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 skills/happymonkeyai/agentsprotocol/git-pr-workflows-git-workflownpx skills add HappyMonkeyAI/AgentsProtocol --skill git-pr-workflows-git-workflowgit clone --depth 1 https://github.com/HappyMonkeyAI/AgentsProtocolWhat 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 | $0.00041 | $0.02175 |
| Opus 5 | $0.00020 | $0.01087 |
| Sonnet 5 | $0.00008 | $0.00435 |
| Haiku 4.5 | $0.00004 | $0.00217 |
Grade A, and why
git-pr-workflows-git-workflow 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 2d 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.
This is a copy
97% identical to git-pr-workflows-git-workflow — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Complete Git Workflow with Multi-Agent Orchestration
Orchestrate a comprehensive git workflow from code review through PR creation, leveraging specialized agents for quality assurance, testing, and deployment readiness. This workflow implements modern git best practices including Conventional Commits, automated testing, and structured PR creation.
[Extended thinking: This workflow coordinates multiple specialized agents to ensure code quality before commits are made. The code-reviewer agent performs initial quality checks, test-automator ensures all tests pass, and deployment-engineer verifies production readiness. By orchestrating these agents sequentially with context passing, we prevent broken code from entering the repository while maintaining high velocity. The workflow supports both trunk-based and feature-branch strategies with configurable options for different team needs.]
Use this skill when
- Working on complete git workflow with multi-agent orchestration tasks or workflows
- Needing guidance, best practices, or checklists for complete git workflow with multi-agent orchestration
Do not use this skill when
- The task is unrelated to complete git workflow with multi-agent orchestration
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
Configuration
Target branch: $ARGUMENTS (defaults to 'main' if not specified)
Supported flags:
--skip-tests: Skip automated test execution (use with caution)--draft-pr: Create PR as draft for work-in-progress--no-push: Perform all checks but don't push to remote--squash: Squash commits before pushing--conventional: Enforce Conventional Commits format strictly--trunk-based: Use trunk-based development workflow--feature-branch: Use feature branch workflow (default)
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.
- 2d ago First seen · 141 lines · 41 tokens per session scan A 0c2522433716
git-pr-workflows-git-workflow is a skill published in the GitHub repository HappyMonkeyAI/AgentsProtocol (5 stars, last pushed 1mo ago), licensed MIT. It adds 41 tokens to every session and 2,175 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to git-pr-workflows-git-workflow, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.