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apify-market-research

Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.

60

Quality

70%

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-market-research/SKILL.md
SKILL.md
Quality
Evals
Security

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.

A well-structured, executable workflow document: concrete commands, a tracked checklist, and an error-recovery table make it immediately actionable. The main gaps are the absence of a concrete example input for the run script and no explicit output-validation checkpoint before summarization.

Suggestions

Add one concrete example of a filled-in run command, e.g. --actor "compass/google-maps-extractor" --input '{"searchStringsArray":["coffee shop"],"locationQuery":"Berlin","maxCrawledPlaces":50}', so JSON_INPUT is not an unresolved placeholder.

Insert a validation checkpoint between Step 4 and Step 5, such as checking the output row count or file contents and retrying with adjusted input if empty, to close the workflow's feedback loop.

Tighten small padding items — the '(No need to check it upfront)' parenthetical and the vague 'Based on character of use case' guidance — to reach full token efficiency.

DimensionReasoningScore

Conciseness

The body is lean and command-driven with a dense actor-selection table and no explanations of concepts Claude already knows. Minor instances could be trimmed, such as the parenthetical '(No need to check it upfront)' in Prerequisites and the vague 'Number of results: Based on character of use case', so it does not reach the every-token-earns-its-place anchor of 5.

4 / 5

Actionability

Concrete, executable commands are given throughout — the mcpc schema-fetch call and the three run_actor.js invocations with exact flags. It falls short of fully copy-paste ready because 'JSON_INPUT' is a placeholder that is never instantiated with a concrete example payload, leaving a minor gap in covering the most common case.

4 / 5

Workflow Clarity

A five-step sequence with a copyable progress checklist and an Error Handling section mapping failures to fixes provides clear sequencing and error-recovery guidance. It misses a 5 because there is no explicit validation checkpoint on the output itself (e.g., verify result count or file contents) before summarizing in Step 5.

4 / 5

Progressive Disclosure

The body is well sectioned (When to Use, Workflow, Error Handling, Limitations) and the only external dependency, the run_actor.js script, is referenced one level deep and clearly signaled inside the commands. It does not reach 5 because the ~125-line body inlines the full 16-actor catalog and error handling without any navigational signposting to separate materials, and the referenced script lives outside the bundle.

4 / 5

Total

16

/

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 specific, platform-anchored description that clearly states what the skill does, but it omits any 'when to use' trigger guidance, which caps completeness and weakens trigger coverage. Adding a 'Use when...' clause with natural synonyms would lift the two weakest dimensions.

Suggestions

Append an explicit trigger clause, e.g. 'Use when the user needs market research, market sizing, competitor pricing, or regional demand data from Google Maps, Facebook, Instagram, Booking.com, or TripAdvisor.'

Include common user phrasings as synonyms such as 'market research', 'market analysis', 'competitor research', and 'location/scraping data' to improve trigger term coverage.

Vary the verbs beyond 'Analyze' (e.g. 'extract', 'compare', 'validate') to describe the distinct concrete actions the skill performs.

DimensionReasoningScore

Specificity

The description lists several concrete analytical objects ('market conditions, geographic opportunities, pricing, consumer behavior, and product validation') plus five named platforms, matching the 'several specific actions; minor gaps' anchor. It stops short of a 5 because everything hangs on the single verb 'Analyze' rather than multiple distinct concrete actions.

4 / 5

Completeness

The 'what' is clear (analyze market conditions, pricing, consumer behavior, etc. across named platforms), but there is no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines. It is not a 2 because the 'what' is concrete and multi-faceted rather than vague.

3 / 5

Trigger Term Quality

Natural terms like 'market conditions', 'pricing', 'consumer behavior', and platform names (Google Maps, Facebook, Instagram, Booking.com, TripAdvisor) give good keyword coverage. It misses common variations users would say such as 'market research', 'competitor analysis', or 'trends', keeping it below the comprehensive-synonym anchor of 5.

4 / 5

Distinctiveness Conflict Risk

Naming five specific platforms carves out a clear niche with minimal conflict risk against unrelated skills. It is not a 5 because the broad market-research framing ('market conditions', 'consumer behavior') could overlap with other research or analysis skills given the absence of explicit trigger phrases.

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
Reviewed

Table of Contents

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