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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.

56

Quality

63%

Does it follow best practices?

Run evals on this skill

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/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 skill provides concrete, executable guidance through a clear five-step workflow, but it inlines a large reference table that belongs in a separate file and lacks validation checkpoints for its batch scraping operations.

Suggestions

Move the 27-row actor-selection table into a references file (e.g. actors.md) and link to it from Step 1 to improve progressive disclosure and token efficiency.

Add an explicit validation checkpoint between fetching the actor schema (Step 2) and running it (Step 4), e.g. confirm required input parameters are present before execution.

Trim minor filler ("Copy this checklist and track progress:", "(No need to check it upfront)") and verify the referenced run_actor.js script exists in the bundle.

DimensionReasoningScore

Conciseness

The body is mostly efficient with executable commands and a useful actor-selection table, but the 27-row actor table is bulk reference material that would be more token-efficient in a separate file, and minor padding ("Copy this checklist and track progress:", "(No need to check it upfront)") could be trimmed.

3 / 5

Actionability

Steps 2 and 4 give concrete, copy-paste-ready bash commands with real actor IDs and flag combinations, with only minor gaps such as the referenced run_actor.js script not being present in the bundle.

4 / 5

Workflow Clarity

A clear five-step sequence with a progress checklist and an error-handling section is present, but running Apify scrapers is a batch operation with no explicit validation/verification checkpoint before or after the run, which caps workflow clarity at 3 per the rubric.

3 / 5

Progressive Disclosure

The body is well-sectioned but essentially monolithic: the large actor-selection table and script invocations are inlined rather than split into reference files, and there are no one-level-deep references to navigate to.

3 / 5

Total

13

/

20

Passed

Description

70%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 clearly communicates a niche capability with concrete platforms and analysis dimensions, but it omits an explicit "Use when..." trigger clause, which limits completeness. Specificity and distinctiveness are strong.

Suggestions

Add an explicit trigger clause, e.g. "Use when analyzing competitors across Google Maps, Booking.com, Facebook, Instagram, YouTube, or TikTok."

Replace the single verb "Analyze" with a few distinct concrete verbs (e.g. "Extract, compare, and benchmark competitor...") to raise specificity.

Include natural synonyms users might say (e.g. "competitive analysis", "rival research", "ad spying") to broaden trigger coverage.

DimensionReasoningScore

Specificity

The description lists several concrete analysis targets ("strategies, content, pricing, ads, and market positioning") across a named set of platforms, giving comprehensive coverage, though all hang off the single verb "Analyze" rather than multiple distinct verbs.

4 / 5

Completeness

The "what" is clearly stated (analyze competitor strategies/content/pricing/ads/positioning across platforms), but there is no "Use when..." clause or equivalent trigger guidance, which caps completeness at 3 per the rubric guidelines.

3 / 5

Trigger Term Quality

Natural user-facing terms are present ("competitor", "pricing", "ads", plus platform names Google Maps, Booking.com, Facebook, Instagram, YouTube, TikTok), but common synonyms and variations are missing and there are no explicit trigger phrases.

4 / 5

Distinctiveness Conflict Risk

The combination of competitor intelligence plus an explicit list of named platforms defines a clear niche with distinct triggers and minimal overlap risk with other skills.

5 / 5

Total

16

/

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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