apify-market-research

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

Quality

65%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

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

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.

The body is highly actionable with concrete, executable commands and a clean Actor-selection table, and it is reasonably concise and well-structured. Its main weakness is workflow clarity: batch scraping operations run without any validation/verification checkpoint, which the rubric caps at 3.

Suggestions

Add a validation checkpoint in the workflow, e.g. inspect the output row count / file integrity before reporting findings, to satisfy the batch-operation feedback-loop requirement.

Trim minor padding such as the '(No need to check it upfront)' note and the 'This returns:' bullets, or fold them into a single line.

Make the referenced bundle path explicit (confirm reference/scripts/run_actor.js exists and link it) so progressive disclosure navigation is unambiguous.

Dimension	Reasoning	Score
Conciseness	The body is mostly efficient — a compact Actor-selection table, terse bash snippets, and minimal prose — with only minor over-explanation (e.g., the 'This returns:' bullets and the parenthetical '(No need to check it upfront)') that could be trimmed, fitting 'efficient; minor instances of over-explanation'.	4 / 5
Actionability	It provides copy-paste-ready, concrete executable commands (mcpc fetch-actor-details invocation, the run_actor.js calls with --actor/--input/--output/--format flags) and an Actor-selection table covering the common cases, matching 'fully executable; copy-paste ready code or commands; specific examples cover the common cases'.	5 / 5
Workflow Clarity	A clear 5-step checklist sequence is present, but the workflow involves batch scraping operations with no validation/verification checkpoint before reporting results; per the rubric's feedback-loop rule for batch operations, workflow_clarity is capped at 3.	3 / 5
Progressive Disclosure	Content is well-organized into clear sections with the bulk detail (per-Actor schemas) delegated to a referenced script (reference/scripts/run_actor.js) rather than inlined; references are mostly clear though the reference/ path convention is only implied via ${CLAUDE_PLUGIN_ROOT}, leaving minor organization gaps versus the score-5 anchor.	4 / 5
	Total	16 / 20 Passed

Description

56%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.

The description is specific and domain-distinct, naming concrete platforms and research dimensions, but it omits an explicit 'Use when...' trigger clause, which caps its completeness and weakens trigger-term quality. Adding a trigger phrase would lift the two lower dimensions.

Suggestions

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

Include common natural phrasings users say ('market research', 'competitive analysis', 'local business density') to strengthen trigger terms.

Keep the third-person voice (already correct) and avoid adding first/second person phrasing.

Dimension	Reasoning	Score
Specificity	Names the domain and lists several specific actions ('Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation') across concrete platforms, but the actions are somewhat broad categories rather than discrete operations, leaving minor coverage gaps versus the score-5 anchor.	4 / 5
Completeness	It clearly answers 'what' (analyze market conditions across named platforms) but provides no explicit 'when/Use when' guidance; the judging guidelines cap completeness at 3 for a missing 'Use when...' clause, which applies here.	3 / 5
Trigger Term Quality	It includes relevant terms users might say ('market conditions', 'pricing', 'consumer behavior', 'Google Maps', 'Facebook', 'Instagram'), but it lacks a 'Use when...' trigger clause and misses common phrasings like 'market research' as an explicit trigger, keeping it at the 'some relevant keywords but missing variations' anchor.	3 / 5
Distinctiveness Conflict Risk	The named-platform scope (Google Maps, Facebook, Instagram, Booking.com, TripAdvisor via Apify) carves out a fairly distinct niche with minor overlap risk against generic scraping or analytics skills, fitting 'mostly distinct; minor overlap risk'.	4 / 5
	Total	14 / 20 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: sickn33/antigravity-awesome-skills
Path: skills/apify-market-research/SKILL.md
Commit: 1f67c44

Reviewed: about 16 hours ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.