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/whit3rabbit/clean-room-skill/initnpx skills add whit3rabbit/clean-room-skill --skill initgit clone --depth 1 https://github.com/whit3rabbit/clean-room-skillWrote 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/whit3rabbit/clean-room-skill/init)<a href="https://agentmods.dev/skills/whit3rabbit/clean-room-skill/init"><img src="https://agentmods.dev/badge/skills/whit3rabbit/clean-room-skill/init.svg" alt="Measured on agentmods" 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 | $0.00032 | $0.02294 |
| Opus 5 | $0.00016 | $0.01147 |
| Sonnet 5 | $0.00006 | $0.00459 |
| Haiku 4.5 | $0.00003 | $0.00229 |
Grade A, and why
init 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clean Room Init
Overview
Initialize or revise durable Clean Room run preferences before source analysis starts. The output is an init-config.json controller artifact and an initialization_snapshot copied into each new task-manifest.json.
Preflight Goal Contract
Before creating active artifacts, collect or confirm preflight-goal.json. Do not start attended or unattended execution until the goal contract records end goal, target stack, license policy, dependency policy, compatibility/exactness policy, feature add/remove policy, code hygiene limits, output policy, existing destination policy, and controller mode. Completed preflight inputs and unattended contracts must also record intent_confirmation proving the end goal, target stack, and controller mode came from explicit user answers.
Do not infer the user's end goal or target stack from the source repository. A source stack is not a destination stack; ports and rewrites often intentionally change language, runtime, framework, package manager, and test framework. If end goal or target stack is unknown, leave blocking open_questions, keep controller_policy.unattended_allowed_after_preflight false, and do not write runner-ready task-manifest.json or clean-run-context.json.
Keep preflight-goal.json in the controller/contaminated artifact domain. Clean roles receive only the clean-safe goal_contract subset, code_hygiene_policy, and optional Agent 4 local commit policy through clean-run-context.json.
The safest path for canonical artifacts is the generated CLI schema/template path. Use clean-room-skill preflight --template or clean-room-skill preflight --input for preflight, and use clean-room-skill artifact template --kind <kind> --output <path> plus clean-room-skill artifact validate --path <path> for other canonical artifacts. Do not hand-write task-manifest.json or clean-run-context.json from scratch; start from CLI generators, CLI templates, or existing schema-valid artifacts.
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.
- 4d ago First seen · 70 lines · 32 tokens per session scan A 0df3540a5363
init is a skill published in the GitHub repository whit3rabbit/clean-room-skill (10 stars, last pushed 2d ago), licensed MIT. It adds 32 tokens to every session and 2,294 once invoked, about $0.0002 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…