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apify-content-analytics

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

Quality

68%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/apify-content-analytics/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

56%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 presents a clear, well-sequenced paid-API workflow with good safety boundaries and error handling, and it avoids teaching known concepts. Its main weaknesses are a broken core dependency (the referenced runner script is absent from the bundle), placeholder-laden commands, and duplicated bash examples that inflate token cost.

Suggestions

Bundle the referenced 'run_actor.js' script (or fix the path to the actual file) so the Step 4 commands are executable — currently the sole referenced path does not exist in the skill directory.

Collapse the three near-identical bash examples into one canonical example plus a short note on the '--output'/'--format' flags for CSV and JSON, saving roughly 10 lines of duplicated tokens.

Move the 17-row Actor catalog to a single reference file (e.g. references/actor-catalog.md) and keep a short inline selection guide, improving both conciseness and progressive disclosure.

DimensionReasoningScore

Conciseness

Mostly efficient with no explanations of concepts Claude already knows, but the three near-identical bash blocks in Step 4 (differing only in flags), the dense run-on 'Run Boundaries' paragraph, and boilerplate Limitations lines could be tightened; this is more than the 'minor instances' of the 4 anchor.

3 / 5

Actionability

Concrete commands and a specific Actor ID table exist, but every command relies on unresolved placeholders ('<resolved-skill-directory>', 'ACTOR_ID', 'JSON_INPUT') and the referenced runner script 'reference/scripts/run_actor.js' does not exist in the bundle (no references/ or scripts/ directory), so the core execution path is incomplete — key details are missing, matching the 3 anchor rather than the mostly-executable 4.

3 / 5

Workflow Clarity

A clear five-step sequence with a copyable progress checklist, a prerequisites gate before any paid run, a user-confirmation checkpoint before launch, an error-to-recovery table, and a duplicate-charge guard on timeout; it falls short of 5 because there is no explicit post-run validation loop (e.g., verify the output file is non-empty and row counts are sane before reporting).

4 / 5

Progressive Disclosure

Internal sectioning is good, but the single external reference — 'reference/scripts/run_actor.js' — is a dangling path (the bundle contains no such file or directory), and the 17-row Actor catalog is inline content that arguably belongs in a reference file; structure exists but organization and reference signaling are incomplete, matching the 3 anchor.

3 / 5

Total

13

/

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.

A solid, moderately specific description that clearly states what the skill does and names the target platforms, but it entirely lacks a 'when to use it' clause, which caps completeness and weakens its trigger function. Keyword coverage is good though missing common synonyms like 'social media analytics' or 'followers'.

Suggestions

Append an explicit trigger clause, e.g. 'Use when the user needs engagement, ROI, or content-performance data for Instagram, Facebook, YouTube, or TikTok, or mentions social media analytics, follower growth, or post/reel performance.'

Mention the mechanism and outputs to sharpen specificity and distinctiveness, e.g. '...by running Apify scrapers and exporting CSV/JSON results' — this also distinguishes it from generic analytics skills.

Add natural synonyms users would say such as 'social media analytics', 'follower growth', and 'post/video performance' to improve trigger term coverage.

DimensionReasoningScore

Specificity

Lists three concrete actions ('Track engagement metrics, measure campaign ROI, and analyze content performance') across a named multi-platform domain, matching the several-specific-actions anchor; it is not a 5 because the mechanism (Apify Actors, scraping, export formats) is omitted, leaving minor coverage gaps.

4 / 5

Completeness

The 'what' is clear (track, measure, analyze across four platforms) but there is no 'Use when...' clause or equivalent trigger guidance, which per the judging guidelines caps completeness at 3; it is not a 2 because the 'what' is concrete rather than vague.

3 / 5

Trigger Term Quality

Good natural keyword coverage ('engagement metrics', 'campaign ROI', 'content performance') plus all four platform names (Instagram, Facebook, YouTube, TikTok), but misses common variations like 'social media analytics', 'followers', 'posts/reels', so it falls short of the comprehensive-synonyms anchor.

4 / 5

Distinctiveness Conflict Risk

The explicit platform enumeration carves a mostly distinct niche with minor overlap risk against other social/analytics skills; it is not a 5 because phrases like 'engagement metrics' and 'content performance' are broad enough to overlap with related skills, and no mechanism (Apify) is named to sharpen the niche.

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.

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

Path

skills/apify-content-analytics/SKILL.md

Reviewed

Table of Contents

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