Install
openclaw skills install @aaron-he-zhu/fit-scorerUse when the user asks to "score this influencer", "rank these creators for our campaign", or "tell me which influencer is the best fit"; produces weighted fit scores across audience match, content quality, brand alignment, engagement authenticity, and partnership potential, plus a ranked comparison and a go/pass verdict. Not for finding new influencers — use influencer-discovery; not for sending outreach — use outreach-manager.
openclaw skills install @aaron-he-zhu/fit-scorerObjectively evaluate how well an influencer matches your brand by scoring them across five weighted dimensions, turning gut feel into a defensible go/pass decision.
Score one influencer:
Score @[handle] for [brand/campaign] and tell me if they're a good fit
Compare and rank a shortlist:
Compare and rank these influencers for [campaign]: @influencer1, @influencer2, @influencer3
influencer-discovery). Optional prior audience profiles from memory/influencer/audience-mapper/ and competitor partner benchmarks from memory/influencer/competitor-tracker/. For rostered creators, read partnership history and audience-stat provenance from memory/creators/<handle-slug>.md — the creator-registry roster record — as Partnership Potential inputs.memory/influencer/fit-scorer/YYYY-MM-DD-<topic>.md.memory/hot-cache.md so downstream skills pick the right targets.Emit the standard shape from skill-contract.md §Handoff Summary Format.
This family needs no live integrations (Tier 1). Fit Scorer works end to end by asking the user for the inputs it scores — handles, 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.memory/creators/<handle-slug>.md when the creator is rostered (creator-registry curates it); ~~CRM is 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.
All fill-in tables and the comparison/report layouts live in references/scoring-templates.md — copy the matching block for each step.
Define the scoring framework. Set the five dimensions, weights (default below; tune per goal via the custom-weighting matrix), and the 1-5 scale. Use the Step 1 template.
C³ ACE alignment & veto gate. This skill is the C³ Creator scorer (ACE). Map dimensions onto ACE: Audience Match → Audience; Engagement Quality → Engagement; the Brand Safety sub-check → Credibility (C1). Note: the value/aesthetic/messaging-fit part of Brand Alignment is creator × brand fit, which C³ scores in ROI.Orchestration (O1), not ACE — ACE is brand-independent, so keep brand-fit out of Credibility. Before ranking, screen every creator against the three ACE veto items; any failure is disqualifying → verdict PASS (do not partner) AND cap the Final Rating at the Poor / Below-Average band (≤ 2.9 / 5, i.e. ACE ≤ 59/100) so the score never contradicts the decline. State the veto ID + evidence:
| Veto | Item | Fail condition |
|---|---|---|
| A2 | Real-Follower Rate | < 70% real followers, or audit refused (follower fraud) |
| C1 | Brand Safety | disqualifying content / active scandal |
| E2 | Engagement Authenticity | pod / bought engagement |
Score Audience Match — target-vs-actual demographics plus audience quality (real/active/bot %). Step 2 template.
Score Content Quality — production value, cadence, content mix, best examples, concerns. Step 3 template.
Score Brand Alignment — value/aesthetic/messaging fit and the Brand Safety check (feeds ACE C1). Step 4 template.
Score Engagement Quality — engagement rate vs industry avg, authenticity indicators, pod/buying signs (feeds ACE E2). Step 5 template.
Score Partnership Potential — partnership history, professionalism, exclusivity/availability, estimated value; pull prior-partnership and response-history facts from the memory/creators/ roster record when one exists. Step 6 template.
Calculate the final score — roll raw × weight into the weighted total, apply the interpretation band, write the verdict and expected performance. Step 7 template.
For multiple influencers, produce the ranking summary, dimension-by-dimension comparison, and prioritize/combine/pass recommendation. Step 8 template.
Save the report to memory/influencer/fit-scorer/YYYY-MM-DD-<topic>.md and promote top picks + verdict to memory/hot-cache.md.
User: "Compare @ecofashionista, @greenwardrobe, @sustainablesarah for our sustainable fashion brand (goal: conversion)."
Output: Each scored across the five dimensions with conversion weighting (Audience 35%, Brand 20%). @sustainablesarah ranks #1 (4.4/5) on highest audience match and authentic engagement; @greenwardrobe flagged DONE_WITH_CONCERNS on a borderline real-follower rate (A2 watch); ranked comparison + go/pass verdicts saved, top pick promoted to hot cache.
Primary: competitor-tracker — benchmark your top-scored picks against the creators competitors already work with before you commit budget.
Alternates (same discover phase):
Termination note: Track a visited-set of skills invoked this session. If the recommended next skill has already run, stop and report the chain complete rather than re-invoking it. Stop after at most 3 hops (max-depth 3) and hand back to the user with the saved report path.