Cleanup

Bidirectional AI slop scanner — Claude + GPT independently analyze, then debate disagreements.From its SKILL.md

Install

npx -y skills add dr-code/tessera --skill cleanup

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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SKILL.md

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/cleanup — Bidirectional AI Slop Scanner

Usage

/cleanup [file or directory] /cleanup — uses tessera graph to find recently modified files, or prompts for target

Description

Claude and GPT independently analyze code for quality issues and AI slop patterns, then reconcile disagreements. Produces a unified report with agreed issues (high confidence) and model-specific findings (lower confidence).

Instructions

Phase 0: Identify Target

If tessera MCP is configured:

1. graph_continue (mandatory first call)
2. graph_retrieve("recently modified files") — find candidates
3. graph_read each target file before analysis begins

If user specified files or a directory: use those (still call graph_read for each)
If tessera not active: ask user which files to analyze

Read all target files before starting analysis.

Phase 1: Claude's Independent Analysis

Analyze the files independently. Look for:

Unnecessary complexity or indirection
Dead code, unused variables, unreachable branches
Poor naming: vague, misleading, or verbose AI-generated names
Missing error handling at system boundaries (user input, external APIs, file I/O)
Premature abstractions: helpers used once, over-generalized interfaces, unnecessary wrapper functions
AI-generated patterns: comments that restate what the code does, defensive checks for impossible cases, empty catch blocks
Security issues at input boundaries

Format each issue as: [HIGH|MED|LOW] [TYPE] file:line — description

Do not share your findings yet.

Phase 2: GPT's Independent Analysis

Send the same files to Codex without revealing Claude's findings:

codex exec "Analyze this code for quality issues. Look for: unnecessary complexity, dead code, poor naming, missing error handling at boundaries, premature abstractions, AI-generated patterns (verbose comments restating code, unnecessary wrappers, impossible-case guards). File contents: <FILE_CONTENTS>. Format each issue as: [HIGH|MED|LOW] [TYPE] file:line — description. Do not suggest rewrites, only identify issues."

Phase 3: Reconcile

Compare the two sets of findings:

Agreed: both found the same issue — high confidence, list first
Claude-only: Claude found it, GPT missed it — present with reasoning
GPT-only: Claude evaluates each one. For significant disagreements, run a quick debate:

codex exec "Quick verdict needed. Issue: <ISSUE>. Claude disagrees because: <CLAUDE_REASONING>. Is Claude correct? Give a one-sentence verdict: agree with Claude OR stand by original finding with one supporting reason."

Phase 4: Report

Present the unified report:

Agreed Issues (fix these): [list with severity and file:line]

Claude-Only Findings: [list — lower confidence, Claude's assessment]

GPT-Only Findings (Claude evaluated): [list with Claude's verdict on each: confirmed / dismissed with reason]

Resolved Disagreements: [what was debated, what was concluded]

Ask: which issues to fix now? All HIGH? Specific items? Leave as reference?