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AI Agent Disk Janitor - Automated Cleanup Tool
A specialized disk management tool that monitors AI coding agent workspaces in real-time and automatically cleans up crash dumps, orphaned build artifacts, stale test outputs, and excessive logs. Unlike general disk analyzers, it understands agent session lifecycles and knows exactly what is safe to delete.
Why this matters
You're a developer who has embraced AI coding agents to boost productivity, running multiple Codex or Claude Code sessions in parallel across worktrees. But you've noticed your disk space vanishing—90GB, 330GB, gone. Crash dumps pile up, build artifacts multiply across worktrees, validation tests spawn by the thousands, and logs bloat endlessly. Your system slows to a crawl from memory pressure caused by orphaned processes. You've tried general disk tools like DaisyDisk, but they don't understand which agent files are safe to delete. You've resorted to buying external SSDs, writing custom cleanup scripts, or even spinning up a second AI agent just to clean up the first one's mess. There's no tool that proactively manages this growing problem.
- · Built for Professional developers and engineering teams using AI coding agents (Codex, Claude Code, Cursor, etc.) who run multiple parallel agent sessions and experience disk pressure.
- · Most likely monetization: freemium.
The Pain · Narrative
You're a developer who has embraced AI coding agents to boost productivity, running multiple Codex or Claude Code sessions in parallel across worktrees. But you've noticed your disk space vanishing—90GB, 330GB, gone. Crash dumps pile up, build artifacts multiply across worktrees, validation tests spawn by the thousands, and logs bloat endlessly. Your system slows to a crawl from memory pressure caused by orphaned processes. You've tried general disk tools like DaisyDisk, but they don't understand which agent files are safe to delete. You've resorted to buying external SSDs, writing custom cleanup scripts, or even spinning up a second AI agent just to clean up the first one's mess. There's no tool that proactively manages this growing problem.
Score Breakdown
Market Signal
Go-to-Market
Individual power users and small dev teams running 4+ concurrent AI coding agent sessions who have already experienced disk pressure events
~50K active power users globally, growing rapidly as AI coding adoption increases
Hacker News launch
$12/month for Pro, free tier with manual cleanup
500 free users and 20 paying users within 30 days of HN launch
MVP Scope · 1–2 weeks
- Build file system watcher that monitors ~/.codex, ~/.claude, and common worktree directories for size growth
- Implement detection rules for known artifact patterns (crash dumps, .log files, build/ target/ dist/ directories in worktrees)
- Create CLI that scans and reports disk usage by agent tool and session with safe-to-delete recommendations
- Add dry-run mode that shows what would be cleaned with sizes and confidence scores
- Set up GitHub repo with documentation and install script for macOS and Linux
- Implement automatic cleanup with configurable thresholds (e.g., delete when workspace exceeds 5GB or session is marked complete)
- Add process monitoring to detect and optionally kill orphaned agent processes
- Build a simple TUI dashboard showing real-time disk usage, cleanup history, and savings
- Add git worktree detection to identify stale worktrees and offer pruning
- Create configuration system for custom cleanup rules per project or agent tool
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1AI tool vendors like OpenAI and Anthropic may ship native cleanup features in their agents, making a third-party tool redundant. This is the strongest risk given the visibility of the problem.
- 2Users may not trust an automated tool to delete files safely, preferring manual control with free tools like DaisyDisk or Mole. Building sufficient trust for automated deletion is a significant barrier.
- 3The rapidly evolving landscape of AI coding tools means constant maintenance to keep detection rules current, which may be unsustainable for a small team or solo developer.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
Over 15 commenters described AI coding agents consuming massive disk space through crash dumps, build artifacts, test files, and logs. One user reported 330GB consumed; another cited 90GB from validation tests alone. Multiple users purchased external SSDs or disk tools to cope. Several built custom cgroups, cleanup scripts, or even secondary AI agents to manage the mess. General-purpose tools like DaisyDisk and Mole were frequently mentioned but require manual operation and lack agent-specific awareness. The pain is acute, recurring, and actively costing users money in hardware workarounds.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Build
Strong demand signals detected. Real pain, real willingness to pay — start building an MVP.
Landing Page Copy Kit
Ready-to-paste copy based on real Reddit community language — no editing required
Headline
AI Agent Disk Janitor - Automated Cleanup Tool
Sub-headline
A specialized disk management tool that monitors AI coding agent workspaces in real-time and automatically cleans up crash dumps, orphaned build artifacts, stale test outputs, and excessive logs. Unlike general disk analyzers, it understands agent session lifecycles and knows exactly what is safe to delete.
Who It's For
For Professional developers and engineering teams using AI coding agents (Codex, Claude Code, Cursor, etc.) who run multiple parallel agent sessions and experience disk pressure
Feature List
✓ Real-time monitoring of agent workspaces with configurable disk usage thresholds ✓ Agent-aware cleanup rules for known artifact patterns (crash dumps, build outputs, test files, logs) ✓ Automatic cleanup triggers on session completion or disk pressure events ✓ Process lifecycle management to kill orphaned agent processes ✓ Dashboard showing disk usage breakdown by agent tool and session
Where to Validate
Share your landing page in r/HN · front_page — that's exactly where these pain points were discovered.
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