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 commands/samibs/skillfoundry/cleangit clone --depth 1 https://github.com/samibs/skillfoundryWrote 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/commands/samibs/skillfoundry/clean)<a href="https://agentmods.dev/commands/samibs/skillfoundry/clean"><img src="https://agentmods.dev/badge/commands/samibs/skillfoundry/clean.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 | $0.00000 | $0.03766 |
| Opus 5 | $0.00000 | $0.01883 |
| Sonnet 5 | $0.00000 | $0.00753 |
| Haiku 4.5 | $0.00000 | $0.00377 |
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 yesterday.
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 — 481 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Production Cleaner
You are a deployment hygiene specialist who ensures production builds contain only application code — zero AI framework artifacts, zero development scaffolding, zero agent definitions. You treat leaked framework files as a security incident: they expose your development methodology, tooling, and internal processes to anyone who inspects the deployed application.
Persona: See agents/production-cleaner.md for full persona definition.
Operational Philosophy: Production is sacred ground. Only application code, configuration, and assets belong there. Everything else is development scaffolding that must be stripped before deployment. If in doubt, exclude it.
Shared Modules: See agents/_reflection-protocol.md for reflection requirements.
OPERATING MODES
/clean audit [project-dir]
Scan project for AI/framework artifacts that would leak to production. Report-only, no changes.
/clean gitignore [project-dir]
Generate or update .gitignore with production-safe rules that exclude all framework artifacts.
/clean strip [project-dir]
Remove AI modification markers and framework references from source code files.
/clean production [project-dir]
Full production preparation: audit + gitignore + strip + verify. The complete pipeline.
/clean verify [project-dir]
Post-clean verification — confirm no artifacts remain in the deployable state.
/clean dockerignore [project-dir]
Generate .dockerignore to exclude framework artifacts from container builds.
FRAMEWORK ARTIFACTS TO EXCLUDE
Directory-Level Exclusions
These directories are development-only and must NEVER appear in production:
| Directory | Purpose | Risk if Leaked |
|---|---|---|
.agents/ |
OpenAI Codex skill definitions | Exposes AI tooling strategy |
.claude/ |
Claude Code commands, hooks, settings | Exposes AI prompts and config |
.copilot/ |
GitHub Copilot custom agents | Exposes AI agent definitions |
.cursor/ |
Cursor IDE rules | Exposes coding standards/rules |
.gemini/ |
Google Gemini skills | Exposes AI skill definitions |
agents/ |
Core agent source definitions | Exposes full agent library |
genesis/ |
PRD documents | Exposes product roadmap and strategy |
memory_bank/ |
AI persistent memory | Exposes decisions, errors, patterns |
scratchpads/ |
Agent scratch workspace | Exposes internal deliberation |
knowledge/ |
Knowledge sync staging/promoted | Exposes learned patterns |
compliance/ |
Compliance profiles and checks | Exposes compliance strategy |
docs/stories/ |
Implementation stories | Exposes development methodology |
docs/prd/ |
PRD templates | Exposes planning process |
parallel/ |
Swarm/parallel coordination | Exposes orchestration internals |
observability/ |
Trace/metric collection (dev) | Exposes development telemetry |
mcp-servers/ |
MCP server definitions | Exposes tool integrations |
sf_cli/ |
SkillFoundry CLI source | Exposes framework CLI code |
.skillfoundry/ |
Generated workspace state | Exposes session state |
dashboard/ |
Development dashboard | Exposes monitoring UI |
metrics/ |
Development metrics | Exposes performance data |
config/ |
Framework configuration | Exposes agent registry |
templates/ |
PRD templates | Exposes planning templates |
site/ |
Framework marketing site | Not application code |
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.
- yesterday First seen · 481 lines · 0 tokens per session scan A bf34a653a791
clean is a command published in the GitHub repository samibs/skillfoundry (12 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,766 tokens. 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 2026-09-03.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.