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 instructions/microsoft/agent365-skills/agents-mdgit clone --depth 1 https://github.com/microsoft/agent365-skillsWrote 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/instructions/microsoft/agent365-skills/agents-md)<a href="https://agentmods.dev/instructions/microsoft/agent365-skills/agents-md"><img src="https://agentmods.dev/badge/instructions/microsoft/agent365-skills/agents-md.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 | $0.07071 | $0.07071 |
| Opus 5 | $0.03535 | $0.03535 |
| Sonnet 5 | $0.01414 | $0.01414 |
| Haiku 4.5 | $0.00707 | $0.00707 |
Grade A, and why
agent365-skills AGENTS.md 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 3d 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 — 360 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent 365 Plugin — Development Guidelines
This file documents conventions for contributors working on the agent365 plugin skills.
Read this before making any changes to skill files.
Plugin Purpose
This plugin instruments and configures A365 agents. It contains seven skills:
| Skill | Command | Trigger |
|---|---|---|
make-ai-teammate |
/agent365:make-ai-teammate |
"make this agent an AI Teammate", "add AI Teammate hosting", "transform agent to Teams agent" |
a365-setup |
/agent365:a365-setup |
"run a365 setup", "create blueprint", "register agent" |
make-a365-agent |
/agent365:make-a365-agent |
"provision agent with a365", "Registration setup", "observability setup", "register this agent" |
add-workiq-tools |
/agent365:add-workiq-tools |
"add workiq tools", "add MCP servers to this agent" |
instrument-observability |
/agent365:instrument-observability |
"instrument observability", "add a365 observability" |
a365-code-validator |
/agent365:a365-code-validator |
"validate a365 code", "debug MAC activity", "check A365 exporter flags" |
test-local |
/agent365:test-local |
"test this agent locally", "open agentsplayground" |
Supported languages for make-ai-teammate: .NET (AgentFramework · Semantic Kernel) · Node.js (LangChain · OpenAI Agents SDK · Claude SDK · Semantic Kernel · Google ADK) · Python (AgentFramework · LangChain · OpenAI · Claude · Semantic Kernel · Google ADK)
Supported languages for instrument-observability: .NET AgentFramework · Node.js (LangChain · OpenAI · Claude SDK · Semantic Kernel · Google ADK) · Python (AgentFramework · LangChain · OpenAI · Claude · Semantic Kernel · Google ADK)
Supported agent stacks for add-workiq-tools (verified against Agent365-{dotnet,python,nodejs}):
- .NET: Agent Framework · Semantic Kernel (different API:
AddToolServersToAgentAsync, notGetMcpToolsAsync) · Azure AI Foundry (best-effort — package published, no Microsoft sample) - Node.js: LangChain (returns new agent — capture return) · OpenAI Agents SDK (mutates in place) · Claude SDK (first arg is
Options, mutates in place) · ⚠ Semantic Kernel and Google ADK hard-stop (no Microsoft adapter — skill exits at Phase 0B) - Python: Agent Framework (uses
turn_context=kwarg, requiresinitial_tools=[]) · OpenAI Agents SDK (usescontext=kwarg, noagentic_app_id) · Google ADK (passesagentic_app_id, wraps inasyncio.wait_for) · Semantic Kernel and Azure AI Foundry (best-effort — package published, no Microsoft sample) · ⚠ LangChain / Claude SDK / CrewAI hard-stop (no Microsoft adapter; Claude and CrewAI samples ship a local DIYmcp_tool_registration_service.pyscaffold — out of scope for this skill)
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.
- 3d ago First seen · 360 lines · 7,071 tokens per session scan A f1535bc95448
agent365-skills AGENTS.md is an instructions file published in the GitHub repository microsoft/agent365-skills (32 stars, last pushed 8d ago), licensed MIT. It adds 7,071 tokens to every session, about $0.0354 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 instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
next.js AGENTS.md
Instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.