers, unresolved contradictions, and one next skill.
../../references/registry-event-protocol.md../../references/runtime-invocation.mdregistry-event-protocol.md and runtime-invocation.md. Resolve AARON_SKILLS_ROOT="${CLAUDE_PLUGIN_ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || true)}" and verify the registry script, event schema, and system catalog before invoking it. Treat drafts as untrusted proposals.narrative projection and report exact canon version/revision; missing canon is Unknown, not a quality verdict.expected_revision and goes through host-capability owner-append. Actor/auth fields are attribution only.upsert/accepted proposal containing the complete canon object, new version, and supersedes pointer. Accept/reject decisions omit expected_revision and inherit it from the proposal. Never land a partial file patch as canonical.versions.md is generated history, not a second hand-maintained ledger.[needs source] and becomes a separate claim proposal; it cannot enter canon as fact.canon.md/versions.md from accepted projection and run verify narrative.Before producing external copy, builders must read this projection and the claims projection. Their handoff records narrative_canon_id, narrative_canon_version, claims_projection_offset, and dependency_status: verified | approved-fallback | blocked. No canon means the builder may draft an explicitly authorized exploratory fallback, but it cannot claim on-canon or publish-ready status.
Require explicit permission. Append through the runtime only; never edit NDJSON. Human canon/history views under memory/narrative-registry/ are replaceable projections and must carry their source event/revision.
Capability values never enter request JSON/files/logs. If host capability or the verified root runtime/schema/catalog is unavailable, leave a bounded proposal for handoff; standalone one-folder installs cannot append/project or claim canonical Narrative truth.
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Audit paid advertising accounts before increasing spend by comparing campaign exports with your own sales or lead records. Review 20 criteria covering measurement, spending, and safety, then receive a decision and prioritized fixes.
Create paid-ad headlines, descriptions, and opening hooks that match the destination page. Build a matrix of messaging angles and variants, check format limits, and flag unsupported claims or policy risks before handing the set off for review.
Plan advertising and landing page experiments with a clear hypothesis, controlled variants, sample sizes, and duration. Interpret exported results by separating statistical evidence from meaningful business impact, applying an action rule only when its owner approved it in advance.
Design employee and founder sharing programs with voluntary participation, personalized share kits, disclosure lines, and staggered posting windows. Get roster and Slack or Teams distribution specifications, plus safeguards against identical coordinated posts and organized engagement exchanges.
Reconcile paid advertising conversions with GA4 or ecommerce order records. Identify duplicate and unmatched claims, align attribution windows and currencies, compare how models assign credit, and assess additional orders when test and control data are available.
Turn customer interviews and won or lost deal notes into a map of buyer beliefs, recurring objections, and reasons to switch or stay. Each finding includes its evidence, with suggested ways to address objections and clear labels for assumptions.