apify-audience-analysis

Understand audience demographics, preferences, behavior patterns, and engagement quality across Facebook, Instagram, YouTube, and TikTok.

Quality

68%

Does it follow best practices?

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Securityby

Low

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tessl review fix ./skills/apify-audience-analysis/SKILL.md

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 content is a strong, actionable workflow: concrete commands, a clear step sequence with a checklist, and a useful error-recovery section. The main gaps are the absence of a concrete JSON input example, some redundancy across the three output-format command blocks, and no explicit post-run validation step.

Suggestions

Include one concrete 'JSON_INPUT' example (e.g. for an Instagram or Facebook actor) so the run command is fully copy-paste ready, and document '--timeout' in the run commands rather than only in error handling.

Collapse the three near-identical node command blocks into one command with a note on the optional '--output'/'--format' flags to save tokens.

Add a lightweight validation step after Step 4 (e.g. verify the output file exists and has the expected row/record count) before summarizing findings.

Dimension	Reasoning	Score
Conciseness	The body is efficient — an actor-selection table, copy-paste commands, and a terse error list with no explanations of concepts Claude already knows. Minor trimming is possible: three near-identical bash blocks for quick/CSV/JSON differ only in flags, and "Based on character of use case" is filler, keeping it just below anchor 5.	4 / 5
Actionability	Concrete, executable guidance throughout: a complete mcpc schema-fetch command with jq, node script invocations with explicit flags, and per-error remediation steps. It falls short of anchor 5 because 'JSON_INPUT' is never shown with a real example and '--timeout' appears only in the error-handling section, not in the run commands.	4 / 5
Workflow Clarity	A clearly sequenced 5-step workflow with a copyable progress checklist, a user-preference checkpoint before running, and an error-handling section providing recovery actions. It misses anchor 5 because there is no explicit output-validation step after a run (Step 5 summarizes but does not verify results).	4 / 5
Progressive Disclosure	Well-organized single-file skill with clear section headers and a one-level-deep, clearly signaled reference to a script (${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js). Not anchor 5 because the 18-row actor table is substantial inline content that could live in a reference file, and the referenced script is not present in the bundle.	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 is clear and well-scoped to a specific domain, with good natural platform keywords, but it is missing the "when to use" trigger clause and uses only a vague action verb ("Understand"). Adding explicit usage triggers and concrete actions would raise both completeness and specificity.

Suggestions

Append an explicit trigger clause, e.g. "Use when the user asks about follower demographics, engagement patterns, or audience behavior on Facebook, Instagram, YouTube, or TikTok."

Replace the vague verb "Understand" with concrete actions such as "Extract follower demographics and engagement data via Apify Actors and summarize audience findings".

Add natural synonyms users would say, such as "followers", "social media audience", and "comment sentiment", to improve trigger-term coverage.

Dimension	Reasoning	Score
Specificity	The description names the domain ("audience demographics, preferences, behavior patterns, and engagement quality") and the four platforms, but the only action verb is the vague "Understand" — no concrete actions like extract, scrape, or summarize are stated. It sits at anchor 3: concrete domain, minimal actions; not 4 because no specific actions are listed.	3 / 5
Completeness	It clearly answers "what" (understand audience demographics/preferences/behavior/engagement across four platforms) but contains no "Use when..." or equivalent trigger clause, which caps completeness at 3 per the judging guidelines. Not 2 because the "what" is clear and specific.	3 / 5
Trigger Term Quality	Good natural keywords — "audience demographics", "engagement quality", and all four platform names (Facebook, Instagram, YouTube, TikTok) that users would naturally say. A few common terms are missing ("followers", "social media", "comments"), so it is not the comprehensive anchor 5 but clearly above anchor 3.	4 / 5
Distinctiveness Conflict Risk	The four named platforms plus demographics/engagement scoping give it a mostly distinct niche with only minor overlap risk against general social-media scraping or analytics skills. Not 5 because it lacks distinct trigger phrases that would fully separate it from sibling Apify skills.	4 / 5
	Total	14 / 20 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
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-audience-analysis/SKILL.md
Commit: c26b706

Reviewed: 2 hours ago

Table of Contents

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