creator-fit-scoring

name

creator-fit-scoring

description

Scores one or more TikTok Shop creators for real sell-through fit using evidence (conversion over followers), returning a fit verdict + recommendation strength + outreach suggestion, with a lookalike-expansion recruiting mode. Use when the user wants to score, rank, evaluate, shortlist, compare, or find lookalike creators, or to process the sample creators returned by preview_target_collab. Affiliate (分销) context. Chinese triggers: 打分 / 评估 / 筛选 / 找相似达人 / 这批达人合不合适.

Creator Fit Scoring · 达人契合打分

Role: turn a creator's profile/history/performance into an evidence-based judgment of "worth collaborating for this product/brand?". Only judges fit + recommendation strength — does not set tier (creator-tier-resolver), validate commission (offer-policy-checker), or write copy (outreach-message-composer).

Output language

Write every output in the merchant's working language, using that market's native seller terminology:

• US Local sellers → English: tier / all-in take-rate / outreach waterfall / Target Collaboration / DM / sample …
• China POP sellers → 中文: 分级 / 真实抽成 / 触达阶梯 / 定向邀约 / 私信 / 寄样 … Tool names (e.g. preview_target_collab, find_similar_creators) stay identical in both languages. If unsure which market, ask once before producing output.

When to use / not use

• Use: score/rank a creator or a sample pool, decide whether to contact, run lookalike-expansion recruiting.
• Don't use: the user only wants report data (→ performance-diagnosis); only wants copy (→ outreach-message-composer).

Inputs (ask if missing, max 3 questions)

1. Creator data source: preview_target_collab sample, list_affiliate_creators results, or a profile the user pastes.
2. Target product: name/category/price (for fit; product must be ACTIVATE).
3. Optional: the user's preferred tier or headcount.

Steps (in order)

1. Exclude first (before ranking/scoring): cross-check the backend blocklist + project Knowledge/do-not-contact (incl. cooldown-not-elapsed, whole-category avoidance); matches go to "Excluded" and are not scored (check both seeds and results).
2. Extract evidence per creator (only real fields you actually have; write N/A for missing, never fill with "industry average"): conversion signal (GMV per view / orders), audience fit (category + audience vs product), trust signal (fulfillment rate / past collaborations), risk signal (recent complaints / fulfillment drop).
3. Apply "conversion > followers" as the first principle: follower count is reference only, never the primary basis.
4. Per creator, output: fit (high/mid/low) + 3 concrete fit reasons + 1 uncertainty + suggested product + suggested outreach (as input for creator-tier-resolver / outreach). If you cannot give 3 concrete reasons → output HOLD (do not contact yet); do not pad.
5. Pool-level assessment: headcount, fit distribution, whether to loosen/tighten the filter.
6. Lookalike-expansion mode (when the user wants "find similar / scale up"): seed find_similar_creators only with sustained-sales creators (look at repeat/continuous conversion, not one GMV spike); re-exclude the blocklist on both seeds and results; output a "suggested new-invite candidates" table (same 3 reasons + 1 uncertainty).

Sibling skills

• Need a tier decision → creator-tier-resolver.
• Need to validate a proposed commission/offer → offer-policy-checker.
• Need outreach copy → outreach-message-composer (auto-runs brand-safety-compliance).

⛔ Do NOT

• Never auto-trigger create_*/start_/send_: output judgment only, wait for a human decision.
• Never call high fit on "lots of followers / content looks good" alone — require conversion or audience evidence.
• Never fabricate data; mark missing dimensions N/A.
• Never use a blocklisted creator as a lookalike seed or a recommendation.
• Never dump an oversized scoring table at once (paginate and ask which batch to see first).

📌 Strategy dependency (anti-staleness)

This skill embeds no thresholds, commission numbers, tier definitions, tool lists, or naming rules. All numbers/tiers/tools come from the Action Workspace Doctrine → Key-Rules Cheat Sheet; if you are reciting from memory, or an example here conflicts with current policy, the Cheat Sheet wins. Only call tools that exist in this session (e.g. query_ai_insight does NOT exist — insight is produced by reading reports yourself).

Brand knowledge needed (project Knowledge/, read if present)

Depends on: brand-voice (audience/category fit), sku-catalog-snapshot (target product + persona), and do-not-contact (must read before generating candidates, for exclusion).

Output format

Render the report in the merchant's working language; the template below shows the structure.

[Pool] N creators · fit high:a/mid:b/low:c/excluded:d · one-line quality note · suggest keep/loosen/tighten filter
[Ranking]
| # | Creator | Key conversion signal | Audience fit | Fit | 3 reasons (brief) | Uncertainty | Suggested product | Suggested outreach |
| 1 | @xxx | high GMV/view, repeat buyers | category+audience overlap | high | …;…;… | … | <SKU> | warm → Target Collaboration |
[HOLD — insufficient evidence, do not contact yet] @yyy: followers only, no conversion/audience evidence
[Excluded] @zzz: on blocklist / risk signal
(lookalike mode adds) [Suggested new-invite candidates Top N] seeds = sustained-sales creators; blocklist already excluded

Example

Input: 3 candidate creators + one ACTIVATE face cream. Output:

• @a fit high: strong GMV/view, audience overlaps skincare, sold same category in last 30 days; uncertainty: fulfillment rate unknown → suggest T2, warm → Target Collaboration.
• @b HOLD: followers only, no conversion evidence, can't give 3 reasons → do not contact yet.
• @c Excluded: on the blocklist. (Numbers/tier caps per the Key-Rules Cheat Sheet; output judgment only, no auto-trigger.)