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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.

Quality

73%

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

The canonical home for this skill is apify-market-research in sickn33/agentic-awesome-skills

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 solid, action-oriented skill body: executable commands, a clear sequenced workflow with a progress checklist, and genuine error-recovery guidance. The main gaps are minor — unexemplified input JSON, an unverifiable script reference, triplicated command blocks, and no explicit output-verification step for batch runs.

Suggestions

Consolidate the three near-identical run commands into one block with documented optional flags (--output, --format) to save tokens.

Add one concrete --input example (e.g. a small JSON payload for compass/crawler-google-places) so the JSON_INPUT placeholder is unambiguous.

Add an explicit verification step in Step 5: confirm the output file exists and inspect a sample of rows before summarizing findings.

DimensionReasoningScore

Conciseness

The body is lean — an actor-selection table, copy-paste commands, and terse error handling — with no explanations of concepts Claude already knows. Minor trimming is possible: the three near-duplicate run-command blocks (Quick/CSV/JSON) differ only in flags and could be consolidated, and the 'Best For' column largely echoes the 'User Need' column. This fits anchor 4 ('efficient; minor instances that could be trimmed') rather than 5.

4 / 5

Actionability

Commands are concrete and executable with real flags (mcpc tools-call with auth header, node --env-file with --actor/--input/--output/--format), and Step 2's dynamic schema fetch is a smart executable pattern. It stops short of anchor 5 because 'JSON_INPUT' is never exemplified with a real payload, and the referenced script '${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js' is not present in the skill bundle.

4 / 5

Workflow Clarity

A clear 5-step sequence with a copyable progress checklist, and the Error Handling section provides explicit failure-to-remedy feedback loops (e.g. 'Run FAILED' - check Apify console link; 'Timeout' - reduce input or increase --timeout). Minor validation gap keeps it at anchor 4: for batch extraction runs there is no explicit output-verification step before reporting (e.g. confirm the file exists and inspect sample rows).

4 / 5

Progressive Disclosure

Well-organized single-file structure (Prerequisites, 5-step Workflow, Error Handling) with the 16-row actor table appropriately inline as core selection logic. The single script reference is one level deep and clearly signaled, but it points to a plugin-root path with no scripts/ directory in the bundle, so navigation cannot be verified in-bundle — matching anchor 4's 'minor organization gaps' rather than 5.

4 / 5

Total

16

/

20

Passed

Description

61%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 moderately good description: the 'what' is concrete thanks to named platforms and research categories, but the single generic verb 'Analyze' limits specificity, and the absence of any 'Use when...' trigger clause caps completeness. Adding explicit trigger guidance and action synonyms (scrape, export, competitor analysis) would lift it substantially.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user asks for market research, competitor pricing, location analysis, or product validation on Google Maps, Facebook, Instagram, Booking.com, or TripAdvisor.'

Replace the single generic verb with concrete actions: 'Scrape and analyze market data... export results to CSV or JSON via Apify Actors' to cover the full capability set.

Include natural trigger synonyms such as 'market research', 'competitor analysis', and 'scrape reviews' that users would actually say.

DimensionReasoningScore

Specificity

The description offers one generic action verb ("Analyze") applied across concrete research objects ("market conditions, geographic opportunities, pricing, consumer behavior, and product validation") and platforms, but names no extraction, scraping, or export mechanics. This matches anchor 3 ('names domain and 1-2 concrete actions, but not comprehensive') — not 4, which requires several distinct specific actions, and not 2, since the analysis objects and platform list are concrete rather than minimal.

3 / 5

Completeness

The 'what' is clear (analyze market conditions, pricing, consumer behavior across five platforms), but there is no 'Use when...' clause or equivalent explicit trigger guidance anywhere, so completeness is capped at 3 per the judging guideline. Not 4 because the 'when' is entirely absent rather than merely improvable.

3 / 5

Trigger Term Quality

Platform names ("Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor") are strong natural triggers alongside research phrases like "pricing" and "consumer behavior". It falls short of anchor 5 because common synonyms are missing — notably "market research", "scrape", "competitor analysis", and "reviews" — but coverage exceeds anchor 3's 'some relevant keywords'.

4 / 5

Distinctiveness Conflict Risk

The multi-platform market-research framing ("across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor") carves out a mostly distinct niche with minor overlap risk against single-platform scraping skills. Not 5 because the broad categories ('market conditions', 'pricing', 'consumer behavior') overlap generic analytics skills and individual platform names may collide with dedicated Instagram/Maps skills.

4 / 5

Total

14

/

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

administrakt0r/AI-Agents-Safe-Coding-Skills

Path

skills/apify-market-research/SKILL.md

Reviewed

Table of Contents

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