apify-audience-analysis
Understand audience demographics, preferences, behavior patterns, and engagement quality across Facebook, Instagram, YouTube, and TikTok.
46
48%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/apify-audience-analysis/SKILL.mdQuality
Discovery
32%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 identifies a clear domain (social media audience analysis) and names specific platforms, which helps with identification. However, it lacks concrete actions (listing categories rather than specific capabilities), omits a 'Use when...' clause entirely, and uses somewhat generic analytical language that could overlap with other social media or marketing skills.
Suggestions
Add an explicit 'Use when...' clause with trigger terms like 'audience insights,' 'follower demographics,' 'social media analytics,' 'who is my audience,' or 'engagement analysis.'
Replace abstract categories with concrete actions, e.g., 'Generates demographic breakdowns, segments audiences by behavior, analyzes engagement quality metrics, and compares follower profiles across Facebook, Instagram, YouTube, and TikTok.'
Include common user phrasing variations such as 'social media audience,' 'follower insights,' 'audience segmentation,' and 'platform analytics' to improve trigger term coverage.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | It names the domain (social media audience analysis) and mentions some aspects like 'demographics, preferences, behavior patterns, and engagement quality,' but these are more like categories than concrete actions. It doesn't list specific actions like 'generate demographic reports,' 'segment audiences,' or 'track engagement metrics.' | 2 / 3 |
Completeness | It describes what the skill does (understand audience data across platforms) but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per the rubric, a missing 'Use when...' clause caps completeness at 2, and since the 'what' is also somewhat vague (no concrete actions), this scores a 1. | 1 / 3 |
Trigger Term Quality | Includes relevant platform names (Facebook, Instagram, YouTube, TikTok) and terms like 'audience demographics' and 'engagement,' which users might naturally say. However, it's missing common variations like 'social media analytics,' 'follower insights,' 'audience analysis,' or 'social metrics.' | 2 / 3 |
Distinctiveness Conflict Risk | The mention of specific platforms (Facebook, Instagram, YouTube, TikTok) and audience-focused analysis provides some distinctiveness, but it could overlap with general social media analytics skills, content performance skills, or marketing analytics skills due to the broad framing. | 2 / 3 |
Total | 7 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a functional audience analysis skill with strong actionability—concrete commands, specific Actor IDs, and clear output format options. Its main weaknesses are the lack of validation checkpoints in the workflow (no verification after schema fetch or before actor execution) and moderate verbosity in the Actor table and boilerplate sections. The skill would benefit from integrating error handling into workflow steps and extracting the large lookup table to a reference file.
Suggestions
Add validation checkpoints after Step 2 (verify schema was fetched successfully and contains expected fields) and after Step 4 (verify output file exists and contains expected data structure).
Extract the 18-row Actor lookup table to a separate reference file (e.g., ACTORS.md) and keep only a few representative examples inline.
Remove or significantly trim the generic 'Limitations' section—these are general agent guidelines Claude already knows, not skill-specific constraints.
Integrate error handling into the relevant workflow steps rather than listing them separately at the end.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably structured but includes some unnecessary verbosity. The large Actor table (18 rows) could be more concise, and the 'Limitations' section contains generic boilerplate that doesn't add value. The 'Best For' column in the table largely repeats the 'User Need' column with abbreviations. | 2 / 3 |
Actionability | The skill provides fully executable bash commands for fetching schemas and running actors, with concrete examples for each output format (quick answer, CSV, JSON). The Actor selection table gives specific Actor IDs, and the mcpc command is copy-paste ready with clear placeholder substitution. | 3 / 3 |
Workflow Clarity | The workflow has a clear 5-step sequence with a progress checklist, but lacks validation checkpoints. There's no step to verify the Actor schema was fetched correctly, no validation of the JSON input before running, and no feedback loop for handling partial results or retrying failed runs. The error handling section is separate rather than integrated into the workflow steps. | 2 / 3 |
Progressive Disclosure | The skill references external scripts (e.g., `${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js`) but no bundle files were provided to verify these exist. The large Actor lookup table could be extracted to a reference file. The content is reasonably organized with sections but is somewhat monolithic for its length (~120 lines of substantive content). | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- sickn33/antigravity-awesome-skills
- Commit
96e6849
- Reviewed
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