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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.

75

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

100%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A tight, fully actionable SKILL.md body with executable commands, a clear CLI reference, and a real backing script, organized into clean sections without unnecessary explanation. It exemplifies token-efficient, copy-paste-ready skill content.

DimensionReasoningScore

Conciseness

The body is lean — executable Quick Start commands, a tight numbered 'How It Works', a CLI table, an output schema, and a one-line cost — with no padding or explanation of concepts Claude already knows, matching the 'every token earns its place' anchor.

3 / 3

Actionability

Provides fully executable copy-paste commands, a CLI reference table with defaults, and a concrete output schema; the referenced script (scripts/extract_commenters.py) exists and is complete, matching the 'copy-paste ready' anchor rather than the pseudocode score 2.

3 / 3

Workflow Clarity

The 'How It Works' section gives a clear 5-step sequence for a single-action CLI skill, which the simple-skill allowance lets score 3 when the action is unambiguous; it is not the score-2 case because the sequence and outputs are explicit.

3 / 3

Progressive Disclosure

Under 50 lines with well-organized sections and a single real bundle file referenced by an accurate path (verified to exist at scripts/extract_commenters.py), so per the under-50-lines allowance it scores 3 on organization alone.

3 / 3

Total

12

/

12

Passed

Description

85%

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 specific, third-person description that clearly states both what it does and when to use it, with low conflict risk. Its only real gap is trigger-term breadth — it covers the core terms but omits common phrasings a user might naturally say.

Suggestions

Broaden trigger terms to include natural variations like 'who commented on a LinkedIn post', 'people who commented', or 'LinkedIn comments' alongside 'commenters'.

Consider adding the data-source hint earlier (e.g. 'when you need to identify people engaging with LinkedIn content') to widen the surface of natural triggers.

DimensionReasoningScore

Specificity

Lists multiple concrete actions and outputs — 'Extract commenters from LinkedIn posts via Apify. Returns commenter names, titles, LinkedIn profile URLs, and comment text' — matching the comprehensive-anchor rather than the partial score 2.

3 / 3

Completeness

Explicitly answers both what ('Extract commenters... Returns...') and when ('Use to find warm leads engaging with relevant discussions'), matching the explicit-trigger anchor and not the score-2 'when only implied' case.

3 / 3

Trigger Term Quality

Has natural terms ('LinkedIn posts', 'commenters', 'warm leads') but misses common variations a user might say ('LinkedIn comments', 'who commented', 'people who commented'), so it lands at 'some relevant keywords but missing variations' rather than full coverage.

2 / 3

Distinctiveness Conflict Risk

A clear niche — LinkedIn comment extraction via Apify with distinct triggers — that is unlikely to trigger the wrong skill, fitting the 'clear niche with distinct triggers' anchor.

3 / 3

Total

11

/

12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
gooseworks-ai/goose-skills
Reviewed

Table of Contents

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