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apify-trend-analysis

Discover and track emerging trends across Google Trends, Instagram, Facebook, YouTube, and TikTok to inform content strategy.

58

Quality

68%

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

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tessl review fix ./skills/apify-trend-analysis/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.

The body is a well-structured, mostly executable workflow with a clear 5-step sequence, a decision table for actor selection, and concrete error recovery guidance. Remaining gaps are the placeholder input JSON with no example, generic boilerplate sections, and the absence of explicit validation feedback loops around batch scraping runs.

Suggestions

Add a concrete example JSON input for at least one actor so the `--input 'JSON_INPUT'` placeholder is grounded.

Delete or rewrite the generic "When to Use" and "Limitations" sections, which restate template text and add no skill-specific information.

Add a verification step after Step 4 (e.g., check the output file is non-empty and spot-check a few rows) to close the validation gap for batch runs.

DimensionReasoningScore

Conciseness

The body is efficient — a compact actor decision table, copy-paste commands, and an error table with no concept explanations. Not a 5 because the boilerplate "When to Use" section ("Use this skill when tackling tasks related to its primary domain or functionality as described above") and generic Limitations section are template filler that could be trimmed.

4 / 5

Actionability

Provides executable commands (the mcpc fetch call with a concrete example actor, and three complete `node --env-file=.env ... run_actor.js` invocations) plus concrete error fixes. Not a 5 because `JSON_INPUT` is a placeholder with no example input object, and the referenced script (`reference/scripts/run_actor.js`) is not in the bundle for verification.

4 / 5

Workflow Clarity

A copyable progress checklist and five clearly sequenced steps, with a schema-fetch checkpoint (Step 2) and a user-preference gate (Step 3) before any run, plus an error-handling table with recovery actions. Not a 5 because error handling is a lookup table rather than embedded validate→fix→retry loops, and output verification beyond "report number of results" is thin for batch scraping runs.

4 / 5

Progressive Disclosure

Well-organized sections with the only external reference being a single script path (`${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js`), one level deep; no bundle files exist to score against. Not a 5 because the skill exceeds 50 lines and the 19-row actor catalog is inlined content that could live in a reference file.

4 / 5

Total

16

/

20

Passed

Description

61%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 clearly communicates what the skill does and which platforms it covers, with strong natural platform-name triggers. Its main weakness is the missing "Use when..." trigger clause, which caps completeness, and the actions described ("discover", "track") remain generic despite the specific platform scope.

Suggestions

Add an explicit trigger clause, e.g. "Use when the user asks about trending topics, hashtags, viral content, or trend tracking on social platforms."

Name one or two concrete operations instead of generic verbs, e.g. "scrape hashtag stats, trending sounds, and search trends, and export results to CSV or JSON".

Include common synonyms users would say — "viral", "hashtags", "social media trends" — to strengthen trigger term coverage.

DimensionReasoningScore

Specificity

"Discover and track emerging trends" are generic verbs, but the enumeration of five concrete platforms (Google Trends, Instagram, Facebook, YouTube, TikTok) names the domain with 1-2 actions and concrete scope. Not a 4 because no specific operations (scrape hashtags, export CSV, pull stats) are stated.

3 / 5

Completeness

The "what" is clear ("Discover and track emerging trends across..."), but there is no "Use when..." clause or equivalent explicit trigger guidance, which caps completeness at 3 per the rubric guideline. Not a 2 because the "what" half is concrete and specific.

3 / 5

Trigger Term Quality

Platform names ("Google Trends", "Instagram", "TikTok") plus "emerging trends" and "content strategy" are natural terms users would say, giving good keyword coverage. Not a 5 because common variations like "hashtags", "viral", "social media", or "scraper" are missing.

4 / 5

Distinctiveness Conflict Risk

The named platforms carve a mostly distinct niche unlikely to trigger for unrelated skills. Not a 5 because "trends... to inform content strategy" could overlap with general social-media analytics or content-planning skills since no distinct trigger phrases are given.

4 / 5

Total

14

/

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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