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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.

60

Quality

70%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/apify-influencer-discovery/SKILL.md

The canonical home for this skill is apify-influencer-discovery in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

68%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.

The body is actionable and well-structured with a clear five-step workflow, but it lacks an explicit validation checkpoint for a batch scraping operation, which caps workflow clarity.

Suggestions

Insert a validation checkpoint after Step 4 (e.g., confirm the actor run finished successfully and inspect row count before summarizing).

Replace the 'JSON_INPUT' placeholder with a small worked example so the run commands are copy-paste ready.

Move the large actor-selection table into a reference file and summarize the top options inline, keeping the overview lean.

DimensionReasoningScore

Conciseness

Mostly lean with concrete commands and a useful actor table, with only minor padding such as the restated opening line and the parenthetical '(No need to check it upfront)'.

4 / 5

Actionability

Provides concrete, executable mcpc and node commands with real Actor IDs, held back only by the generic 'JSON_INPUT' placeholder that lacks a worked example.

4 / 5

Workflow Clarity

Five sequenced steps with a checklist are clear, but this is a batch scraping operation with no explicit validation checkpoint confirming the run succeeded before summarizing, capping the score at 3.

3 / 5

Progressive Disclosure

Well-organized sections with a clearly signaled one-level reference to the run_actor.js script; the inline 16-row actor table is borderline but appropriate for selection guidance.

4 / 5

Total

15

/

20

Passed

Description

71%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.

The description is specific and well-scoped with strong natural trigger terms, but it omits any explicit 'when to use' guidance, leaving the completeness dimension capped.

Suggestions

Add an explicit 'Use when...' clause naming trigger phrases (e.g., 'Use when finding influencers for brand campaigns, verifying creator authenticity, or tracking sponsorship performance').

Include common synonyms such as 'creators' or 'content creators' alongside 'influencers' to broaden trigger coverage.

Reference the Apify/Actor tooling in the description so the skill is distinguishable from generic social-media analytics skills.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('Find and evaluate influencers', 'verify authenticity', 'track collaboration performance') across named platforms, giving comprehensive coverage rather than vague abstraction.

5 / 5

Completeness

A clear 'what' is present but there is no 'Use when...' clause or equivalent trigger guidance, which caps completeness at 3 per the judging guidelines.

3 / 5

Trigger Term Quality

Strong natural keywords ('influencers', 'brand partnerships', 'Instagram, Facebook, YouTube, TikTok') that users would say, but misses common synonyms like 'creator' or 'content creator'.

4 / 5

Distinctiveness Conflict Risk

The influencer-discovery niche across specific platforms is mostly distinct, with only minor overlap risk against a generic Apify or social-analytics skill.

4 / 5

Total

16

/

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.

Validation15 / 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
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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