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apify-influencer-discovery

Find and evaluate influencers for brand partnerships, verify authenticity, and track collaboration performance across Instagram, Facebook, YouTube, and TikTok.

78

2.41x
Quality

70%

Does it follow best practices?

Impact

94%

2.41x

Average score across 3 eval scenarios

SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./skills/apify-influencer-discovery/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

75%Weight 40%Scale 1-5

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

A well-structured, lean workflow skill with concrete executable commands, a clear five-step sequence, and a useful error-handling section. The main weaknesses are the missing example of an actor input payload, no explicit output-verification step before summarizing, and an unverifiable external script reference.

DimensionReasoningScore

Conciseness

The body is efficient — a compact actor-selection table, single-line error handling, and no explanations of concepts Claude already knows — but the three near-identical bash blocks for Quick/CSV/JSON, the "(No need to check it upfront)" parenthetical, and generic Limitations boilerplate could be trimmed, matching anchor 4 rather than the fully lean anchor 5.

4 / 5

Actionability

Concrete, executable commands are given (the mcpc schema-fetch pipeline with jq, and copy-paste node invocations of run_actor.js with flags), matching 'mostly executable with minor gaps'; the gap is that no example JSON_INPUT is ever shown for even one actor, leaving input construction entirely implicit from the fetched schema.

4 / 5

Workflow Clarity

A clear five-step sequence with a copyable progress checklist, a schema-fetch step that prevents bad input, user-preference confirmation before the paid run, and an error-handling map for recovery — matching anchor 4. It falls short of anchor 5 because there is no explicit output-verification checkpoint (e.g., confirm the run succeeded and the output is non-empty before summarizing in Step 5).

4 / 5

Progressive Disclosure

Good structure with well-organized sections, an inline actor table that supports an in-context decision, and a single clearly signaled external reference (the run_actor.js script invoked in the code blocks), matching anchor 4. It does not reach anchor 5 because the body is ~120 lines and the referenced script (${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js) is not present in the skill bundle (no references/, scripts/, or assets/ directories exist) and uses a nonstandard 'reference/scripts' path, so it cannot be verified.

4 / 5

Total

16

/

20

Passed

Description

66%Weight 40%Scale 1-5

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

A solid, mostly specific description that clearly conveys the domain and platforms but omits any explicit 'when to use' trigger guidance, which caps its completeness. Adding a 'Use when...' clause with natural trigger phrases would lift it into the top tier.

Suggestions

Append an explicit trigger clause, e.g. "Use when the user asks to find influencers or creators for outreach, brand deals, or campaign planning, or wants to vet an influencer's authenticity or engagement."

Include common user synonyms such as "creators", "creator marketing", "sponsorships", and "engagement" to improve trigger-term coverage.

Mention the Apify-based mechanism briefly so the skill is distinguishable from generic social-media scraping skills.

DimensionReasoningScore

Specificity

"Find and evaluate influencers for brand partnerships, verify authenticity, and track collaboration performance" names several concrete actions across four named platforms, but the actions are high-level (no mention of what metrics are pulled or how evaluation works), matching the 'several specific actions; minor gaps' anchor rather than the comprehensive anchor 5.

4 / 5

Completeness

The 'what' is clear (find, evaluate, verify, track), but there is no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines; the platform list weakly implies 'when' but never states it, so it does not reach anchor 4.

3 / 5

Trigger Term Quality

Natural phrases users would say are present — "influencers", "brand partnerships", "authenticity", "collaboration", "Instagram, Facebook, YouTube, and TikTok" — but common variations like "creators", "creator marketing", "sponsorships", or "engagement" are missing, fitting anchor 4 rather than the comprehensive synonym coverage of anchor 5.

4 / 5

Distinctiveness Conflict Risk

The influencer-discovery/brand-partnership niche with four named platforms is mostly distinct with minor overlap risk against generic social-media scraping skills; it does not fully reach anchor 5 because the description omits the Apify-based mechanism, leaving some ambiguity versus other influencer or scraping skills.

4 / 5

Total

15

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
sickn33/agentic-awesome-skills
Reviewed

Table of Contents

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