Fit Scorer
Use when the user asks to "score this influencer", "rank these creators for our campaign", or "tell me which influencer is the best fit"; produces the typed STAR Suitability (S) read plus a separately labeled campaign-fit ranking without mixing campaign-specific commercial fit into the Suitability read. Not for finding new influencers — use influencer-discovery; not for sending outreach — use outreach-manager. 达人适配度评分/创作者筛选排名
- Skill ID
- aaron-he-zhu/aaron-marketing-skills/fit-scorer
- Publisher
- aaron-he-zhu
- Repository
- aaron-marketing-skills
- Installs
- 0
- Files
- 2
- License
- Apache-2.0
- Requires
- Claude Code and compatible agent-skill hosts
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
aaron-he-zhu/aaron-marketing-skills/fit-scorerInstalls these files- SKILL.md
- references/scoring-templates.md
What this skill tells the agent
Fit Scorer
Score each shortlisted creator on the typed STAR Suitability (S) dimension, then keep deal-specific commercial fit in a separate prioritization matrix. Suitability includes the STAR-S8 brand/category and audience-brand evidence that is independent of any single deal; deal terms, availability, and campaign orchestration stay outside it. The commercial matrix is not a Suitability score and never enters the SQS.
Quick Start
Score one influencer:
Score @[handle] for [brand/campaign] and tell me if they're a good fitCompare and rank a shortlist:
Compare and rank these influencers for [campaign]: @influencer1, @influencer2, @influencer3Skill Contract
- Reads: brand/campaign context, target audience definition, campaign goal, and shortlist entries carrying a stable opaque
creator_refplus either transient handles/profile URLs or resolvable opaque handle refs (supplied by the user or carried over frominfluencer-discovery). Optional prior audience profiles frommemory/influencer/audience-mapper/, competitor partner benchmarks frommemory/influencer/competitor-tracker/, and a WARM Campaign Retro Card'sevidence_refsplusnext_campaign_hypothesiswhen the user supplies or authorizes that handoff. For rostered creators, read partnership history and audience-stat provenance frommemory/creators/<aggregate-id>.md— the creator-registry roster record — as Partnership Potential inputs. - Writes: return the typed Suitability (S) read and separately labeled commercial-fit comparison inline by default; when a Retro Card is supplied, preserve its hypothesis as a separately labeled next-cycle test constraint with no score or verdict effect. Save the report to
memory/influencer/fit-scorer/YYYY-MM-DD-<topic>.mdonly with exact WARM-save authorization. Saved reports and handoffs retain the stable opaquecreator_refand opaque evidence refs, never a raw handle, name, profile URL, email, provider ID, or deterministic hash increator_ref. - Promotes: only with separate exact authorization, promote evidence-backed top picks and their exact Suitability (S) read and catalog version to
memory/hot-cache.md; never promote an unscored/provisional result or the Retro Card's qualitative decision/hypothesis as scored truth. - Done when:
- Every creator has all 10 Suitability items
S1–S10explicitly Pass/Partial/Fail/Unknown/N/A with dated evidence or a gap reason. - Every creator's stable opaque
creator_refis preserved from discovery/registry or generated once for this lineage; raw identity locators remain transient. - The typed goal/context and the Suitability item states are preserved for the gate; Unknown prevents a Suitability read.
- Any commercial-fit ranking is visibly separate from the Suitability read and cannot override a veto or missing evidence.
- If a Retro Card is supplied, its
next_campaign_hypothesisis visible only as a falsifiable test constraint/commercial-matrix context; itsevidence_refsare pointers for fresh investigation, not STAR item evidence or an automatic selection rule. - Primary next skill: campaign-planner — turn the ranked shortlist into an approved campaign plan. If that plan is already approved and outreach-ready, hand off to outreach-manager instead; competitor benchmarking is optional.
Handoff Summary
Emit the standard shape from skill-contract.md §Handoff Summary Format.
Data Sources
This family needs no live integrations (Tier 1). Fit Scorer works end to end by asking the user for the inputs it scores — transient handles or profile locators, audience targets, brand values, and any metrics they have. A connector sharpens the numbers but none is required.
~~influencer database— follower counts, audience demographics, and partnership history.~~social platform analytics— engagement rate, comment quality samples, posting cadence, growth trend.~~audience intelligence— real-vs-bot follower estimates and audience overlap with your target.- Roster record (keyless Tier 1) — prior contact, response reputation, and delivery history come from
memory/creators/<aggregate-id>.mdwhen the creator is rostered (creator-registry curates it);~~CRMis an optional Tier-2 sharpener for the same history when no roster record exists.
Measured YouTube inputs (free key): for YouTube candidates, python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/youtube.py" videos @handle --limit 10 supplies the engagement-authenticity inputs directly — per-video views/likes/comments against the displayed subscriber base (views-to-subs consistency, comment rate, cadence) — so those sub-scores come from Measured numbers instead of screenshots. Free YOUTUBE_API_KEY; shortlist vetting only (ToS refuses bulk-harvesting quota). See scripts/connectors/README.md.
With zero integrations, ask the user to supply each value the scoring tables request; the framework and weighting still produce a defensible ranking. See CONNECTORS.md for the free/keyless recipe per category.
Instructions
The contract-compatible copied layouts live in references/scoring-templates.md: use the creator_ref-only typed STAR-S1–STAR-S10 evidence table for the Suitability read, then the optional commercial_fit_score tables for separate decision support. Never copy a raw locator into those outputs.
- Lock identity and typed context. Reuse the opaque
creator_refexplicitly carried by discovery, or a creator-registry aggregate ID only when its handle link is verified. If the user supplies only a raw handle/profile URL and no verified aggregate exists, generate one randomcreator-<UUIDv4>and reuse it unchanged across this report, any authorized save, and downstream handoffs. Never setcreator_refto a raw handle, name, URL, email, provider ID, or deterministic hash of one; keep those locators transient for evidence acquisition. Resolve an opaque ref only through its accompanying authorized artifact or verified registry link. If neither is available, request the transient locator and preserve the identity as unresolved rather than guessing or merging. Then require the creatortargetand target version, named STAR profile/goal (awareness|engagement|conversion|brand-building),assessment_time: forecast|actual, shared campaignrollup_id, observation date, platform/tier/niche cohort, evidence window, material context object, and current STARcatalog_version— the exact typed identity the gate will reuse. If any field is absent, do not invent it: returnNEEDS_INPUT, name the missing fields, and preserve the supplied identity unchanged for resume. When a Campaign Retro Card is supplied, record itsevidence_refsandnext_campaign_hypothesisin a separate non-scoring prior-cycle context block; neither becomes part of the STAR typed identity. - Freeze evidence for the current window. Use current creator analytics, public observations, roster history, and cohort benchmarks with source/date/type/confidence. A Retro Card and its
evidence_refsare discovery pointers only, never STAR item evidence. If a referenced primary source is independently reacquired and qualifies in the current evidence window, cite that fresh observation rather than the card. Missing or refused private access is Unknown, never Fail or Partial.
More from aaron-he-zhu
Use this skill in a project
Open any RiverX project, search the Skills panel for aaron-he-zhu/aaron-marketing-skills/fit-scorer, and install it in one click.
