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apify-trend-analysis

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

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 ./skills/apify-trend-analysis/SKILL.md

The canonical home for this skill is apify-trend-analysis in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

67%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 delivers a genuinely actionable workflow: concrete actor IDs, executable commands for all three output modes, and a specific error-recovery table. Weaknesses are localized: a filler 'When to Use' section and other noise hurt conciseness, the run_actor.js helper's interface is under-documented, and validation is not integrated as in-step checkpoints.

Suggestions

Delete the boilerplate 'When to Use' section and the '(No need to check it upfront)' parenthetical; replace vague 'Based on character of use case' with concrete default guidance (e.g., suggest 20-50 results for a quick scan).

Document run_actor.js's supported flags (including --timeout) and include one real --input example (e.g., a hashtag or keyword object) so the JSON_INPUT placeholder is concrete.

Move the 19-row actor catalog into a references/actors.md file and add an explicit checkpoint in Step 2 (e.g., confirm the schema fetch returned parameters before proceeding to Step 4).

DimensionReasoningScore

Conciseness

The commands, actor table, and error-handling list are tight, but there is clear slack: the boilerplate "When to Use" section ("Use this skill when tackling tasks related to its primary domain or functionality as described above") says nothing, the "(No need to check it upfront)" parenthetical and "Based on character of use case" are noise, and the "Best For" column largely duplicates the "User Need" column. This fits anchor 3 (mostly efficient but some unnecessary content that could be tightened) better than 4, where only minor trimming would be needed.

3 / 5

Actionability

The mcpc schema-fetch command and all three run_actor.js invocations are concrete and executable with clearly explained placeholders, and the error-handling section gives specific remediations. Minor gaps keep it below anchor 5: no example of a real JSON_INPUT payload, and the --timeout flag is referenced in error handling but never shown in any usage line.

4 / 5

Workflow Clarity

A clear five-step sequence with a copyable progress checklist, explicit step boundaries, and a dedicated error-recovery section covering the main failure modes. Not 5 because validation is not woven into the steps as explicit checkpoints (e.g., verify the schema fetch succeeded before running the actor) and Step 5's reporting is a loose list rather than a verification gate; not 3 because the sequence and error feedback are well beyond 'present but checkpoint-less'.

4 / 5

Progressive Disclosure

The body is a well-organized single file with clean section headers, and the actor catalog plus commands are reasonably placed. It misses anchor 5 because the referenced helper script (${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js) is not part of this bundle and its interface is undocumented, and the 19-row actor catalog is exactly the kind of lookup material that belongs in a separate one-level-deep reference file.

4 / 5

Total

15

/

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 is concise, in third person, and identifies a clear multi-platform niche, but it omits any explicit "when to use" trigger guidance and gives only a high-level view of capabilities. Adding a Use-when clause and more operational specifics would lift the two weakest dimensions (completeness, specificity).

Suggestions

Append an explicit trigger clause, e.g. "Use when the user asks about trending topics, hashtags, viral content, or trend tracking on Google Trends, Instagram, Facebook, YouTube, or TikTok."

State the concrete operations performed, e.g. "runs Apify Actors to scrape trend/hashtag data and export results as chat summaries, CSV, or JSON".

Include natural synonyms such as "trending", "viral", "hashtags", and "social media trends" to broaden trigger coverage.

DimensionReasoningScore

Specificity

The description names the domain with five concrete platforms and two actions ("Discover and track emerging trends"), but stops short of what is actually done (scraping, exports, formats). It matches anchor 3 (domain plus 1-2 concrete actions, not comprehensive) rather than 4, which requires several specific listed actions.

3 / 5

Completeness

The "what" is clear (discover and track emerging trends across five named platforms), but there is no "Use when..." trigger clause; "to inform content strategy" only weakly implies when. Per the guideline, a missing explicit trigger caps completeness at 3.

3 / 5

Trigger Term Quality

"Google Trends, Instagram, Facebook, YouTube, TikTok", "emerging trends", and "content strategy" are natural phrases users would say, giving good keyword coverage. It falls short of anchor 5 because common variations like "hashtags", "viral", "trending", or "social media" are absent.

4 / 5

Distinctiveness Conflict Risk

The multi-platform trend-discovery niche framed by five named platforms is mostly distinct with only minor overlap risk against other social-media scraping skills. Not 5 because "discover and track emerging trends" is broad enough to overlap with other analytics/scraper skills.

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

administrakt0r/AI-Agents-Safe-Coding-Skills

Path

skills/apify-trend-analysis/SKILL.md

Reviewed

Table of Contents

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