Borrowing it
Nothing to install: this file belongs to satsilem/ai-workflow-framework. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/satsilem/ai-workflow-framework/main/.claude/skills/project-cleanup/SKILL.mdgit clone --depth 1 https://github.com/satsilem/ai-workflow-frameworkWrote 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/satsilem/ai-workflow-framework/project-cleanup)<a href="https://agentmods.dev/skills/satsilem/ai-workflow-framework/project-cleanup"><img src="https://agentmods.dev/badge/skills/satsilem/ai-workflow-framework/project-cleanup/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/satsilem/ai-workflow-framework/project-cleanup"><img src="https://agentmods.dev/badge/skills/satsilem/ai-workflow-framework/project-cleanup.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 | $0.00138 | $0.02871 |
| Opus 5.5 | $0.00055 | $0.01148 |
| Sonnet 5.5 | $0.00028 | $0.00574 |
| Haiku 4.5 | $0.00014 | $0.00287 |
Grade A, and why
project-cleanup 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 2mo 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 — 317 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Cleanup — Enhanced
Complete project maintenance: Validation → Audit → Cleanup → Application.
Each phase presents numbered sub-tasks. User replies with task numbers to run (1,3,5, all, or skip), then those tasks execute sequentially with pauses for confirmation before application.
Preparation — Read all files
Before producing any proposal, read:
TODO.md— full fileSESSION.md— full file (KEY LEARNINGS + all chronological entries)INSIGHTS.md— full fileCLAUDE.md— for gaps and content audit.gitignore— verify local-only working directories are excluded appropriately.reference/INDEX.md— verify it exists and is current.reference/folder — list all files and check their metadata
Also identify:
- Project
.claude/skills/directory - Global skills directory (if configured for cross-project reuse)
Phase 1 — VALIDATION (Check doc consistency)
Sub-tasks:
1. SESSION.md link audit — Check for broken/outdated file references
2. INSIGHTS.md file validation — Verify referenced files exist
3. .reference/ health check — Verify metadata complete + fresh (Last reviewed ≤ 30 days)
4. CLAUDE.md gap detection — Check if "CLAUDE.md Gaps" from INSIGHTS are documented
5. Broken internal refs in skills — Find stale paths in skill files
→ Which validation tasks to run? Reply: 1, 2, 3, 1,3,5, all, or skip
After user replies:
For each selected task, perform the audit and report findings:
- Task 1: List broken SESSION.md links + suggested corrections
- Task 2: List missing files/folders referenced in INSIGHTS.md
- Task 3: List
.reference/files with missing/stale metadata - Task 4: List CLAUDE.md gaps NOT yet documented
- Task 5: List skill files containing stale
.ai-notes/paths
Present one task result at a time. After all selected tasks complete, ask:
Validation complete. Proceed to Phase 2 (Audit)?
→ Reply: yes, or skip to Phase 3/4
Phase 2 — AUDIT (Check INSIGHTS integrity)
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.
- 2mo ago First seen · 317 lines · 138 tokens per session scan A 0bae874eb20c
project-cleanup is a skill published in the GitHub repository satsilem/ai-workflow-framework (2 stars, last pushed 1mo ago), licensed MIT. It adds 138 tokens to every session and 2,871 once invoked, about $0.0006 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 2026-08-31.
project-cleanup compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| project-cleanupsatsilem/ai-workflow-framework | Skill | 2 | 1mo ago | MIT | A | 138 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |