Clean AI-generated code slop with a regression-safe, deletion-first workflow and optional reviewer-only mode
48
51%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/ai-slop-cleaner/SKILL.mdQuality
Discovery
40%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 carves out a distinctive niche around cleaning AI-generated code with a specific methodology, which is its main strength. However, it lacks explicit trigger guidance ('Use when...'), uses somewhat jargon-heavy terms like 'slop' without covering natural user phrasings, and doesn't enumerate the concrete actions the skill performs.
Suggestions
Add an explicit 'Use when...' clause, e.g., 'Use when the user asks to clean up, refactor, or simplify AI-generated code, or mentions code slop, boilerplate removal, or dead code elimination.'
List specific concrete actions the skill performs, e.g., 'Removes dead code, eliminates redundant comments, simplifies over-abstracted patterns, and runs regression checks before and after changes.'
Include natural trigger terms users would say, such as 'clean up code', 'refactor', 'simplify', 'remove boilerplate', 'code quality', alongside the more niche terms already present.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | It names the domain ('AI-generated code slop') and describes a methodology ('regression-safe, deletion-first workflow', 'reviewer-only mode'), but doesn't list specific concrete actions like 'remove dead code, simplify abstractions, eliminate redundant comments'. | 2 / 3 |
Completeness | It describes what it does at a high level but has no explicit 'Use when...' clause or equivalent trigger guidance, and the 'what' is also somewhat vague. Per rubric guidelines, a missing 'Use when' clause caps completeness at 2, and the weak 'what' brings it to 1. | 1 / 3 |
Trigger Term Quality | Includes some relevant terms like 'AI-generated code', 'slop', 'deletion', and 'reviewer-only mode', but misses common natural user phrases like 'clean up code', 'refactor', 'remove boilerplate', 'simplify code', or 'code review'. | 2 / 3 |
Distinctiveness Conflict Risk | The combination of 'AI-generated code slop', 'regression-safe', 'deletion-first workflow', and 'reviewer-only mode' creates a very distinct niche that is unlikely to conflict with general code refactoring or review skills. | 3 / 3 |
Total | 8 / 12 Passed |
Implementation
62%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured process skill with strong workflow clarity and good smell classification taxonomy. Its main weaknesses are the lack of concrete executable examples (before/after code, specific commands) and some verbosity in sections that restate general best practices Claude already knows. The UI/Design Reviewer Checklist feels like it belongs in a separate reference file rather than inline.
Suggestions
Add a concrete before/after code example showing a specific slop cleanup (e.g., removing a pass-through wrapper or consolidating duplicate logic) to improve actionability.
Move the UI/Design Reviewer Checklist to a separate reference file (e.g., UI_REVIEW_CHECKLIST.md) and link to it from the main skill to improve progressive disclosure and conciseness.
Trim the 'OMC Execution Posture' section to only include constraints Claude wouldn't already follow by default — items like 'Stay concise and evidence-dense' and 'Treat new user instructions as local scope updates' are general agent behavior, not skill-specific guidance.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably well-structured but includes some unnecessary verbosity. The 'When to Use' and 'When Not to Use' sections overlap with the opening paragraph. The UI/Design Reviewer Checklist, while useful, is quite detailed and could be split to a separate file. Some sections like 'OMC Execution Posture' restate general good practices Claude already knows (e.g., 'Stay concise and evidence-dense'). | 2 / 3 |
Actionability | The workflow steps are clearly sequenced and the smell classification is concrete, but there are no executable code examples, no specific test commands, and no concrete before/after code snippets showing what a cleanup looks like. The guidance is instructional rather than executable — it tells Claude what to do conceptually but doesn't provide copy-paste-ready commands or code patterns. | 2 / 3 |
Workflow Clarity | The multi-step workflow is clearly sequenced with explicit validation checkpoints: protect behavior first with tests, write a plan before editing, run one smell-focused pass at a time with verification after each pass, run quality gates, and close with an evidence-dense report. The feedback loop (fix or back out if gates fail) is explicitly stated. The review mode has a clear separate workflow with specific checks. | 3 / 3 |
Progressive Disclosure | The content is well-organized with clear sections and headers, but it's a fairly long monolithic document (~130 lines of content) with no references to supporting files. The UI/Design Reviewer Checklist and the detailed smell classification could be split into separate reference files. No bundle files are provided, so there's no progressive disclosure structure to leverage. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
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