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/basher83/lunar-claude/git-workflownpx skills add basher83/lunar-claude --skill git-workflowgit clone --depth 1 https://github.com/basher83/lunar-claudeWrote 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/skills/basher83/lunar-claude/git-workflow)<a href="https://agentmods.dev/skills/basher83/lunar-claude/git-workflow"><img src="https://agentmods.dev/badge/skills/basher83/lunar-claude/git-workflow.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 | $0.00026 | $0.01456 |
| Opus 5 | $0.00013 | $0.00728 |
| Sonnet 5 | $0.00005 | $0.00291 |
| Haiku 4.5 | $0.00003 | $0.00146 |
Grade A, and why
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 4d 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Git Workflow Best Practices
Guidance for creating clean, atomic commits and organizing git workflows effectively.
Available Skills
This plugin provides fork-isolated skills to automate git workflows:
| Skill | Purpose |
|---|---|
git-status |
Quick repository status summary |
git-commit |
Create commits with pre-commit hooks via commit-craft agent |
branch-cleanup |
Clean up merged/stale branches |
generate-changelog |
Generate CHANGELOG.md using git-cliff |
All workflow skills use context: fork for delegation isolation. The git-commit skill delegates to the commit-craft agent, which handles the full commit workflow including pre-commit hook detection, execution, and failure recovery.
Changelog Generation
Use generate-changelog after creating commits to update CHANGELOG.md. Accepts an optional action argument (preview, generate, release). Without an argument, prompts interactively.
Conventional Commit Format
Structure commit messages following the conventional commit specification. Project-specific CLAUDE.md conventions take precedence over these defaults.
type(scope): subject
body (optional)
footer (optional)
Commit Types
| Type | Purpose |
|---|---|
feat |
New feature |
fix |
Bug fix |
docs |
Documentation only |
style |
Formatting, no code change |
refactor |
Code restructuring |
perf |
Performance improvement |
test |
Adding/updating tests |
build |
Build system changes |
ci |
CI configuration |
chore |
Maintenance tasks |
revert |
Revert previous commit |
Subject Line Rules
- Maximum 72 characters
- Use imperative mood ("add feature" not "added feature")
- No period at end
- Lowercase subject (e.g.
feat(auth): add oauth2 login— match the casing of existing commits in the repo if a different convention is established)
Body Guidelines
- Wrap at 72 characters
- Explain what and why, not how
- Separate from subject with blank line
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.
- 4d ago First seen · 210 lines · 26 tokens per session scan A 4800554aba0d
git-workflow is a skill published in the GitHub repository basher83/lunar-claude (22 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 1,456 once invoked, about $0.0001 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 2026-08-30.
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.
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.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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…