Awesome Claude Code Toolkit is a curated collection of extensions and configuration for Claude Code, including agents, skills, commands, plugins, hooks, rules, templates, MCP configurations, and companion apps. It is for Claude Code users who want ready-made workflows and integrations for different development tasks. The catalogue add-ons are selected components from this toolkit.
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 commands/rohitg00/awesome-claude-code-toolkit/ci-pipelinegit clone --depth 1 https://github.com/rohitg00/awesome-claude-code-toolkitWrote 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/commands/rohitg00/awesome-claude-code-toolkit/ci-pipeline)<a href="https://agentmods.dev/commands/rohitg00/awesome-claude-code-toolkit/ci-pipeline"><img src="https://agentmods.dev/badge/commands/rohitg00/awesome-claude-code-toolkit/ci-pipeline.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 | $0.00000 | $0.00648 |
| Opus 5 | $0.00000 | $0.00324 |
| Sonnet 5 | $0.00000 | $0.00130 |
| Haiku 4.5 | $0.00000 | $0.00065 |
Grade A, and why
ci-pipeline 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 yesterday.
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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/deploy-pilot:ci-pipeline
Generate a GitHub Actions CI/CD workflow tailored to the current project.
Process
-
Analyze the project to determine the pipeline requirements:
- Detect language and package manager from manifest files
- Check for existing test commands in package.json scripts, Makefile, or pyproject.toml
- Identify linting tools already configured (ESLint, Prettier, Ruff, golangci-lint, Clippy)
- Look for existing Docker configuration
- Check for deployment targets (Vercel, AWS, GCP, Kubernetes manifests)
-
Generate the workflow file at
.github/workflows/ci.ymlwith these jobs:
Lint Job
- Install dependencies with caching (npm ci, pip install, go mod download)
- Run the project's configured linter
- Run format checking (prettier --check, ruff format --check, gofmt)
- Run type checking if applicable (tsc --noEmit, mypy, pyright)
- Fail fast: this job should complete in under 2 minutes
Test Job
- Run the full test suite with coverage reporting
- Upload coverage artifacts for later reference
- For Node.js: use matrix strategy for Node 20 and 22
- For Python: use matrix strategy for Python 3.11 and 3.12
- Set timeout to prevent hung tests from blocking the pipeline
- Run tests in parallel if the framework supports it (pytest -n auto, vitest)
Build Job
- Depends on lint and test passing
- Build the application artifacts
- For Docker projects: build the image and optionally push to registry
- For libraries: build the package and verify it can be published (dry-run)
- Cache build outputs between runs
Deploy Job (optional, triggered on main branch only)
- Depends on build passing
- Deploy to the detected target environment
- Use environment protection rules and manual approval for production
- Include rollback instructions in job comments
- Configure these workflow settings:
- Trigger on push to main and pull requests
- Cancel in-progress runs when new commits are pushed to the same PR
- Set appropriate permissions (contents: read, packages: write if publishing)
- Use
actions/checkout@v4,actions/setup-node@v4, or equivalent - Cache dependency directories (node_modules, .pip-cache, ~/go/pkg/mod)
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.
- yesterday First seen · 65 lines · 0 tokens per session scan A 0f68ad8dce1b
ci-pipeline is a command published in the GitHub repository rohitg00/awesome-claude-code-toolkit (2,587 stars, last pushed 3mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 648 tokens. 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 2026-09-03.
Other commands, from other repositories
ci
Analyze Github Actions logs and fix issues.
brainstorm
You are a Solution Brainstormer, an elite software engineering expert who specializes in system architecture design and technical decision-making. Your core mission is to collaborate with users to find the best possible solutions while maintaining brutal honesty about feasibility and trade-offs.
config
Adjust Claude Code status line display (preset, sections, bar width).
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.