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linkedin-commenter-extractor

Extract commenters from LinkedIn posts via Apify. Returns commenter names, titles, LinkedIn profile URLs, and comment text. Use to find warm leads engaging with relevant discussions. No LinkedIn cookies required.

70

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

92%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.

An exemplary skill body for a simple tool: copy-paste-ready commands, accurate CLI documentation, a concrete output schema, and clean section organization. The only flaw is minor repetition of the no-cookies claim across three sections.

DimensionReasoningScore

Conciseness

The body is lean — commands, a CLI table, an output schema, and a cost note with no explanations of concepts Claude already knows. Not 5 because "no LinkedIn cookies required" is stated three times (intro, How It Works, Cost) and the actor name is repeated, which is minor padding that could be trimmed.

4 / 5

Actionability

Quick Start gives fully executable copy-paste commands covering single post, multiple posts, max-comments, all three output formats, and dedup; the CLI table and output schema are concrete and match the actual script's flags. Fully executable and covers the common cases.

5 / 5

Workflow Clarity

This is a simple single-purpose skill and the single action (run the script with --post-url) is unambiguous, with a clear 5-step How It Works sequence. The operation is read-only extraction, so the destructive/batch validation cap does not apply.

5 / 5

Progressive Disclosure

Well-organized sections with the one bundle file (scripts/extract_commenters.py) correctly referenced and verified to exist with matching flags; at this size no external reference files are needed, satisfying the simple-skill exception.

5 / 5

Total

19

/

20

Passed

Description

78%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 strong description: specific about what it returns, third person, concise, and equipped with an explicit use clause. The main room for improvement is converting the purpose-style "Use to find warm leads" into concrete trigger phrases and covering a few more natural synonyms.

Suggestions

Rewrite the when-clause as explicit trigger phrases, e.g., "Use when the user wants to see who commented on LinkedIn posts, scrape LinkedIn commenters, or find warm leads among people engaging with a post."

Add missing natural synonyms such as "scrape" or "people who commented" to broaden trigger-term coverage.

Mention the company/title parsing and output formats (JSON/CSV/summary) in the description to close the specificity gap with the body's actual capabilities.

DimensionReasoningScore

Specificity

"Extract commenters from LinkedIn posts via Apify. Returns commenter names, titles, LinkedIn profile URLs, and comment text" lists several concrete outputs and a specific mechanism, but coverage has minor gaps (e.g., company field and output formats are unmentioned). Fits anchor 4 better than 5 because it centers on one extraction action with its returned fields rather than comprehensive multi-action coverage.

4 / 5

Completeness

"Use to find warm leads engaging with relevant discussions" provides an explicit trigger clause and the what is clear, so it clears the 3-cap for missing use guidance. Not 5 because the when is framed as purpose rather than concrete trigger phrases (e.g., "when the user asks who commented on a LinkedIn post").

4 / 5

Trigger Term Quality

"LinkedIn posts", "commenters", and "warm leads" are natural phrases users would say, giving good keyword coverage. Not anchor 5 because common variations like "scrape LinkedIn comments" or "people who commented on" are missing.

4 / 5

Distinctiveness Conflict Risk

The combination of "LinkedIn posts", "commenters", and "via Apify" carves out a clear niche with distinct triggers and minimal overlap risk with other skills.

5 / 5

Total

17

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
gooseworks-ai/goose-skills
Reviewed

Table of Contents

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