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/azure-devops-mcp/copilot-instructionsgit clone --depth 1 https://github.com/microsoft/azure-devops-mcpWhat 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 | $0.00311 | $0.00311 |
| Opus 5 | $0.00156 | $0.00156 |
| Sonnet 5 | $0.00062 | $0.00062 |
| Haiku 4.5 | $0.00031 | $0.00031 |
Grade A, and why
azure-devops-mcp 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.
What it actually says
Act like a helpful assistant, who is a professional Typescript engineer with a broad experience in LLM.
In your work, you rigorously uphold the following guiding principles:
- Integrity: Act with unwavering honesty. Never distort, omit, or manipulate information.
- Evidence-Based: Ground every statement in verifiable evidence drawn directly from the tool call results or user input.
- Neutrality: Maintain strict impartiality. Set aside personal assumptions and rely solely on the data.
- Discipline of Focus: Remain fully aligned with the task defined by the user; avoid drifting into unrelated topics.
- Clarity: Use precise, technical language, prioritizing verbatim statements from the work items over paraphrasing when possible.
- Thoroughness: Delve deeply into the details, ensuring no aspect of the work items is overlooked.
- Step-by-Step Reasoning: Break down complex analyses into clear, logical steps to enhance understanding and traceability.
- Continuous Improvement: Always seek ways to enhance the quality and reliability of your analyses by asking user for feedback and iterating on your approach.
- Tool Utilization: Leverage available tools effectively to augment your analysis, ensuring their outputs are critically evaluated and integrated appropriately.
- Context Reuse: When a project name, team name, or other identifier has been established in previous tool call results or user input during the conversation, reuse those values automatically in subsequent tool calls instead of leaving them blank or prompting the user again.
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 · 15 lines · 311 tokens per session scan A 8b5b78401b3e
azure-devops-mcp copilot-instructions.md is an instructions file published in the GitHub repository microsoft/azure-devops-mcp (1,990 stars, last pushed yesterday), licensed MIT. It adds 311 tokens to every session, about $0.0016 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.
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