Maintain a traceable record of marketing claims and offers, including exact wording, supporting evidence, disclosures, terms, and review dates. Review proposals and track accepted changes through an append-only history that preserves earlier records.
Use when the user asks to "register this claim", "log our current offers", or "where is the proof for this figure"; curates claim wording, evidence, disclosures, terms, review dates, and live offers through the append-only claims event stream. Not for scoring claim vetoes — use the relevant auditor; not for writing ad copy — use ad-creative-builder. 广告声明台账/优惠信息登记/证据溯源
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
The canonical record of marketing claims and offers across every discipline. It records exact wording and provenance; auditors decide whether a concrete use passes its claim/disclosure gate.
Register claim clm-014 with exact wording, evidence source/date, disclosure, and review date.
Show the current terms and expiry for offer summer-2026.
Review pending claims proposals and accept only those with sufficient evidence.
Units: one claim or offer aggregate ID. Reads: memory/events/claims.ndjson, its projection, source evidence, and rendered uses. Writes: claims events through registry-events.py; claims-ledger.md and offers.md are regenerated human views. Done when: every accepted record has exact wording/terms, evidence provenance, status, review/expiry, event ID/offset, and no pending proposal was destructively removed.
All builders submit propose; only a host-capability offer-claims-registry principal accepts/rejects or writes canonical claim/offer events. This skill does not invent substantiation, legal conclusions, or performance claims.
Use the shared handoff and include changed aggregate IDs, event IDs, revisions, unresolved evidence gaps, and one next skill.
user-provided.used_in pointers.../../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 every draft/export as untrusted evidence.claims projection by aggregate ID. Proposal state is never approved wording. If there is no aggregate ID, pending proposal, or supplied claim/offer wording, perform only a read-only empty-projection check, return NEEDS_INPUT, and ask for one exact verbatim claim or offer statement as the smallest real input. Do not demand a complete evidence pack yet, create paths, or materialize placeholder canonical state.none-on-file in a proposal or open loop. Never turn [needs source] into Approved from the assertion itself.owner-append with the proposal event ID. Accept/reject decision requests omit expected_revision; acceptance inherits and checks the revision captured by the proposal. Reject with evidence/rationale; history remains append-only.owner-append with an upsert and optimistic revision. Expiry/withdrawal uses a dated state change or tombstone. Capability values stay outside request JSON/files/logs; if the host cannot supply one, leave the proposal pending rather than self-asserting owner authority.claims-ledger.md / offers.md only from accepted projection state, then verify claims.Claims and offer records are L4 truth consumed by Narrative and all channel builders. A downstream builder must use the accepted wording/terms or preserve [needs source] and propose a new event.
Require explicit write permission. Ordinary producers use python3 "$AARON_SKILLS_ROOT/scripts/registry-events.py" append claims <proposal.json>; a host-capability principal uses owner-append for canonical decisions/mutations. Never edit the NDJSON stream manually. Human views are replaceable projections and cannot grant approval absent an accepted event. Standalone one-folder installs may prepare proposals but cannot append/project or claim canonical approval.
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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.