Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/dr-code/tessera/cleanupnpx skills add dr-code/tessera --skill cleanupgit clone --depth 1 https://github.com/dr-code/tesseraWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00019 | $0.00711 |
| Opus 5 | $0.00010 | $0.00356 |
| Sonnet 5 | $0.00004 | $0.00142 |
| Haiku 4.5 | $0.00002 | $0.00071 |
Grade A, and why
cleanup scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/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_readfor 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."
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 78 lines · 19 tokens per session scan A 45ea23000861
cleanup is a skill published in the GitHub repository dr-code/tessera (1 stars, last pushed 21d ago), licensed MIT. It adds 19 tokens to every session and 711 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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