Microsoft Agent 365 SDK is a Python software development kit for building agents that work with Microsoft 365, Teams, Copilot Studio, and Webchat. Developers use it for agent observability, notifications, runtime utilities, and development tools.
Borrowing it
Nothing to install: this file belongs to microsoft/Agent365-python. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/microsoft/Agent365-python/main/.claude/skills/commit/SKILL.mdgit clone --depth 1 https://github.com/microsoft/Agent365-pythonWrote 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/microsoft/agent365-python/commit)<a href="https://agentmods.dev/skills/microsoft/agent365-python/commit"><img src="https://agentmods.dev/badge/skills/microsoft/agent365-python/commit/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/microsoft/agent365-python/commit"><img src="https://agentmods.dev/badge/skills/microsoft/agent365-python/commit.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 | $0.00065 | $0.01275 |
| Opus 5.5 | $0.00026 | $0.00510 |
| Sonnet 5.5 | $0.00013 | $0.00255 |
| Haiku 4.5 | $0.00006 | $0.00128 |
Grade A, and why
commit 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.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Commit Skill
You are a commit assistant that ensures code quality before committing changes. Follow these steps in order, stopping if any step fails.
Step 1: Check for Staged Changes
First, verify there are staged changes to commit:
git diff --cached --stat
If there are no staged changes, inform the user and stop. Suggest they stage changes with git add.
Step 2: Check Linting
Run the linting check first (matches CI order in .github/workflows/ci.yml):
uv run --frozen ruff check .
If linting check fails:
- Inform the user about the linting errors
- Ask if they want you to auto-fix what can be auto-fixed
- If yes, run:
uv run --frozen ruff check . --fix - If there are remaining errors that cannot be auto-fixed:
- Show the errors clearly to the user
- STOP the commit process
- Explain what needs to be manually fixed
- If all errors were auto-fixed:
- Stage the fixes by running
git addonly on the files that were fixed - Continue to the next step
- Stage the fixes by running
If linting check passes:
Continue to the next step.
Step 3: Check Code Formatting
Run the formatting check (after linting, matches CI order):
uv run --frozen ruff format --check .
If formatting check fails:
- Inform the user that formatting issues were found
- Ask if they want you to auto-fix the formatting issues
- If yes, run:
uv run --frozen ruff format . - Show the user what files were reformatted
- Stage the formatting fixes by running
git addonly on the files that were reformatted - Continue to the next step
If formatting check passes:
Continue to the next step.
Step 4: Re-verify After Auto-fixes
IMPORTANT: If any auto-fixes were applied in Steps 2 or 3, re-run both checks to ensure consistency:
uv run --frozen ruff check .
uv run --frozen ruff format --check .
This prevents commits that pass locally but fail CI (e.g., lint fixes that introduce formatting issues). If either check fails after auto-fixes, STOP and inform the user that manual intervention is needed.
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 · 165 lines · 65 tokens per session scan A f2a13afd7711
commit is a skill published in the GitHub repository microsoft/Agent365-python (41 stars, last pushed 2d ago), licensed MIT. It adds 65 tokens to every session and 1,275 once invoked, about $0.0003 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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