ai-slop-cleaner

Clean AI-generated code slop with a regression-safe, deletion-first workflow and optional reviewer-only mode

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

77%

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

The content is highly actionable with a clearly sequenced, validation-backed workflow and concrete examples, but it has minor redundancy between the posture and workflow sections and keeps sizable reference-grade material inline rather than splitting it out.

Suggestions

Consolidate the 'OMC Execution Posture' bullets into the Workflow/When-to-Use sections to remove overlap and tighten the body.

Move the UI/Design Reviewer Checklist and smell classification taxonomy into a separate reference file referenced one level deep, keeping SKILL.md as an overview.

Dimension	Reasoning	Score
Conciseness	The body is largely lean and assumes Claude's competence, but the 'OMC Execution Posture' section overlaps with the Workflow and When-to-Use sections, leaving real tightening opportunity.	2 / 3
Actionability	Highly concrete guidance throughout: explicit commands, numbered smell-classification passes, good/bad fit examples, and a UI checklist with specific px thresholds and named color values.	3 / 3
Workflow Clarity	The 6-step Workflow has an explicit sequence with validation checkpoints ('Re-run targeted verification after each pass', 'Run the quality gates') and a fix-or-back-out feedback loop, plus a review-mode checklist.	3 / 3
Progressive Disclosure	Well-organized into clear sections, but it is a single monolithic file over 50 lines with sizable inline content (the UI/Design checklist and smell taxonomy) that could be offloaded to reference files.	2 / 3
	Total	10 / 12 Passed

Description

67%

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 specific and distinctive with a clear niche, but it omits an explicit use-when trigger clause and lacks the broader natural trigger-term coverage (deslop, anti-slop) that the body reveals.

Suggestions

Add an explicit 'Use when...' clause naming concrete triggers (e.g., 'Use when the user says deslop, anti-slop, or asks to clean up noisy/repetitive AI-generated code').

Include the natural one-word trigger terms 'deslop' and 'anti-slop' alongside 'AI slop' to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Clean AI-generated code slop', 'regression-safe, deletion-first workflow', and 'optional reviewer-only mode' — rather than vague abstractions.	3 / 3
Completeness	Clearly answers 'what' but lacks an explicit 'Use when...' trigger clause; 'when' is only implied, which caps completeness at 2 per the guideline.	2 / 3
Trigger Term Quality	Uses the natural-ish phrase 'AI-generated code slop' but omits common one-word variations like 'deslop'/'anti-slop' that the body shows users actually say, so coverage is incomplete.	2 / 3
Distinctiveness Conflict Risk	The 'code slop' / 'deletion-first workflow' / 'reviewer-only mode' framing carves a clear niche unlikely to conflict with other skills.	3 / 3
	Total	10 / 12 Passed

Validation

93%

Warnings & errors only

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

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: Yeachan-Heo/oh-my-claudecode
Commit: 6c59219

Reviewed: about 7 hours ago

Table of Contents

Discovery Implementation Validation

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