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 skills add Eldergenix/Proofloop --skill clean-vmgit clone --depth 1 https://github.com/Eldergenix/ProofloopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/eldergenix/proofloop/clean-vm)<a href="https://agentmods.dev/skills/eldergenix/proofloop/clean-vm"><img src="https://agentmods.dev/badge/skills/eldergenix/proofloop/clean-vm/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/eldergenix/proofloop/clean-vm"><img src="https://agentmods.dev/badge/skills/eldergenix/proofloop/clean-vm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00045 | $0.00905 |
| Opus 5.5 | $0.00018 | $0.00362 |
| Sonnet 5.5 | $0.00009 | $0.00181 |
| Haiku 4.5 | $0.00005 | $0.00090 |
Grade A, and why
clean-vm 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 1mo ago.
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.
This is a copy
100% identical to clean-vm — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clean VM
Use this skill when repeated loop, Claude, or Codex runs leave the machine in a bad state.
Start with the bundled script. It does a dry run by default and only mutates the machine with --apply.
Default Workflow
- Run a dry run first:
python3 .agents/skills/clean-vm/scripts/clean_vm.py
- Review the report. The script only targets:
- loop manifests under
~/.loop/runs/<repoId> - direct
loop __bridge-mcp <runDir> <claude|codex>processes for stale runs - loop helper processes tied to stale run dirs
- Next.js and Storybook servers running inside stale loop worktrees
- loop-created worktrees from
git worktree list --porcelain
- Apply the cleanup once the plan looks safe:
python3 .agents/skills/clean-vm/scripts/clean_vm.py --apply
- Only close browser windows when the user explicitly wants browser cleanup:
python3 .agents/skills/clean-vm/scripts/clean_vm.py --apply --browsers
Safety Rules
- Always inspect the dry run before using
--apply. - Treat loop manifests in
~/.loop/runsas the source of truth for paired runs. - Keep any run whose manifest state is
submitted,working,reviewing, orinput-requiredand whosepidortmuxSessionis still live. - Never mass-kill
claude,codex, ornode. Kill only per PID after the script proves the process belongs to stale loop state. - Never remove the main worktree, the worktree containing the current
pwd, or a dirty worktree. - The script does not auto-force worktree removal. If a plain
git worktree removefails, it reports the failure and leaves escalation to a manual follow-up. - Treat any live tmux session as in use even if the manifest looks stale.
- Browser cleanup is opt-in and macOS-only.
What the Script Checks
- repo identity via
git rev-parse --git-common-dir, using the same repo id scheme as loop - run manifests under
~/.loop/runs - tmux liveness with exact session targets like
tmux has-session -t =<name>plus a live-pane check fromtmux list-panes - helper processes whose command line references a stale run dir
- direct loop bridge MCP server processes whose run dir is stale or orphaned
- dev servers matching
next dev,next-server,storybook, orstart-storybook - worktrees from
git worktree list --porcelain
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 90 lines · 45 tokens per session scan A 6b0bb4161aa6
clean-vm is a skill published in the GitHub repository Eldergenix/Proofloop (40 stars, last pushed 5mo ago), licensed MIT. It adds 45 tokens to every session and 905 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to clean-vm, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
skill-builder
Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.
rudder-data-graphs
Produces Data Graph YAML from RETL sources for Audiences. Use when designing Data Graphs, mapping RETL to entities/events, or assessing customer fit for Audiences.
rudder-typer-workflow
Generates type-safe SDKs (Swift/Kotlin) from tracking plans with compile-time validation. Use when generating type-safe event tracking code from tracking plans using RudderTyper.
rudder-instrumentation-planning
Designs event taxonomies and instrumentation strategies from business requirements. Use when designing event taxonomy from scratch or restructuring existing instrumentation strategy.
rudder-instrumentation-debugging
Diagnoses and fixes validation errors, schema issues, and instrumentation problems. Use when debugging validation errors, schema issues, or instrumentation problems.
rudder-ai-events-verify
Verifies RudderStack AI events locally: captures what an instrumented app or MCP server sends, validates it against the AI events v0.3 schema, and checks joins, counts, identity and content. Use when testing AI events, or when AI or MCP events look wrong or missing.