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apify-competitor-intelligence

Analyze competitor strategies, content, pricing, ads, and market positioning across Google Maps, Booking.com, Facebook, Instagram, YouTube, and TikTok.

54

Quality

61%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/apify-competitor-intelligence/SKILL.md

The canonical home for this skill is apify-competitor-intelligence in sickn33/agentic-awesome-skills

SKILL.md
Quality
Evals
Security

Quality

Content

57%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 content is actionable with concrete commands and a clear step sequence, but it is weighed down by a large inline actor catalog and lacks a validation checkpoint in its batch workflow. Splitting the catalog into a reference file and adding a verify step would materially improve it.

Suggestions

Add an explicit validation/verification step to the workflow (e.g., check the run succeeded and the output file is non-empty before summarizing findings) so the batch operation is not capped at 3 for workflow_clarity.

Move the 30-row Actor catalog into a separate references file (e.g., references/actors.md) and keep SKILL.md as an overview pointing to it, improving progressive disclosure and conciseness.

Fill in the ACTOR_ID/JSON_INPUT/output placeholders with a worked example for at least one Actor so the commands are fully copy-paste ready.

DimensionReasoningScore

Conciseness

The body is mostly efficient with clear commands, but the 30-row Actor table repeats a "Competitor ..." prefix on every row and could be grouped by platform; it is useful reference data that could be tightened.

3 / 5

Actionability

Provides concrete, executable mcpc and run_actor.js commands covering the quick/CSV/JSON cases, with only minor gaps since ACTOR_ID, JSON_INPUT, and the output filename remain as un-exemplified placeholders.

4 / 5

Workflow Clarity

A clear 5-step sequence with a progress checklist is present, but this batch scraping workflow has no explicit validation checkpoint before summarizing findings; per the rubric, a batch operation without validation is capped at 3.

3 / 5

Progressive Disclosure

Section headers are clean and the run_actor.js reference is clearly signaled, but the skill is monolithic: a large inline Actor catalog that reads as reference data has no bundle files (references/scripts/assets) to offload it.

3 / 5

Total

13

/

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.

The description is specific and well-targeted with strong platform and analysis-term coverage, but it omits any explicit trigger guidance for when Claude should invoke the skill. Adding a "Use when..." clause would lift completeness and reduce overlap risk.

Suggestions

Append an explicit trigger clause, e.g. "Use when the user wants to research competitors, compare competitor pricing/ads/content, or benchmark rivals across Google Maps, Booking, Facebook, Instagram, YouTube, or TikTok."

Vary the verbs beyond "Analyze" (e.g., "Extract, compare, and benchmark competitor strategies...") to convey multiple distinct actions.

Add natural synonyms users say ("competitor research", "competitive analysis", "benchmarking") to improve trigger-term coverage.

DimensionReasoningScore

Specificity

Lists several concrete analysis facets ("strategies, content, pricing, ads, and market positioning") across seven named platforms, but relies on a single generic verb ("Analyze") rather than multiple distinct actions, so it falls just below the comprehensive anchor.

4 / 5

Completeness

Clearly states what the skill does but provides no "Use when..." clause or equivalent trigger guidance; per the rubric guideline, a missing explicit trigger caps completeness at 3.

3 / 5

Trigger Term Quality

Includes natural user terms ("competitor strategies", "pricing", "ads") and concrete platform names users would say, but misses common synonyms such as "competitor research", "competitive analysis", or "benchmarking".

4 / 5

Distinctiveness Conflict Risk

The competitor-intelligence-via-Apify niche across named platforms is mostly distinct with minimal conflict risk, though it could overlap with broader marketing/market-research skills.

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.

Validation15 / 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
Reviewed

Table of Contents

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