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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

68

Quality

85%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

70%Weight 40%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-structured with strong workflow sequencing and clean progressive disclosure, and it avoids explaining concepts Claude already knows. Its weaknesses are repeated guardrail restatements (conciseness) and delegation of the core scoring mechanics to referenced files, leaving the body's actionability concrete but not self-contained.

Suggestions

Consolidate the Suitability-vs-commercial-fit / SQS guardrail into one stated location (e.g. the intro) and reference it from steps 3, 6, and the example rather than restating it each time.

Inline a minimal, copy-paste-ready scoring skeleton (the S1-S10 state table with one example row) so the body is actionable without opening references/scoring-templates.md.

Trim the duplicated STAR-S2/S6 veto handling between step 4 and the Compact Example to a single authoritative statement.

DimensionReasoningScore

Conciseness

The body is largely free of concepts Claude already knows, but the same guardrail is restated several times — "not a Suitability score and never enters the SQS" appears in the intro, step 3, step 6, and the compact example — which is padding that could be tightened. It is above level 1 because it does not explain basic concepts, but not level 3 due to the redundancy.

2 / 3

Actionability

It gives one concrete executable command (`python3 ...youtube.py videos @handle --limit 10`), exact save paths, and a specific typed-field checklist (target, rollup_id, assessment_time, catalog_version) with a NEEDS_INPUT return. However, the actual scoring tables and Suitability item definitions are delegated to referenced files rather than present inline, so the body's guidance is concrete but incomplete on its own.

2 / 3

Workflow Clarity

The 8-step Instructions are clearly sequenced with explicit validation gates — step 1 returns NEEDS_INPUT and preserves identity for resume, step 2 forbids softening Unknown to Fail/Partial, step 4 holds outreach on qualifying Fail evidence — plus a "Done when" checklist. This matches the anchor for a clear sequence with explicit validation steps and a checklist, exceeding the level-2 implicit-checkpoint case.

3 / 3

Progressive Disclosure

The body is a clean overview (Quick Start, Skill Contract, Data Sources, Instructions, Compact Example, Reference Materials, Next Best Skill) with well-signaled one-level references; the in-bundle reference references/scoring-templates.md is a real file and clearly labeled. It avoids the level-2 case of inline content that should be split, since detailed scoring tables are correctly pushed to references.

3 / 3

Total

10

/

12

Passed

Description

100%Weight 40%Scale 1-3

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong: third-person trigger voice, concrete outputs, explicit natural-language triggers, and clear negative scope boundaries that separate it from sibling skills. No first/second-person voice to penalize.

DimensionReasoningScore

Specificity

It names multiple concrete actions — "produces the typed STAR Suitability (S) read plus a separately labeled campaign-fit ranking" — rather than vague abstractions, matching the anchor for listing several specific concrete actions. It avoids the level-2 partial-coverage case by stating both outputs explicitly.

3 / 3

Completeness

It clearly answers both what (produces the Suitability read plus a separate campaign-fit ranking) and when (an explicit "Use when the user asks to..." trigger clause). It is above level 2 because the when is explicit, not merely implied, and below scale max only by being a 1–3 scale.

3 / 3

Trigger Term Quality

It quotes natural user phrasing — "score this influencer", "rank these creators for our campaign", "tell me which influencer is the best fit" — giving good coverage of terms a user would actually say. It is not the level-2 case of relevant-but-incomplete keywords because it supplies several common variations.

3 / 3

Distinctiveness Conflict Risk

It states explicit negative boundaries — "Not for finding new influencers — use influencer-discovery; not for sending outreach — use outreach-manager" — carving a clear niche unlikely to fire for the wrong skill. This exceeds the level-2 overlap case by naming sibling skills to defer to.

3 / 3

Total

12

/

12

Passed

Validation

75%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation12 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 24 suspicious

Warning

referenced_paths_exist

Referenced path issues: 1 missing, 1 deeper-than-1-level

Warning

Total

12

/

16

Passed

Repository
aaron-he-zhu/aaron-marketing-skills
Reviewed

Table of Contents

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