What is production-ops?

A production operations specialist for the AutoDocs MCP Server. Production operations covers releasing software, deploying it, monitoring it, and keeping its configuration secure.

Use it to manage versions and changelogs, monitor CI/CD pipelines, prepare PyPI releases, validate environments, and investigate production problems.

It brings release, deployment, monitoring, and security work into one place, reducing mistakes when software moves from development to users.

How bradleyfay/autodoc-mcp configures Claude Code

Reuse

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.

Copy the file

curl -O https://raw.githubusercontent.com/bradleyfay/autodoc-mcp/main/.claude/agents/production-ops.md

Clone the repo

git clone --depth 1 https://github.com/bradleyfay/autodoc-mcp

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/agents/bradleyfay/autodoc-mcp/production-ops/github.svg)](https://agentmods.dev/agents/bradleyfay/autodoc-mcp/production-ops)

Your own site

<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.

Your own site · 80×15

<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>

Per session 50 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 2,319 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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 5.5	$0.00020	$0.00928
Sonnet 5.5	$0.00010	$0.00464
Haiku 4.5	$0.00005	$0.00232

Measured 2mo ago against content hash 8cb79ca46516, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

.claude/agents/production-ops.md · 251 lines

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.x branches 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.x branch 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

Read the full file on GitHub · 251 lines

Changes

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.

2mo ago First seen · 251 lines · 50 tokens per session scan A 8cb79ca46516

Subscribe to this mod's changes

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 $0.0002 per session on Opus 5.5. 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.

DevOps and cloud

production-ops next to 6 agents from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
production-opsbradleyfay/autodoc-mcp	Agent	1	1y ago	MIT	A	50	Claude Code
playwright-test-generatormicrosoft/playwright	Agent	99k	today	Apache-2.0	A	151	Claude Code
.NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginners	Agent	79k	today	MIT	A	33	Claude Code
AVM Owner Triagegithub/awesome-copilot	Agent	41k	yesterday	MIT	A	61	Claude Code
Ultimate Transparent Thinking Beast Modegithub/awesome-copilot	Agent	41k	yesterday	MIT	A	11	Claude Code
code-revieweranthropics/claude-cookbooks	Agent	54k	3d ago	MIT	A	52	Claude Code
Pimcore Expertgithub/awesome-copilot	Agent	41k	yesterday	MIT	A	26	Claude Code

autodoc-mcp: Agent for Claude Code