apify-market-research
Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
72
66%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./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 specific platforms, creating a distinctive niche. However, it lacks an explicit 'Use when...' clause, which is a significant gap for skill selection. Some trigger terms could be more natural to how users would phrase requests.
Suggestions
Add a 'Use when...' clause such as 'Use when the user asks about market research, competitor analysis, review analysis, or business intelligence on Google Maps, Facebook, Instagram, Booking.com, or TripAdvisor.'
Include more natural user trigger terms like 'market research', 'competitor analysis', 'reviews', 'ratings', 'local business analysis', or 'social media insights' that users would commonly say.
| 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 there is no explicit 'Use when...' clause or equivalent trigger guidance, which caps this at 2 per the rubric guidelines. | 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 missing common user phrases like 'market research', 'competitor analysis', 'reviews', or 'scrape'. Users might not naturally say 'product validation' or 'geographic opportunities'. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of market analysis specifically across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor creates a clear niche that is unlikely to conflict with other skills. The platform-specific focus makes it highly distinguishable. | 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 solid, actionable skill with clear executable commands and a well-structured workflow. Its main weaknesses are the lack of validation/feedback loops in the workflow (e.g., checking if the Actor run succeeded before summarizing) and the inline Actor table that inflates the token footprint. The error handling section is useful but would be more effective if integrated as checkpoints within the workflow steps.
Suggestions
Add a validation checkpoint after Step 4 (e.g., check exit code or output before proceeding to Step 5) and include a retry/fix loop for failed runs.
Move the Actor selection table to a separate reference file (e.g., ACTORS.md) and link to it from Step 1 to reduce token footprint.
Integrate error handling into the workflow steps rather than listing them separately at the end, so Claude knows when to expect and handle each error type.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient but includes some unnecessary bulk. The large Actor selection table is useful reference material but could be in a separate file. The three nearly identical command examples for CSV/JSON/quick answer add redundancy. | 2 / 3 |
Actionability | Provides fully executable bash commands with clear placeholders, concrete Actor IDs, specific CLI flags, and exact tool invocations. The mcpc command for fetching schemas is copy-paste ready with only the Actor ID needing substitution. | 3 / 3 |
Workflow Clarity | The 5-step workflow is clearly sequenced with a progress checklist, but lacks validation checkpoints. There's no step to verify the Actor ran successfully before summarizing, no feedback loop for retrying failed runs, and the error handling section is separate rather than integrated into the workflow. | 2 / 3 |
Progressive Disclosure | References external scripts (`run_actor.js`) appropriately, but the large Actor selection table (16 rows) could be split into a separate reference file. The skill is somewhat monolithic with all content inline rather than using well-signaled references for the detailed Actor catalog. | 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
7241463
- Reviewed
Table of Contents
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