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gunk-buster

Finds and quarantines context gunk — stale, agent-readable repo residue — before AI coding agents consume it.

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Gunk Buster digital zombie mascot contained with stale documentation, broken links, and code fragments

Gunk Buster — stop stale repository context from steering coding agents wrong

Local, deterministic hygiene for the docs and instructions AI coding agents read.
Find hallucination bait before an agent acts on it.

MIT license Node.js 20 or later Read-only MCP tools No telemetry

See it · Install · Proof · Workflow · Safety · Docs

Your coding agent reads more than code. It reads AGENTS.md, CLAUDE.md, setup guides, old plans, copied docs, and generated leftovers. When those files disagree with the repository, they become hallucination bait: plausible context that sends the agent in the wrong direction.

Gunk Buster checks that agent-readable surface against repository facts. It does not inspect application code, call a model, upload content, assign a magic score, or silently delete anything.

Agents may recommend. The CLI decides. The Chief approves.

See it

Terminal example: Gunk Radar finds an npm command in CLAUDE.md that conflicts with the repository's pnpm configuration and proposes a precise edit

The example is derived from the repository's package-manager-drift fixture: Radar locates the claim, cites the repository fact that contradicts it, and proposes the smallest mechanical edit. Diagnostic runs do not edit repository content.

Quick Start

Codex App and CLI

Install the repository plugin—skills, five read-only MCP tools, and the non-blocking edit advisory arrive together:

codex plugin marketplace add gustavo-meilus/gunk-buster
codex plugin add gunk-buster@gunk-buster

Start a fresh task and ask:

check this repo for stale context

No manual MCP registration is required. The same plugin is used by the Codex desktop app and CLI.

Claude Code

/plugin marketplace add gustavo-meilus/gunk-buster
/plugin install gunk-buster@gunk-buster

Restart the session after installation, then use the same unnamed prompt or invoke /gunk-scan or /gunk-radar directly.

CLI

The plugin is deliberately read-only. Trapping, restoring, and applying approved fixes require the separately installed gunk CLI:

npm install --global gunk-buster
gunk --version

Then establish a baseline:

gunk scan
gunk radar
gunk pile

See the full installation guide for updates, removal, platform status, and the separation between plugin and CLI capabilities.

Proof, not promises

Gunk Buster ships with recorded evidence, not a universal “saves tokens” claim.

The plugin changes agent behavior

In a fresh Codex CLI task, the byte-identical unnamed prompt automatically invoked the plugin-managed gunk_radar MCP tool. The prompt never named Gunk Buster, Radar, a skill, or an MCP tool. The run exited successfully and left the worktree unchanged.

Matched tool-exposure observation

Two runs used the same commit, worktree, prompt, model, effort, harness, and host six minutes apart. The only isolated difference was whether the plugin's MCP tools were available:

MetricTools absentTools presentDelta
Wall clock122.4s108.6s−11.3%
Reasoning output813693−14.8%
Answer output5,7824,766−17.6%
Shell commands1815−16.7%
Uncached input58,14457,432−1.2%
Total input317.7K408.7K+28.6%

The tools-present answer covered all five requested elements, was 34% shorter, and surfaced a duplicated skill that the control missed. It also challenged Radar's output instead of accepting every finding.

Independent context-cleanup experiment

On a difficult 398-file repository with 338 structural findings and 505 Radar findings, three medium-effort runs per condition showed 12.2% lower median wall time and 12.1% lower reasoning output after filtering identified context gunk. Total input rose 6.8%, and high-effort runs regressed.

These are proof-of-concept observations, not performance guarantees. Agent paths vary; cached input dominated several runs; the matched pair is a single observation; and the result depends on model and reasoning effort. Read the context-cleanup methodology and raw runs and the Codex activation proof.

How it works

gunk scan   → structural evidence: orphan docs, dumps, duplicates, broken links
gunk radar  → factual contradictions: dead paths, bad commands, package-manager drift
gunk pile   → grouped human view
gunk report → Markdown report
gunk trap   → Chief-approved move to an external vault, with a receipt
gunk restore / verify → byte-identical recovery and damage checks

scan finds structural problems. radar finds claims in docs and agent instructions that repository facts contradict. Both are deterministic; neither uses an LLM.

Labels describe; verdicts prescribe

LabelWhat was found
GHOSTOrphaned documentation or an unreferenced asset
DUMPGenerated output, cache, coverage, or tool residue
ECHODuplicated documentation
RELICSensitive orphaned material that always needs review
BAITWrong or misleading agent instructions
MOLDDocumentation contradicted by current repository facts
VerdictWhat to do
SAFECertain evidence and no safety concern; may be batch-trapped after confirmation
PROPOSEPlausibly stale; Chief decides
ASK_CHIEFSensitive, recent, or protected; interactive approval is mandatory
KEEPHealthy, insufficient evidence, or explicitly retained

Labels and verdicts are intentionally separate. Broken links and Radar claim findings describe a concrete inconsistency and propose an edit; they are never trapped.

The safety moat

Gunk Buster doesGunk Buster does not
Analyze docs, agent instructions, referenced assets, and generated artifactsAnalyze source code, imports, ASTs, or dead code
Produce explicit CERTAIN, STRONG, or WEAK evidenceProduce numeric “gunk scores” or fake precision
Move approved files outside agent reach with receiptsDelete files silently or hide them in an in-repo archive
Restore trapped files byte-for-byteStage, commit, or push Git changes
Expose read-only MCP toolsExpose trap, restore, or automatic fixes through MCP
Run locally with no telemetry or cloud serviceCall a model or upload repository content

Mutations remain visible terminal workflows. A content hash prevents acting on a file that changed after it was judged; every trap has a restore command; verify checks whether the mutation caused damage.

The Claude Code gunk-auditor profile requests a read-only tool allowlist, but current plugin-loaded subagent behavior does not enforce that allowlist as a structural security boundary. The MCP tools themselves remain read-only. Track the upstream-dependent limitation in issue #37.

Read the full safety model.

Agent surfaces

SurfaceStatusWhat ships
Gunk CLIImplemented and testedFull deterministic engine and Chief-approved mutations
Codex CLIVerifiedSkills, five read-only MCP tools, advisory hook
Codex desktopVerified smoke lifecycleSame repository plugin
Codex IDEAvailable, dedicated lifecycle waivedSame repository plugin; no separate transcript
Claude CodeImplementedSkills, MCP, advisory hook, auditor profile with the limitation above

Windows 11 is the manually certified MVP platform. The implementation is portable Node.js, but macOS and Linux do not yet have equivalent manual certification records.

Architecture

                 ┌─ CLI: scan, radar, pile, report, trap, restore, verify
deterministic    ├─ MCP: five read-only diagnostic tools
TypeScript core ├─ skills: when and how agents use the core
                 └─ hooks: advisory warnings, never enforcement

The CLI core is the single source of truth. Plugins and skills are distribution shells; they do not reimplement detection or safety policy. See the roadmap, domain vocabulary, and architecture decisions.

Documentation

Contributing

Bug reports, focused fixes, documentation improvements, and detector proposals are welcome. Read CONTRIBUTING.md before opening a pull request. Please use GitHub Issues for bugs and proposals, and report vulnerabilities through the process in SECURITY.md.

License

MIT © 2026 Gustavo Meilus. See LICENSE.

If Gunk Buster caught hallucination bait in your repo, star it and share the finding. Chief deserves clean context.

Keywords

ai-agents

FAQs

Package last updated on 17 Jul 2026

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