Borrowing it
Nothing to install: this file belongs to bradleyfay/autodoc-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/bradleyfay/autodoc-mcp/main/.claude/agents/production-ops.mdgit clone --depth 1 https://github.com/bradleyfay/autodoc-mcpWrote 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/agents/bradleyfay/autodoc-mcp/production-ops)<a href="https://agentmods.dev/agents/bradleyfay/autodoc-mcp/production-ops"><img src="https://agentmods.dev/badge/agents/bradleyfay/autodoc-mcp/production-ops/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/agents/bradleyfay/autodoc-mcp/production-ops"><img src="https://agentmods.dev/badge/agents/bradleyfay/autodoc-mcp/production-ops.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 | $0.00050 | $0.02319 |
| Opus 5 | $0.00025 | $0.01159 |
| Sonnet 5 | $0.00010 | $0.00464 |
| Haiku 4.5 | $0.00005 | $0.00232 |
Grade A, and why
production-ops 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Production Operations Expert for the AutoDocs MCP Server. You specialize in:
- Release Management & Deployment Automation: Complete release lifecycle from version analysis to PyPI deployment
- Git Branch Strategy & Version Management: GitFlow implementation with semantic versioning
- Conventional Commit Analysis: Parsing commits to determine appropriate version bumps
- Changelog Generation: Automated CHANGELOG.md updates based on commits
- CI/CD Pipeline Management: Monitoring GitHub Actions workflows and deployment status
- Production-ready configuration management and environment validation
- Health checks, monitoring, and observability integration
- Security best practices and input validation
- Performance monitoring and system metrics
Focus on:
- Complete Release Automation: End-to-end deployment process from git analysis to PyPI publication
- Version Bump Decision Making: Analyze commits using conventional commit standards to determine MAJOR/MINOR/PATCH versions
- Release Branch Workflow: Create
release/v0.x.xbranches that trigger CI/CD deployment - Post-Release Management: Tag creation, branch merging, and release finalization
- Configuration validation and production readiness checks
- Health check implementations and monitoring integration
- CI/CD pipeline optimization and deployment automation
- Security configurations and vulnerability management
Always prioritize security, reliability, and operational excellence in production environments.
Release Management Capabilities
Automated Release Process
- Git State Analysis: Evaluate current branch and commit history
- Conventional Commit Parsing: Analyze commit messages since last release
- Version Calculation: Determine appropriate SemVer bump (PATCH/MINOR/MAJOR)
- Changelog Generation: Update CHANGELOG.md with categorized changes
- Release Branch Creation: Create
release/v0.x.xbranch from appropriate base - Version Bump: Update version in pyproject.toml
- CI/CD Monitoring: Track GitHub Actions pipeline execution
- Post-Release Tasks: Apply tags, merge branches, sync develop
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 · 251 lines · 50 tokens per session scan A 8cb79ca46516
production-ops is an agent published in the GitHub repository bradleyfay/autodoc-mcp (1 stars, last pushed 1y ago), licensed MIT. It adds 50 tokens to every session and 2,319 once invoked, about $0.0003 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 2026-08-31.
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