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apify-trend-analysis

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

55

Quality

63%

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-claude/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

65%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 action-oriented and reasonably concise with executable commands and a clear five-step sequence, but it misses validation checkpoints for a batch-scraping workflow and inlines reference data (the actor table) that belongs in a separate file. Adding an explicit verify step and splitting the actor table into a bundled reference file would lift the score.

Suggestions

Add an explicit validation checkpoint between Step 4 and Step 5, e.g. "Verify the output file exists and contains results; if empty or FAILED, consult Error Handling before summarizing."

Move the 19-row actor selection table into a bundled references file (e.g. references/actors.md) and link to it from Step 1, keeping only a few representative rows inline.

Replace the generic "When to Use" filler sentence with a concrete trigger statement, or remove it since the frontmatter description already covers purpose.

DimensionReasoningScore

Conciseness

The body is lean with executable commands and a focused table, avoiding concept explanations Claude already knows; only minor filler (the generic "When to Use" sentence and a redundant intro line) could be trimmed.

4 / 5

Actionability

It provides concrete, mostly executable commands (mcpc fetch-actor-details, node run_actor.js with --actor/--input/--output/--format flags) and a concrete error-handling table, with minor gaps from un-substituted placeholders and an unseen run_actor.js script.

4 / 5

Workflow Clarity

The five steps are sequenced with a tracking checklist, but this batch-scraping workflow lacks explicit validation checkpoints (e.g. verifying output existence or non-empty results), so per the rubric cap workflow clarity stays at 3.

3 / 5

Progressive Disclosure

No bundle files exist in ./references/, ./scripts/, or ./assets/, yet the 19-row actor reference table is inlined in SKILL.md and the only referenced script (run_actor.js) lives outside the bundle, so organization could be improved by splitting the table into a reference file.

3 / 5

Total

14

/

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 a specific niche (cross-platform trend analysis) with strong natural platform keywords, but it omits an explicit "Use when..." trigger clause and relies on fairly generic verbs. Adding concrete trigger guidance and sharper action verbs would raise the score.

Suggestions

Append an explicit trigger clause, e.g. "Use when the user wants to research or compare trending topics, hashtags, or content across Google Trends, Instagram, Facebook, YouTube, or TikTok."

Replace generic verbs ("Discover and track") with more specific actions like "Scrape and compare trending topics, hashtags, and post engagement across platforms."

Include common synonyms/file-format terms users might say (e.g. "trend research", "viral content", "hashtag analytics") to broaden trigger coverage.

DimensionReasoningScore

Specificity

"Discover and track emerging trends across [five platforms]" names the domain and two concrete actions (discover, track), but the verbs are generic and coverage is not comprehensive.

3 / 5

Completeness

It clearly states what the skill does but provides no "Use when..." trigger clause, so per the rubric guideline completeness is capped at 3.

3 / 5

Trigger Term Quality

It lists natural platform names a user would actually say ("Google Trends, Instagram, Facebook, YouTube, and TikTok") plus "emerging trends" and "content strategy", though a few common synonyms are missing.

4 / 5

Distinctiveness Conflict Risk

The multi-platform social trend-analysis niche is mostly distinct with platform-specific triggers, with only minor overlap risk against generic analytics 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.

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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