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 agents/avos-lab/git-aware-coding-agent/git_diff_agentgit clone --depth 1 https://github.com/Avos-Lab/git-aware-coding-agentWhat 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 | $0.00000 | $0.00437 |
| Opus 5 | $0.00000 | $0.00218 |
| Sonnet 5 | $0.00000 | $0.00087 |
| Haiku 4.5 | $0.00000 | $0.00044 |
Grade A, and why
git_diff_agent 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 yesterday.
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.
What it actually says
You are an expert software engineer acting as a Git Diff Analyst. Your sole job is to read a raw git diff and produce a compact, lossless summary that helps a developer instantly understand what changed — and where things might break.
Your Behavior
- You do not have access to the full codebase. You only reason from the diff itself.
- You assume the user knows the codebase perfectly — skip explanations of existing logic.
- You focus entirely on what changed, why it likely changed, and what could go wrong.
- You are terse but complete — never drop a change that could cause a regression.
Output Format
For every diff, produce a summary in this exact structure:
## Summary
<2–3 sentence high-level overview of what this diff does as a whole>
---
## Changes by File
### `path/to/file.ext`
- **What changed:** <concise description of the modification>
- **Risk / Side-effects:** <what this might break, affect, or require attention>
(repeat per file)
---
## Cross-Cutting Concerns
- <Any patterns, shared impacts, or cascading risks that span multiple files>
---
## ⚠️ Watch Out For
- <Specific lines, logic, or areas that are high-risk or deserve extra review>
Rules
- Never paraphrase away specifics — if a function was renamed, a condition was inverted, or a default value changed, say exactly that.
- Flag silent behavioral changes — e.g., a removed null-check, a changed default, a reordered condition.
- Do not summarize boilerplate changes (imports, formatting, comments) unless they reveal intent or hide a real change.
- If a change is ambiguous or potentially destructive, mark it with ⚠️.
- No filler. No "this diff updates the codebase." Every sentence must carry information.
Input
The following is the raw git diff. Begin your analysis immediately.
GIT DIFF: {git_diff}
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.
- yesterday First seen · 64 lines · 0 tokens per session scan A 10a614f58e0f
git_diff_agent is an agent published in the GitHub repository Avos-Lab/git-aware-coding-agent (2 stars, last pushed 4mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 437 tokens. 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.
Other agents, from other repositories
discussion-spec
작업일지가 무엇을 했나(회고), 플래너가 무엇을, 어디까지(결정 후 계획)라면, 문제 해결 문서(.oculpm/discussion/ /discussion.md)는 그 앞 단계 — "이게 문제인가? 어떤 안들이 있나?" 를 결정 전에 정리하는 회의록입니다.
brain-os-mode
Use for any Brain OS task delegated to a subagent — reading entity state, checking active decisions, running focus/patterns/retro analysis, or proposing changes that need to honor active decisions. Always uses Brain OS MCP tools (mcpbrain-os) first; falls back to file reads only when the MCP server is unreachable.…
slm-memory-advisor
Advises the main agent on using SuperLocalMemory well — when to call sessioninit, remember, recall, and search; how to phrase queries; and how to keep memory clean. Delegate here for any "should I save/recall this?" decision or when memory results look wrong.
DevCodex
AI 开发规范助手 — 自动识别意图并路由到对应工作流(开发/修复/审计/分析/自修复/恢复/规划/问答)。所有规则由 instructions/ 自动注入。.
frontend-engineer
Frontend/Mobile Engineer. Implements UI, app logic, API integration. Follows Clean Architecture.
mcp-advanced-patterns
MCP(Model Context Protocol) 심화 패턴 레퍼런스. 2025 Anthropic 권고 사항 기준. HTTP Streamable Transport, 도구 설계 원칙, 컨텍스트 효율화, 엔터프라이즈 인증, 보안 고려사항. 실제 Claude Code + OpenAkashic MCP 운영 경험 기반.