Git AI is a Git extension that records which agent, model, and prompts produced AI-generated code in a repository. It is for developers and teams that need line-level attribution and the surrounding context for code created with AI coding tools. The catalogue skills and instructions support using Git AI in agent-assisted development 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.
npx skills add git-ai-project/git-ai --skill askgit clone --depth 1 https://github.com/git-ai-project/git-aiWrote 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/git-ai-project/git-ai/ask)<a href="https://agentmods.dev/skills/git-ai-project/git-ai/ask"><img src="https://agentmods.dev/badge/skills/git-ai-project/git-ai/ask/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/skills/git-ai-project/git-ai/ask"><img src="https://agentmods.dev/badge/skills/git-ai-project/git-ai/ask.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00054 | $0.01482 |
| Opus 5.5 | $0.00022 | $0.00593 |
| Sonnet 5.5 | $0.00011 | $0.00296 |
| Haiku 4.5 | $0.00005 | $0.00148 |
Grade A, and why
ask 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ask — 100% identical, 0 lines differ
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.
Ask Skill
Answer questions about AI-written code by finding the original prompts and conversations that produced it, then embodying the author agent's perspective to answer.
Main Agent's Job (you)
You do the prep work, then hand off to a fast, tightly scoped subagent:
-
Resolve the file path and line range — check these sources in order:
a) Editor selection context (most common). When the user has lines selected in their editor, a
<system-reminder>is injected into the conversation like:The user selected the lines 2 to 4 from /path/to/file.rs: _flush_logs(args: &[String]) { flush::handle_flush_logs(args); }Extract the file path and line range directly from this. This is the primary way users will invoke
/ask— they select code, then type something like "/ask why is this like that" without naming the file or lines.b) Explicit file/line references — "on line 42", "lines 10-50 of src/main.rs" → use directly.
c) Named symbol — mentions a variable/function/class → Read the file, find where it's defined, extract line numbers.
d) File without line specifics → whole file (omit
--lines).e) No file, no lines, no selection context, no identifiable code reference → Do NOT attempt to guess or search. Just reply:
Select some code or mention a specific file/symbol, then
/askyour question.Stop here. Do not spawn a subagent.
-
Spawn one subagent with the template below. Use
max_turns: 4. -
Relay the answer to the user. That's it.
Subagent Configuration
Task tool settings:
subagent_type: "general-purpose"
max_turns: 4
The subagent gets only Bash and Read. It does NOT get Glob, Grep, or Task. It runs at most 4 turns — this is a fast lookup, not a research project.
Choosing Between blame --show-prompt and search
If you want to read an entire file or range of lines AND the corresponding prompts behind them, use git-ai blame --show-prompt. This is better than search for this use case — it gives you every line's authorship plus the full prompt JSON in one call.
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 · 141 lines · 54 tokens per session scan A b9bb8bd00413
ask is a skill published in the GitHub repository git-ai-project/git-ai (2,844 stars, last pushed 2d ago), licensed Apache-2.0. It adds 54 tokens to every session and 1,482 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 2026-08-30.
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