apify-influencer-discovery
Find and evaluate influencers for brand partnerships, verify authenticity, and track collaboration performance across Instagram, Facebook, YouTube, and TikTok.
77
66%
Does it follow best practices?
Impact
99%
6.60xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/apify-influencer-discovery/SKILL.mdQuality
Discovery
67%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 elevate this to an excellent description.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about influencer marketing, finding creators, vetting influencers, or tracking sponsorship ROI.'
Include additional natural trigger terms users might say, such as 'creator partnerships', 'influencer marketing', 'sponsorship', 'KOL', or 'engagement rate'.
| 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 dimension at 2 per the rubric. | 2 / 3 |
Trigger Term Quality | Includes good keywords like 'influencers', 'brand partnerships', 'Instagram', 'YouTube', 'TikTok', but misses common user variations like 'creator', 'KOL', 'sponsorship', 'influencer marketing', 'engagement rate', or 'fake followers'. | 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 |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a functional skill with strong actionability—concrete commands, a useful Actor lookup table, and clear error handling. Its main weaknesses are the lack of validation/feedback loops in the workflow (e.g., no check that the Actor run succeeded before summarizing) and some verbosity from the large inline table and generic boilerplate limitations section. The structure is reasonable but could benefit from better progressive disclosure by moving the Actor catalog to a reference file.
Suggestions
Add a validation checkpoint after Step 4 to verify the Actor run succeeded (check exit code or output) before proceeding to summarize, and include a retry/fix loop for failed runs.
Move the 16-row Actor lookup table to a separate reference file (e.g., ACTORS.md) and link to it from the main skill to improve progressive disclosure and reduce inline bulk.
Remove or significantly trim the generic Limitations section—these are boilerplate instructions Claude doesn't need repeated.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The large Actor table is useful reference but could be more compact. The limitations section contains generic boilerplate that doesn't add value. The workflow is reasonably efficient but has some padding (e.g., 'Based on character of use case' is vague filler). | 2 / 3 |
Actionability | Provides fully executable bash commands with clear flag options, concrete Actor IDs in a lookup table, and specific error messages with resolutions. The commands are copy-paste ready with clear placeholder substitution patterns. | 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 run succeeded before summarizing, no feedback loop for retrying failed runs, and no validation of the fetched schema before proceeding. | 2 / 3 |
Progressive Disclosure | References external scripts appropriately (run_actor.js) and uses mcpc for dynamic schema fetching, but the 16-row Actor table is a large inline block that could be in a separate reference file. The skill is moderately long and could benefit from splitting the Actor catalog into a separate document. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%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
- sickn33/antigravity-awesome-skills
- Commit
b938655
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.