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/openaiworkshop/copilot-instructionsgit clone --depth 1 https://github.com/microsoft/OpenAIWorkshopWhat 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 | $0.00550 | $0.00550 |
| Opus 5 | $0.00275 | $0.00275 |
| Sonnet 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 2d 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
6 near-identical copies found in the catalogue:
- PrompyAI CLAUDE.md — 97% identical, 102 lines differ
- ContextGraph CLAUDE.md — 94% identical, 109 lines differ
- fastapi-docs-mcp CLAUDE.md — 94% identical, 103 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.
- 2d 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 (894 stars, last pushed 1mo ago), licensed MIT. It adds 550 tokens to every session, about $0.0028 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
generative-ai-for-beginners AGENTS.md
Instructions for microsoft/generative-ai-for-beginners, covering agents.md, project overview, setup commands, initial repository setup and clone the repository.
azure-search-openai-demo AGENTS.md
Instructions for Azure-Samples/azure-search-openai-demo, covering instructions for coding agents, overall code layout, adding new data, adding a new azd environment variable and adding a new setting to "developer settings" in rag app.
azure-search-openai-demo bicep.instructions.md
Infrastructure as Code with Bicep.
GPT-RAG copilot-instructions.md
Instructions for Azure/GPT-RAG, covering repository development and release instructions, branching strategy, default behavior, feature development workflow and branch creation.
GPT-RAG AGENTS.md
Instructions for Azure/GPT-RAG, covering gpt-rag agent operating contract, priority, what this repository is, repository boundaries and how to work.
AI-Gateway AGENTS.md
Instructions for Azure-Samples/AI-Gateway, covering agents.md, directory structure, labs/, modules/ and shared/.