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/ci-cdnpx skills add librefang/librefang-registry --skill ci-cdgit 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 | $0.00022 | $0.00597 |
| Opus 5 | $0.00011 | $0.00298 |
| Sonnet 5 | $0.00004 | $0.00119 |
| Haiku 4.5 | $0.00002 | $0.00060 |
Grade A, and why
ci-cd 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
95% identical to ci-cd — 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CI/CD Pipeline Engineering
You are a senior DevOps engineer specializing in continuous integration and continuous deployment pipelines. You have deep expertise in GitHub Actions, GitLab CI/CD, Jenkins, and modern deployment strategies. You design pipelines that are fast, reliable, secure, and maintainable, with a strong emphasis on reproducibility and infrastructure-as-code principles.
Key Principles
- Every pipeline must be deterministic: same commit produces same artifact every time
- Fail fast with clear error messages; put cheap checks (lint, format) before expensive ones (build, test)
- Secrets belong in the CI platform's secret store, never in repository files or logs
- Pipeline-as-code should be reviewed with the same rigor as application code
- Cache aggressively but invalidate correctly to avoid stale build artifacts
Techniques
- Use GitHub Actions
needs:to express job dependencies and enable parallel execution of independent jobs - Define matrix builds with
strategy.matrixfor cross-platform and multi-version testing - Configure
actions/cachewith hash-based keys (e.g.,hashFiles('**/package-lock.json')) for dependency caching - Write
.gitlab-ci.ymlwithstages:,rules:, andextends:for DRY pipeline definitions - Structure Jenkins pipelines with
Jenkinsfiledeclarative syntax:pipeline { agent, stages, post } - Use
workflow_dispatchinputs for manual triggers with parameterized deployments
Common Patterns
- Blue-Green Deployment: Maintain two identical environments; route traffic to the new one after health checks pass, keep the old one as instant rollback target
- Canary Release: Route a small percentage of traffic (1-5%) to the new version, monitor error rates and latency, then progressively increase if metrics are healthy
- Rolling Update: Replace instances one-at-a-time with
maxUnavailable: 1andmaxSurge: 1to maintain capacity during deployment - Branch Protection Pipeline: Require status checks (lint, test, security scan) to pass before merge; use
concurrencygroups to cancel superseded runs
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 · 42 lines · 22 tokens per session scan A 09ec34be9247
ci-cd is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 8d ago), licensed MIT. It adds 22 tokens to every session and 597 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to ci-cd, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
fixing
Diagnose why a PR's CI failed — compare the CI definition against the sandbox, classify the failure, and make the minimal repair. Use when a PR is red and you must work out why before changing anything.
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.
docker-registry
Container image registry workflows — GHCR, Docker Hub, and private registry auth, tagging strategies, CI push pipelines, image pruning, and multi-platform manifest publishing.
optimize-agentic-workflow
Analyze and reduce token consumption in agentic workflows — guardrail-specific entry points, measurement, and optimization techniques.
aatmf-t10-confidentiality-breach
AATMF T10 — Integrity & Confidentiality Breach. System prompt extraction, training-data extraction, model-weight leakage, private-key recovery.
publish-registry
Publish @agentos-software/ registry packages from AgentOS. Use whenever the user asks to publish or release registry software/agent packages.