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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.

57

Quality

66%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./skills/apify-market-research/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

64%Scale 1-3

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This skill provides solid actionable guidance with executable commands and a well-structured Actor selection table, making it practical for market research tasks. Its main weaknesses are the lack of validation checkpoints in the workflow (e.g., verifying schema fetch results or validating inputs before running actors) and some verbosity in the Actor table and boilerplate limitations section. The absence of bundle files makes it impossible to verify referenced script paths.

Suggestions

Add a validation step after fetching the Actor schema (Step 2) to verify the response contains expected fields before proceeding, and add input validation before running the actor in Step 4.

Move the Actor lookup table to a separate reference file (e.g., ACTORS.md) and keep only a brief summary in SKILL.md to improve progressive disclosure and reduce inline bulk.

Remove the generic Limitations section or replace it with skill-specific constraints (e.g., rate limits, data freshness caveats, platform-specific quirks).

DimensionReasoningScore

Conciseness

The skill is reasonably efficient but includes some unnecessary elements like the large Actor table that could be more compact, and the Limitations section contains generic boilerplate that doesn't add value. The error handling section is appropriately concise.

2 / 3

Actionability

Provides fully executable bash commands for each step, concrete Actor IDs in a lookup table, specific CLI flags and options, and clear input/output patterns. The commands are 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 or unexpected results.

2 / 3

Progressive Disclosure

The skill references `${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js` but no bundle files are provided to verify this exists. The content is somewhat monolithic with the large Actor table inline rather than in a reference file. However, the section structure is logical and navigable.

2 / 3

Total

9

/

12

Passed

Description

67%Scale 1-3

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 a strong job listing specific capabilities and naming the exact platforms it covers, creating a distinctive and concrete skill profile. However, it lacks an explicit 'Use when...' clause, which limits Claude's ability to know when to select this skill, and the trigger terms could better reflect how users naturally phrase requests (e.g., 'competitor analysis', 'market research', 'review analysis').

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user asks about market research, competitor analysis, review analysis, or location-based business intelligence on Google Maps, Facebook, Instagram, Booking.com, or TripAdvisor.'

Include natural user-facing trigger terms such as 'market research', 'competitor analysis', 'review scraping', 'location intelligence', and 'social media analysis' to improve keyword coverage.

DimensionReasoningScore

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 'competitor analysis', 'market research', 'reviews', or 'scrape'.

2 / 3

Distinctiveness Conflict Risk

The combination of specific analysis types (market conditions, pricing, consumer behavior, product validation) with the explicit list of five named platforms creates a clear niche that is unlikely to conflict with other skills.

3 / 3

Total

10

/

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

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
popey/claude-code-skills
Reviewed

Table of Contents

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