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apify-content-analytics

Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.

58

Quality

67%

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/skills/apify-content-analytics/SKILL.md

The canonical home for this skill is apify-content-analytics in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

68%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 well-structured, actionable, and token-efficient, with a clear multi-step workflow and error handling. Its main gap is the absence of an explicit validation/verification checkpoint for what is fundamentally a batch data-collection operation.

Suggestions

Add an explicit verification checkpoint in Step 5 (e.g., confirm the output file exists, check row count matches expected results, and validate non-empty data before summarizing) to satisfy the batch-operation validation requirement.

Show a concrete example of constructing JSON_INPUT from the schema fetched in Step 2, so the run command is fully copy-paste ready.

Trim the restated intro line and the 'This returns:' bullets, which overlap with information Claude can derive from the command and schema.

DimensionReasoningScore

Conciseness

The body is lean with a dense actor-selection table and no over-explanation of concepts Claude already knows; only minor padding (the intro restates the description, and the 'This returns:' bullets are slightly explanatory) keeps it from a 5.

4 / 5

Actionability

Provides concrete, mostly copy-paste-ready commands (mcpc schema fetch plus three run_actor.js variants for quick/CSV/JSON) with real actor IDs, but placeholders like ACTOR_ID and JSON_INPUT require substitution and the input-construction step is not shown.

4 / 5

Workflow Clarity

A clear 5-step sequence with a copyable checklist and an error-handling section exists, but this is a batch/scraping operation with no explicit verification checkpoint before reporting, so per the batch-operation cap workflow clarity stays at 3.

3 / 5

Progressive Disclosure

Good section structure (Prerequisites, Workflow, Error Handling) with a one-level-deep script reference; the large inline actor table is justified as a decision matrix, though no separate reference docs are linked.

4 / 5

Total

15

/

20

Passed

Description

66%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 states concrete capabilities across four named platforms but lacks any explicit 'when to use' trigger guidance, which caps its completeness. Trigger-term and specificity coverage is good though not exhaustive.

Suggestions

Add an explicit 'Use when...' clause naming natural trigger phrases (e.g., 'Use when the user asks for Instagram/Facebook/YouTube/TikTok engagement metrics, campaign ROI, or content performance analytics').

Broaden trigger terms with synonyms users actually say — 'social media analytics', 'followers', 'likes', 'views', 'reach' — to improve activation on natural requests.

Consider listing one or two more concrete actions (e.g., reel/comment/hashtag analytics) to lift specificity toward comprehensive coverage.

DimensionReasoningScore

Specificity

Names three concrete actions ('Track engagement metrics, measure campaign ROI, and analyze content performance') plus four platforms, but stops short of the comprehensive action list the body actually supports (reels, comments, hashtags, follower growth).

4 / 5

Completeness

The 'what' is clear, but there is no 'Use when...' clause or equivalent explicit trigger guidance, which per the judging guidelines caps completeness at 3.

3 / 5

Trigger Term Quality

Includes natural phrases a user would say ('engagement metrics', 'campaign ROI', 'content performance', and the four platform names), but omits common synonyms and specific metric terms like 'analytics', 'social media', 'followers', 'likes', or 'reach'.

4 / 5

Distinctiveness Conflict Risk

Naming four specific platforms plus a content-analytics niche makes it mostly distinct, with only minor overlap risk against other social-media or general analytics skills.

4 / 5

Total

15

/

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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