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Workflow Janitor

A cleanup assistant for AI-assisted development workflows that preserves engineering knowledge, removes temporary clutter, and keeps repositories healthy.

Workflow Janitor is an AI coding workflow skill designed to clean up after large development sessions.

AI agents often create temporary artifacts during implementation:

  • Investigation notes
  • Generated reports
  • Debug files
  • Evaluation outputs
  • Scratch scripts
  • Temporary workspaces
  • Agent progress files

Some of these files are disposable. Others contain valuable reasoning, decisions, and project context.

Workflow Janitor performs an audit-first cleanup workflow:

  1. Analyze workspace artifacts
  2. Identify valuable knowledge
  3. Preserve important information
  4. Remove unnecessary clutter safely
  5. Generate an auditable cleanup report

It does not blindly delete files.


Why Workflow Janitor?

AI-assisted development creates a new repository maintenance problem.

A typical coding session may leave behind:

project/
├── debug-output.txt
├── investigation-notes.md
├── temp-script.py
├── evaluation-results/
├── generated-reports/
└── useful-architecture-notes.md

Traditional cleanup tools cannot understand the difference between:

  • temporary noise
  • important engineering context

Workflow Janitor helps agents make that distinction.


Features

Audit Before Cleanup

Workflow Janitor starts in audit mode.

It analyzes:

  • Temporary files
  • Generated artifacts
  • Agent-created reports
  • Workspace changes
  • Documentation impact

Nothing is removed automatically.

The result is a cleanup proposal:

Cleanup Report

Remove:
✓ debug-output.log
✓ temporary benchmark files

Preserve:
✓ architecture-notes.md
✓ migration decisions

Suggest:
→ Add *.log to .gitignore

Knowledge Preservation

Before removing artifacts, Workflow Janitor searches for useful information:

  • Architectural decisions
  • Implementation discoveries
  • Debugging solutions
  • Project constraints
  • Important workflow outcomes

Useful knowledge can be moved into:

  • README files
  • Documentation
  • Architecture notes
  • Project guidelines

Safe Cleanup Model

Workflow Janitor follows a preserve-first philosophy.

Cleanup rules:

  • Never delete without confirmation
  • Prefer quarantine over permanent deletion
  • Keep changes traceable
  • Protect active development work

Cleanup modes:

Audit Mode

Default mode.

Actions:

  • Scan files
  • Classify artifacts
  • Suggest cleanup
  • Extract knowledge

No modifications are made.


Cleanup Mode

Only runs after explicit confirmation.

Actions:

  • Move approved artifacts
  • Apply cleanup changes
  • Update documentation when needed
  • Generate cleanup summary

Workflow

Development Session
        |
        v
AI Creates Temporary Artifacts
        |
        v
Workflow Janitor Audit
        |
        +----------------+
        |                |
        v                v
 Preserve Knowledge   Remove Noise
        |                |
        +----------------+
                 |
                 v
        Cleaner Repository

Supported Platforms

Workflow Janitor supports multiple AI coding environments.

Currently supported:

Platform Support
Claude Code Native skill
Cursor Rules integration
GitHub Copilot AGENTS.md
Windsurf Rules integration
Cline Rules integration
Aider Configuration integration

Installation

Claude Code

Install as a Claude Code plugin:

/plugin marketplace add PrantoSMSS/pranto-plugins
/plugin install workflow-janitor@pranto-plugins

The skill will automatically become available in supported Claude Code workflows.


Other Platforms

Run the installer:

python scripts/install.py

The installer detects available tools and installs the correct configuration files.

Install all detected platforms

python scripts/install.py --all

Install a specific platform

python scripts/install.py --tool cursor

Preview changes

python scripts/install.py --dry-run

Remove installed files

python scripts/install.py --uninstall

When To Use

Use Workflow Janitor after:

  • Large feature implementations
  • Refactoring sessions
  • AI-assisted coding sessions
  • Code reviews
  • Benchmark runs
  • Temporary worktree usage
  • Experimental development

When NOT To Use

Do not run Workflow Janitor during:

  • Active debugging
  • Incomplete implementations
  • Ongoing experiments
  • Failed test investigation
  • Temporary work that is still needed

Design Principles

Preserve First

Important development knowledge should survive cleanup.


Audit Before Action

Understanding comes before modification.


Human Approval

Cleanup decisions remain visible and reversible.


Reduce Future Clutter

Workflow Janitor does not only clean repositories.

It also helps prevent repeated mess by suggesting:

  • .gitignore improvements
  • Documentation updates
  • Better artifact handling

Project Structure

workflow-janitor/

├── SKILL.md
├── .claude-plugin/
│   └── plugin.json
│
├── scripts/
│   └── install.py
│
├── platforms/
│   ├── aider/
│   ├── cline/
│   ├── copilot/
│   ├── cursor/
│   └── windsurf/
│
├── CHANGELOG.md
└── LICENSE

Philosophy

AI coding tools are powerful, but they leave behind traces of their work.

The goal is not to erase those traces.

The goal is to:

Preserve the knowledge. Remove the noise. Keep the workflow clean.


License

MIT License


Created by PrantoSMSS

About

Safely clean AI workflow artifacts while preserving development knowledge through audit-first cleanup, documentation updates, and quarantine-based removal.

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