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linkedin-analytics

Use when someone wants to understand their own LinkedIn numbers — which posts worked, why reach dropped, whether a pattern is real, or how to test a hypothesis. Triggers on "why did my reach drop", "what's working on my LinkedIn", "analyze my posts", "do carousels do better for me", "should I test this", "LinkedIn analytics". Reads your own exported post data, reports medians and outlier bands, tests candidate patterns against a permutation null, and sizes a real experiment — refusing to conclude anything below 10 posts.

76

Quality

94%

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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.

A strong, opinionated analytical skill: executable commands with exit-code validation gates, a clear multi-step workflow with refusal floors, and well-organized one-level-deep references. Only minor editorial rhetoric keeps conciseness from a 5.

DimensionReasoningScore

Conciseness

Mostly lean with no padding of concepts Claude already knows, and the heavy-tailed-distribution framing earns its place, but rhetorical color ('the least informative event available', 'that is the honest answer') could be trimmed, keeping it just below a 5.

4 / 5

Actionability

Each workflow step gives a copy-paste-ready command with concrete flags (e.g. 'python3 scripts/pattern_miner.py --input posts.json --output human') plus explicit exit-code semantics covering the common cases.

5 / 5

Workflow Clarity

A clear four-step sequence (export → describe → test → experiment) with explicit validation checkpoints via exit codes 0/2/3 and a refusal floor below 10 posts, giving feedback loops for the batch statistical operation.

5 / 5

Progressive Disclosure

Well-sectioned overview (Workflow, Rules, Scripts table, References and assets, Distinct from) with one-level-deep references that all resolve to real bundle files, each annotated with a one-line description for easy navigation.

5 / 5

Total

19

/

20

Passed

Description

96%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.

An exemplary description: third-person, specific, with explicit 'Use when'/'Triggers on' guidance and concrete actions. The only soft spot is one generic trigger ('should I test this') that lightly overlaps experiment-design skills.

DimensionReasoningScore

Specificity

Third-person voice lists multiple concrete actions — 'reports medians and outlier bands, tests candidate patterns against a permutation null, and sizes a real experiment — refusing to conclude anything below 10 posts' — with comprehensive coverage and no vague fluff.

5 / 5

Completeness

Explicitly answers both 'what' (reads export, reports medians/bands, tests patterns, sizes experiment, refuses under 10 posts) and 'when' ('Use when...' plus a 'Triggers on...' clause with concrete phrases).

5 / 5

Trigger Term Quality

Comprehensive natural trigger phrases a user would actually say, including synonyms: 'why did my reach drop', 'what's working on my LinkedIn', 'analyze my posts', 'do carousels do better for me', 'should I test this', 'LinkedIn analytics'.

5 / 5

Distinctiveness Conflict Risk

A clear, tightly-scoped LinkedIn-own-data niche with method-specific triggers, but 'should I test this' is generic and creates minor overlap risk with general experiment-design skills, so it sits just below a 5.

4 / 5

Total

19

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
alirezarezvani/claude-skills
Reviewed

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