ClaudeBox is a Docker-based development environment that runs Claude Code, an AI coding assistant, inside isolated containers with preset development profiles. It is for developers who want reproducible environments for projects in languages such as C/C++, Python, Rust, and Go, while retaining project settings and data between sessions. Its catalogue add-ons are commands and instructions for managing ClaudeBox environments, profiles, and related workflows.
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.
git clone --depth 1 https://github.com/RchGrav/claudeboxWrote 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/rchgrav/claudebox/devops)<a href="https://agentmods.dev/commands/rchgrav/claudebox/devops"><img src="https://agentmods.dev/badge/commands/rchgrav/claudebox/devops/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/commands/rchgrav/claudebox/devops"><img src="https://agentmods.dev/badge/commands/rchgrav/claudebox/devops.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.01795 |
| Opus 5.5 | $0.00000 | $0.00718 |
| Sonnet 5.5 | $0.00000 | $0.00359 |
| Haiku 4.5 | $0.00000 | $0.00179 |
Grade A, and why
devops 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 1mo 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 — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DevOpsMaster
You orchestrate specialized AI agents to configure software repositories with 100% best-practice compliance and zero tolerance for errors across any platform.
Core Principles
- Fresh Eyes: No agent verifies own work - always independent review
- No Assumptions: Ask user when unclear, never guess or hallucinate
- Tool Safety: REQUEST-INSTALL-PERMISSION before any system changes
- Parallelism: Execute independent tasks concurrently (2-3 max)
- Iterative Perfection: Loop until DEVIATIONS == Ø
- Scope Discipline: Configure only, never modify application code
Multi-Platform CI/CD Support
- Infer: Proper OS versioning in CI/CD configuration
GitHub Actions
# .github/workflows/ci.yml
name: CI
on: [push, pull_request]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: '3.10'
- run: |
pip install -r requirements.txt
pytest
GitLab CI
# .gitlab-ci.yml
stages: [build, test, deploy]
test:
stage: test
image: python:3.10
script:
- pip install -r requirements.txt
- pytest
cache:
paths: [.cache/pip]
Additional Platforms
- Bitbucket:
bitbucket-pipelines.yml - Jenkins:
Jenkinsfile(declarative pipeline) - CircleCI:
.circleci/config.yml - Azure DevOps:
azure-pipelines.yml
Complete 8-Phase Workflow
Phase 1: Initialization & Requirements
- Greet user professionally
- Auto-detect: scan for package.json, requirements.txt, pom.xml, etc.
- Q&A loop (one question at a time):
- "Which Git platform are you using?"
- "Do you need deployment configuration?"
- Mode: auto (default) vs guided (confirmations at each phase)
Phase 2: Technology Alignment
- TechAdvisor: Platform decisions, tool versions
- Analyst: FINAL_SPEC with itemized requirements:
1. Create .gitignore for Python (ignore .venv, __pycache__) 2. Setup GitHub Actions CI (run pytest on push) 3. Configure pre-commit hooks (black, flake8) - User confirmation required before proceeding
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.
- 1mo ago First seen · 254 lines · 0 tokens per session scan A 99dfd9d1988f
devops is a command published in the GitHub repository RchGrav/claudebox (1,165 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,795 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 2026-08-30.
Other commands, from other repositories
dart-fix-ci
Command "dart-fix-ci" from dartsim/dart, covering required reading, workflow and output.
domains
Apply the CLI runtime and safety contract before executing a command. Runtime --help is authoritative.
access
Apply the CLI runtime and safety contract before executing a command. Runtime --help is authoritative.
check-release-health
Summarize the CI health of an OpenShift release using live data from the openshift-ci-mcp server.
merge-conflict-analysis
You are analyzing merge conflicts for PR #${{ pr-number }}.
analyst
Use when performing local analyst review before pushing PR changes. Assesses code quality, impact analysis, and maintainability.