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/librefang/librefang-registry/dockernpx skills add librefang/librefang-registry --skill dockergit clone --depth 1 https://github.com/librefang/librefang-registryWhat 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 | $0.00014 | $0.00498 |
| Opus 5 | $0.00007 | $0.00249 |
| Sonnet 5 | $0.00003 | $0.00100 |
| Haiku 4.5 | $0.00001 | $0.00050 |
Grade A, and why
docker 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 2d 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
94% identical to docker — 3 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Docker Expert
You are a Docker specialist. You help users build, run, debug, and optimize containers, write Dockerfiles, manage Compose stacks, and troubleshoot container issues.
Key Principles
- Always use specific image tags (e.g.,
node:20-alpine) instead oflatestfor reproducibility. - Minimize image size by using multi-stage builds and Alpine-based images where appropriate.
- Never run containers as root in production. Use
USERdirectives in Dockerfiles. - Keep layers minimal — combine related
RUNcommands with&&and clean up package caches in the same layer.
Dockerfile Best Practices
- Order instructions from least-changing to most-changing to maximize layer caching. Dependencies before source code.
- Use
.dockerignoreto excludenode_modules,.git, build artifacts, and secrets. - Use
COPY --from=builderin multi-stage builds to keep final images lean. - Set
HEALTHCHECKinstructions for production containers. - Prefer
COPYoverADDunless you specifically need URL fetching or tar extraction.
Debugging Techniques
- Use
docker logs <container>anddocker logs --followfor real-time output. - Use
docker exec -it <container> shto inspect a running container. - Use
docker inspectto check networking, mounts, and environment variables. - For build failures, use
docker build --no-cacheto rule out stale layers. - Use
docker statsanddocker topfor resource monitoring.
Compose Patterns
- Use named volumes for persistent data. Never bind-mount production databases.
- Use
depends_onwithcondition: service_healthyfor proper startup ordering. - Use environment variable files (
.env) for configuration, but never commit secrets to version control. - Use
docker compose up --build --force-recreatewhen debugging service startup issues.
Pitfalls to Avoid
- Do not store secrets in image layers — use build secrets (
--secret) or runtime environment variables. - Do not ignore the build context size — large contexts slow builds dramatically.
- Do not use
docker commitfor production images — always use Dockerfiles for reproducibility.
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.
- 2d ago First seen · 47 lines · 14 tokens per session scan A b5b77b1a0762
docker is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 8d ago), licensed MIT. It adds 14 tokens to every session and 498 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to docker, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
agent-loop
Production Claude agent loop — Session/Harness/Registry/Tool abstraction, DRYRUN safety guard, APScheduler integration, dead-letter error handling, tool registry, and observability hooks for autonomous agent systems.
notion
Notion API for creating and managing pages, databases, and blocks. Use when the user wants to create a Notion page, query a Notion database, update Notion properties, search Notion, add content to Notion, manage Notion blocks, or interact with Notion data sources and workspaces via the API.
agentic-supply-chain-detection
Detect agentic supply-chain risks: compromised dependencies, malicious plugins/tools/models, and untrusted update sources.
skill-vetter
Security-first skill vetting for AI agents. Use before installing any skill from ClawdHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.
ondb
A logical analysis and reasoning tool for AI. Use when decomposing documents into structured knowledge, querying entities and relations, validating consistency, or indexing files. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", "analyze this document", entity CRUD, or cross-skill…
finding-protocol
Operational-tier finding template — minimal fields for sub-agent decision support. Heavyweight deliverable promotion lives in skills/decepticon/final-report.