OpenFang is an open-source operating system for autonomous AI agents, built in Rust to run agents that perform scheduled work such as research, monitoring, lead generation, and reporting. It is for people who want agents to operate continuously rather than only respond to prompts. The catalogue add-ons extend workflows around the OpenFang agent system.
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/rightnow-ai/openfang/ci-cdnpx skills add RightNow-AI/openfang --skill ci-cdgit clone --depth 1 https://github.com/RightNow-AI/openfangWrote 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/rightnow-ai/openfang/ci-cd)<a href="https://agentmods.dev/skills/rightnow-ai/openfang/ci-cd"><img src="https://agentmods.dev/badge/skills/rightnow-ai/openfang/ci-cd.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 | $0.00022 | $0.00572 |
| Opus 5 | $0.00011 | $0.00286 |
| Sonnet 5 | $0.00004 | $0.00114 |
| Haiku 4.5 | $0.00002 | $0.00057 |
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 5d 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 — 39 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.
- 5d ago First seen · 39 lines · 22 tokens per session scan A 12c5af1ca4ba
ci-cd is a skill published in the GitHub repository RightNow-AI/openfang (18,162 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 22 tokens to every session and 572 once invoked, about $0.0001 per session on Opus 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
github-actions-docs
Use when users ask how to write, explain, customize, migrate, secure, or troubleshoot GitHub Actions workflows, workflow syntax, triggers, matrices, runners, reusable workflows, artifacts, caching, secrets, OIDC, deployments, custom actions, or Actions Runner Controller, especially when they need official GitHub…
devops
Deploy to Vercel (frontends, serverless), Railway (backends, services), Supabase (database, auth, storage), GitHub Actions CI/CD. Use for deployments, infrastructure, monitoring, CI/CD.
agent-workflow-automation
Agent skill for workflow-automation - invoke with $agent-workflow-automation.
agent-ops-cicd-github
Agent skill for ops-cicd-github - invoke with $agent-ops-cicd-github.
workflow-automation
Workflow creation, execution, and template management. Automates complex multi-step processes with agent coordination. Use when: automating processes, creating reusable workflows, orchestrating multi-step tasks. Skip when: simple single-step tasks, ad-hoc operations.
deployment-patterns
Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications.