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remove-llm-comments

Remove unnecessary LLM-generated comments from code

65

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

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.

The body is a well-structured, concise, and fully actionable reference for a simple skill, with clear keep/remove criteria and a complete worked example.

DimensionReasoningScore

Conciseness

The body is lean: a two-sentence framing intro plus tight one-line rationales ('That's what git history is for.', 'The signature already tells you this.') and example-only sections, with every token earning its place and no padding of concepts Claude already knows.

3 / 3

Actionability

Concrete code snippets illustrate each removable category and a full before/after Rust example demonstrates the result, giving specific, copy-ready guidance appropriate for an instruction-only skill.

3 / 3

Workflow Clarity

This is a simple single-purpose skill whose single action is unambiguous; the explicit 'What to keep' list acts as a guardrail/checklist, satisfying the simple-skill allowance for a top score without a multi-step workflow.

3 / 3

Progressive Disclosure

It is a short, self-contained single file with no need for external references and well-organized sections (What to remove, What to keep, Example), meeting the under-50-line well-organized-sections criterion for a top score.

3 / 3

Total

12

/

12

Passed

Description

57%

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 concise and specific with a clear niche, but it lacks an explicit 'Use when...' trigger clause and broader trigger-term coverage, leaving it at mid-level completeness and trigger quality.

Suggestions

Add an explicit trigger clause, e.g. 'Use when cleaning up AI/LLM-generated comments or de-AI-ifying code before sharing or committing.'

Broaden trigger terms to include natural variations users might say, such as 'AI comments', 'cluttered comments', or 'remove boilerplate comments'.

Optionally enumerate a couple more concrete actions (e.g. 'strip narration and changelog comments while preserving why-comments and license headers') to lift specificity.

DimensionReasoningScore

Specificity

Phrases 'Remove unnecessary LLM-generated comments' and 'comments from code' name a concrete action and a specific domain, but only a single action is described rather than multiple distinct concrete actions, so it falls short of the level-3 anchor.

2 / 3

Completeness

It clearly states what the skill does ('Remove unnecessary LLM-generated comments from code') but provides no 'Use when...' clause or equivalent explicit trigger guidance, which per the rubric caps completeness at 2.

2 / 3

Trigger Term Quality

'LLM-generated comments' and 'comments from code' include the natural keyword 'comments' a user would say, but common variations like 'AI comments', 'clutter', or 'cleanup' are missing, matching the level-2 anchor.

2 / 3

Distinctiveness Conflict Risk

The 'LLM-generated' qualifier carves out a clear, distinct niche that is unlikely to trigger for unrelated skills, matching the level-3 anchor for a clear niche with distinct triggers.

3 / 3

Total

9

/

12

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

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

Total

15

/

16

Passed

Repository
gitlabhq/orbit-knowledge-graph
Reviewed

Table of Contents

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