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 skills add DevelopersGlobal/ai-agent-skills --skill ci-cd-pipelinesgit clone --depth 1 https://github.com/DevelopersGlobal/ai-agent-skillsWrote 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/developersglobal/ai-agent-skills/ci-cd-pipelines)<a href="https://agentmods.dev/skills/developersglobal/ai-agent-skills/ci-cd-pipelines"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/ci-cd-pipelines.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 5.1 | $0.00033 | $0.00673 |
| Opus 5 | $0.00016 | $0.00336 |
| Sonnet 5 | $0.00007 | $0.00135 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
ci-cd-pipelines 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 8d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
CI/CD is the automation layer that enforces quality gates consistently, without relying on human memory or discipline. When CI is green, you know the code is tested, linted, and deployable. When it's red, nothing ships.
When to Use
- Setting up a new project
- Adding a new quality gate
- Reviewing CI/CD pipeline configuration
Process
Step 1: CI Gates (Every PR)
- All gates must pass before merge is allowed:
- Lint: code style and static analysis
- Unit tests: all pass
- Integration tests: key boundaries covered
- Security scan: SAST, dependency vulnerabilities
- Build: production artifact builds successfully
- Gates run in parallel where possible (speed matters).
- Maximum CI time: 10 minutes. If slower, optimize.
Verify: Merging is blocked when any gate fails.
Step 2: CD Pipeline (Every Main Merge)
- Main branch is always deployable.
- Deployment pipeline:
- Deploy to staging → run smoke tests → deploy to production (canary) → full rollout
- Every step is automated — no manual "click to deploy."
- Rollback is automated and tested.
Verify: A push to main triggers automated deployment with no human intervention required.
Step 3: Feature Flags Over Feature Branches
- Incomplete features go behind feature flags — not long-lived branches.
- Feature flags allow dark launching, A/B testing, and instant rollback without redeployment.
- Feature flag state is tracked in a dashboard.
Verify: New features are behind flags. No feature branches > 2 days old.
Step 4: Pipeline as Code
- CI/CD config is in the repo (
.github/workflows/,.gitlab-ci.yml, etc.). - Pipeline changes go through code review like any other change.
- Pipeline config is tested: changes to CI don't break CI.
Verify: CI config is in the repo and reviewed.
Common Rationalizations (and Rebuttals)
| Excuse | Rebuttal |
|---|---|
| "It's a small change, CI is optional" | Every "small change" that skipped CI is in the origin story of a major incident. |
| "Manual deployment gives us control" | Manual steps introduce human error. Automation gives you control. |
| "CI is too slow" | Optimize it. Don't skip it. |
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.
- 8d ago First seen · 80 lines · 33 tokens per session scan A b6f5437e84ea
ci-cd-pipelines is a skill published in the GitHub repository DevelopersGlobal/ai-agent-skills (65 stars, last pushed 4mo ago), licensed MIT. It adds 33 tokens to every session and 673 once invoked, about $0.0002 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
ci-cd-and-automation
Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.
ci-cd-and-automation
Automates CI/CD pipeline setup. Use when setting up or modifying build and deployment pipelines. Use when you need to automate quality gates, configure test runners in CI, or establish deployment strategies.
ci-cd-and-automation
Guidance for setting up continuous integration and delivery, where automated checks test and prepare software changes before release.
performance-optimization
Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.
code-review-and-quality
Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch.
constraint-driven-development
Establishes a project's quality bar as a written contract and stops agents quietly lowering it. Interviews the user on which dimensions matter, supplies sane default thresholds when they have no number in mind, records everything in CONSTRAINTS.md, and watches the diff for a weakened bar — new @ts-ignore or…