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 Rockielab/rockie-claude --skill cleangit clone --depth 1 https://github.com/Rockielab/rockie-claudeWrote 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/rockielab/rockie-claude/clean)<a href="https://agentmods.dev/skills/rockielab/rockie-claude/clean"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-claude/clean/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/rockielab/rockie-claude/clean"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-claude/clean.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 | $0.00114 | $0.01216 |
| Opus 5.5 | $0.00046 | $0.00486 |
| Sonnet 5.5 | $0.00023 | $0.00243 |
| Haiku 4.5 | $0.00011 | $0.00122 |
Grade A, and why
clean 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- clean — 92% identical, 17 lines differ
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/clean — Pre-commit audit adapted for a research repo
Research repos are typically majority prose — scripts, experiment dirs,
and many .md docs read by humans AND future agents. Slop in either
kind hurts. The audit covers both.
When to run
- Before EVERY
git commit. Thepre-commit-gatehook will block if the sentinel hash doesn't match current staged state. - When the user asks to commit.
- When the hook emits a block message.
What the skill does
- Runs
${OPENCLAW_SKILLS_DIR}/clean/audit.py. Arguments:--scope staged(default) — audits files ingit diff --cached --name-only--scope dirty— audits all modified + untracked non-gitignored files
- The audit reports findings in three buckets:
- Blockers — must fix before commit (new
.mdfiles created during cleanup runs, broken internal doc links, obvious debug artifacts, syntax errors) - Warnings — should review (TODO markers pointing to resolved work,
duplicate content across docs, rules contradicting
${OPENCLAW_WORKSPACE_DIR}/memory/workflow.db) - Info — worth knowing (file size growth, complexity spikes)
- Blockers — must fix before commit (new
- If zero blockers: the audit invokes
${OPENCLAW_WORKSPACE_DIR}/scripts/clean-finalize.sh <hash>as its final action. That script writes${OPENCLAW_WORKSPACE_DIR}/.state/clean-ok-<hash>(where<hash>is computed by${OPENCLAW_WORKSPACE_DIR}/scripts/compute_clean_hash.sh) AND emits the upstream-contribute nudge to stderr. The pre-commit-gate hook recomputes the same hash and requires the sentinel to exist. - If blockers exist: the skill reports them and does NOT call
clean-finalize.sh, so no sentinel and no nudge. Agent fixes, reruns.
Hard rules for this repo specifically
- NEVER create new
.mdfiles during cleanup. Consolidate into existing ones. Exception: a user explicitly says "write a new doc for X." - NEVER auto-fix documentation. Flag it, let agent or user decide.
- NEVER audit the whole codebase. Only changed files. Existing slop elsewhere is not this commit's problem.
- Thresholds apply to NEW code only: Python funcs ≤ 80 lines, cyclomatic complexity ≤ 8. Existing violations are noise.
- Degrade gracefully: if
ruff/shellcheck/code-simplifierisn't installed, skip that check with a note. Don't block.
What ships with it
1 file 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 · 106 lines · 114 tokens per session scan A 4d447c01eaf5
clean is a skill published in the GitHub repository Rockielab/rockie-claude (23 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 114 tokens to every session and 1,216 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
git-commit
A guided Git commit workflow that examines changes and creates a commit message using the Conventional Commits format, a shared style for labeling changes such as features, fixes, tests, or documentation.
ln-61-repository-publisher
Commits, pushes and remotely verifies authorized repository changes; does not create releases.
gentle-ai-work-unit-commits
Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.
validate-changes
Evaluate staged changes using LLM-as-a-Judge before committing.
review
Use when operating as the Fabrik Review stage agent. This skill guides code review of an implementation, finding and fixing issues, and ensuring the PR is ready for human review.
review-comment
Use when operating as the Fabrik Review comment reviewer. This skill guides applying user decisions on review findings — fixing issues, dismissing false positives, or deferring items — then committing and pushing without signaling stage completion.