apify-influencer-discovery

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

6.60x

Quality

66%

Does it follow best practices?

Impact

99%

6.60x

Average score across 3 eval scenarios

Securityby

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./plugins/antigravity-awesome-skills-claude/skills/apify-influencer-discovery/SKILL.md

Quality

Content

64%Scale 1-3

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

This skill provides solid actionable guidance with executable commands and a comprehensive Actor selection table for influencer discovery across multiple platforms. Its main weaknesses are the lack of validation checkpoints in the workflow (no verification after schema fetch or before/after Actor runs) and some verbosity in the Actor table and boilerplate limitations section. The referenced script path cannot be verified without bundle files.

Suggestions

Add validation checkpoints after Step 2 (verify schema was fetched successfully) and after Step 4 (verify results are non-empty and contain expected fields before summarizing).

Move the large Actor selection table to a separate reference file (e.g., ACTORS.md) and keep only the most common 4-5 actors inline with a link to the full list.

Remove the generic Limitations section or replace it with specific constraints (e.g., rate limits, maximum results per run, platform-specific restrictions).

Add a feedback loop in Step 4: if the run returns 0 results or fails, suggest adjusting input parameters and re-running.

Dimension	Reasoning	Score
Conciseness	The skill is reasonably efficient but includes some unnecessary elements: the large Actor table could be more compact, the Limitations section contains generic boilerplate that doesn't add value, and the checklist format adds tokens without much benefit. The error handling section is appropriately concise.	2 / 3
Actionability	The skill provides fully executable bash commands with clear placeholders, a well-structured Actor selection table, and specific command patterns for each output format. The mcpc command for fetching schemas is copy-paste ready with clear substitution points.	3 / 3
Workflow Clarity	The 5-step workflow is clearly sequenced with a progress checklist, but lacks validation checkpoints. There's no step to verify the Actor schema was fetched correctly, no validation of the JSON input before running, and no feedback loop if results are unexpected or empty. For a multi-step process involving external API calls, explicit validation steps are needed.	2 / 3
Progressive Disclosure	The skill references `${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js` but no bundle files are provided to verify this exists. The content is reasonably structured with clear sections, but the large Actor table could be split into a reference file, and there are no links to additional documentation for advanced use cases.	2 / 3
	Total	9 / 12 Passed

Description

67%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 in specificity and distinctiveness, clearly naming concrete actions and specific platforms. Its main weakness is the absence of an explicit 'Use when...' clause, which limits completeness. Adding trigger guidance and a few more natural user terms would make this an excellent description.

Suggestions

Add a 'Use when...' clause, e.g., 'Use when the user asks about finding influencers, evaluating creators for partnerships, or tracking sponsorship performance.'

Include additional natural trigger terms users might say, such as 'creator', 'KOL', 'sponsorship', 'engagement rate', or 'social media marketing'.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'find and evaluate influencers', 'verify authenticity', 'track collaboration performance', and names specific platforms (Instagram, Facebook, YouTube, TikTok).	3 / 3
Completeness	Clearly answers 'what does this do' with specific actions and platforms, but lacks an explicit 'Use when...' clause or equivalent trigger guidance, which caps this at 2 per the rubric.	2 / 3
Trigger Term Quality	Includes good keywords like 'influencers', 'brand partnerships', 'Instagram', 'TikTok', 'YouTube', 'Facebook', but misses common user variations like 'creator', 'KOL', 'sponsorship', 'engagement rate', 'follower count', or 'social media marketing'.	2 / 3
Distinctiveness Conflict Risk	The combination of influencer discovery, authenticity verification, and collaboration tracking across named social platforms creates a clear, distinct niche that is unlikely to conflict with other skills.	3 / 3
	Total	10 / 12 Passed

Validation

90%

Warnings & errors only

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

Validation — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

Repository: popey/claude-code-skills
Path: plugins/antigravity-awesome-skills-claude/skills/apify-influencer-discovery/SKILL.md
Commit: a5a6601

Reviewed: 5 days ago

Table of Contents

Discovery Implementation Validation

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