microsoft/OpenAIWorkshop is a repository of materials for designing, prototyping, comparing, and deploying AI-agent solutions with Microsoft technologies. It is for developers exploring single-agent and multi-agent architectures, orchestration patterns, MCP tools, and Azure deployment. The catalogue instruction supports coding agents working through the workshop materials.
Borrowing it
Nothing to install: this file belongs to microsoft/OpenAIWorkshop. 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/OpenAIWorkshop/main/.github/copilot-instructions.mdgit clone --depth 1 https://github.com/microsoft/OpenAIWorkshopWrote 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/openaiworkshop/copilot-instructions)<a href="https://agentmods.dev/instructions/microsoft/openaiworkshop/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/microsoft/openaiworkshop/copilot-instructions/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/instructions/microsoft/openaiworkshop/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/microsoft/openaiworkshop/copilot-instructions.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00550 | $0.00550 |
| Opus 5.5 | $0.00220 | $0.00220 |
| Sonnet 5.5 | $0.00110 | $0.00110 |
| Haiku 4.5 | $0.00055 | $0.00055 |
Grade A, and why
OpenAIWorkshop copilot-instructions.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 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
8 near-identical copies found in the catalogue:
- PrompyAI CLAUDE.md — 97% identical, 102 lines differ
- fastapi-docs-mcp CLAUDE.md — 94% identical, 103 lines differ
- ContextGraph CLAUDE.md — 94% identical, 109 lines differ
- eai-gofer CLAUDE.md — 92% identical, 154 lines differ
- SimpleModule AGENTS.md — 92% identical, 120 lines differ
- agentshare CLAUDE.md — 91% identical, 107 lines differ
- loci AGENTS.md — 89% identical, 122 lines differ
- krx-cli CLAUDE.md — 89% identical, 118 lines differ
How it starts
The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow Orchestration
1. Plan Node Default
- Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions)
- If something goes sideways, STOP and re-plan immediately – don’t keep pushing
- Use plan mode for verification steps, not just building
- Write detailed specs upfront to reduce ambiguity
2. Subagent Strategy
- Use subagents liberally to keep main context window clean
- Offload research, exploration, and parallel analysis to subagents
- For complex problems, throw more compute at it via subagents
- One task per subagent for focused execution
3. Self-Improvement Loop
- After ANY correction from the user: update
tasks/lessons.mdwith the pattern - Write rules for yourself that prevent the same mistake
- Ruthlessly iterate on these lessons until mistake rate drops
- Review lessons at session start for relevant project
4. Verification Before Done
- Never mark a task complete without proving it works
- Diff behavior between main and your changes when relevant
- Ask yourself: “Would a staff engineer approve this?”
- Run tests, check logs, demonstrate correctness
5. Demand Elegance (Balanced)
- For non-trivial changes: pause and ask “is there a more elegant way?”
- If a fix feels hacky: “Knowing everything I know now, implement the elegant solution”
- Skip this for simple, obvious fixes – don’t over-engineer
- Challenge your own work before presenting it
6. Autonomous Bug Fixing
- When given a bug report: just fix it. Don’t ask for hand-holding
- Point at logs, errors, failing tests – then resolve them
- Zero context switching required from the user
- Go fix failing CI tests without being told how
Task Management
- Plan First: Write plan to
tasks/todo.mdwith checkable items - Verify Plan: Check in before starting implementation
- Track Progress: Mark items complete as you go
- Explain Changes: High-level summary at each step
- Document Results: Add review section to
tasks/todo.md - Capture Lessons: Update
tasks/lessons.mdafter corrections
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 · 50 lines · 550 tokens per session scan A c5557c6e73f3
OpenAIWorkshop copilot-instructions.md is an instructions file published in the GitHub repository microsoft/OpenAIWorkshop (898 stars, last pushed yesterday), licensed MIT. It adds 550 tokens to every session, about $0.0022 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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