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 seb1n/awesome-ai-agent-skills --skill ci-cdgit clone --depth 1 https://github.com/seb1n/awesome-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/seb1n/awesome-ai-agent-skills/ci-cd)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/ci-cd"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/ci-cd/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/ci-cd"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/ci-cd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 252 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00048 | $0.02676 |
| Opus 5 | $0.00024 | $0.01338 |
| Sonnet 5 | $0.00010 | $0.00535 |
| Haiku 4.5 | $0.00005 | $0.00268 |
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 11d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ci-cd — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CI/CD Pipeline Setup
This skill enables the agent to design, configure, and maintain CI/CD pipelines that automate the entire software delivery lifecycle. The agent can set up pipeline stages including linting, testing, building, deploying, and notifying stakeholders, ensuring that every code change is validated and delivered reliably. The agent understands secrets management, caching strategies, matrix builds, and deployment strategies such as blue/green and canary releases.
Workflow
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Assess the Project and Choose a Platform: The agent analyzes the project's language, framework, hosting environment, and team preferences to recommend a CI/CD platform. Options include GitHub Actions, GitLab CI/CD, Jenkins, CircleCI, and Azure DevOps. The agent considers factors like repository hosting, cost, plugin ecosystem, and integration with existing tools before making a recommendation.
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Define Pipeline Stages: The agent structures the pipeline into discrete stages: lint (static analysis and code style), test (unit, integration, and end-to-end), build (compilation, bundling, Docker image creation), deploy (staging and production), and notify (Slack, email, or webhook alerts). Each stage has clearly defined inputs, outputs, and failure conditions so the pipeline fails fast on errors.
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Configure Secrets and Environment Variables: The agent sets up secure storage for API keys, database credentials, cloud provider tokens, and other sensitive values using the platform's native secrets manager (e.g., GitHub Secrets, GitLab CI/CD Variables, or Jenkins Credentials). Secrets are never hardcoded in pipeline files and are scoped to the appropriate environment.
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Implement Caching and Optimization: The agent configures dependency caching (npm, pip, Maven) and build artifact caching to reduce pipeline execution time. Matrix builds are used to test across multiple language versions or operating systems in parallel. The agent also sets up conditional execution so that expensive stages like end-to-end tests only run on relevant branches.
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.
- 11d ago First seen · 271 lines · 48 tokens per session scan A d73f8f7f8b59
ci-cd is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 2,676 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.
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