apify-market-research
Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
57
66%
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 ./plugins/antigravity-awesome-skills-claude/skills/apify-market-research/SKILL.mdQuality
Discovery
67%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 does well at specifying concrete analytical capabilities and naming the exact platforms involved, creating a distinctive niche. However, it lacks an explicit 'Use when...' clause, which limits Claude's ability to know when to select this skill, and could benefit from more natural trigger terms that users would actually say when requesting this type of analysis.
Suggestions
Add a 'Use when...' clause with trigger terms like 'market research', 'competitor analysis', 'review analysis', 'local business analysis', or 'travel market data'.
Include common user-facing variations such as 'scrape reviews', 'analyze competitors', 'market opportunity', or 'location-based research' to improve trigger term coverage.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation' across named platforms. | 3 / 3 |
Completeness | Clearly answers 'what does this do' with specific analysis capabilities across named platforms, but lacks an explicit 'Use when...' clause or equivalent trigger guidance for when Claude should select this skill. | 2 / 3 |
Trigger Term Quality | Includes relevant platform names (Google Maps, Facebook, Instagram, Booking.com, TripAdvisor) and domain terms (market conditions, pricing, consumer behavior), but lacks common user-facing trigger variations like 'market research', 'competitor analysis', 'reviews', or 'scrape'. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of market analysis with specific platforms (Google Maps, Booking.com, TripAdvisor, Facebook, Instagram) creates a clear niche that is unlikely to conflict with other skills. | 3 / 3 |
Total | 10 / 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 market research skill with strong actionability—concrete commands, a useful Actor lookup table, and clear error handling. Its main weaknesses are the lack of validation/feedback loops in the workflow (e.g., verifying Actor output before summarizing) and some verbosity in boilerplate sections. The progressive disclosure could be improved by offloading the Actor table to a reference file and confirming bundle file availability.
Suggestions
Add a validation checkpoint between Step 4 and Step 5, e.g., 'Verify the output file exists and contains expected fields before summarizing' to create a feedback loop for failed or partial runs.
Move the Actor selection table to a separate reference file (e.g., ACTORS.md) and link to it from the main skill to reduce SKILL.md length and improve progressive disclosure.
Remove the generic Limitations section boilerplate ('Do not treat the output as a substitute for environment-specific validation...') which doesn't add skill-specific value and wastes tokens.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some unnecessary content. The large Actor table is useful but could be more compact. The limitations section contains generic boilerplate ('Do not treat the output as a substitute for environment-specific validation...') that doesn't add value. The 'When to Use' section partially restates the description. | 2 / 3 |
Actionability | Provides fully executable bash commands for every step, concrete Actor IDs in a lookup table, specific CLI flags and options, and clear error handling with resolutions. Commands are copy-paste ready with clear placeholder substitution patterns. | 3 / 3 |
Workflow Clarity | The workflow has a clear 5-step sequence with a progress checklist, but lacks validation checkpoints. After running the Actor (Step 4), there's no verification step to check if results are valid or complete before summarizing. The error handling section is separate rather than integrated into the workflow as feedback loops. | 2 / 3 |
Progressive Disclosure | The skill references scripts at `${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js` but no bundle files are provided to verify these exist. The content is somewhat monolithic—the large Actor selection table could be in a separate reference file. However, the section structure is logical and navigable. | 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
22710e9
- Reviewed
Table of Contents
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