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/agentevalhq/agenteval/copilot-instructionsgit clone --depth 1 https://github.com/AgentEvalHQ/AgentEvalWhat 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 | $0.02994 | $0.02994 |
| Opus 5 | $0.01497 | $0.01497 |
| Sonnet 5 | $0.00599 | $0.00599 |
| Haiku 4.5 | $0.00299 | $0.00299 |
Grade A, and why
AgentEval 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 yesterday.
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 — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentEval - AI Coding Agent Instructions
AgentEval is the comprehensive .NET evaluation toolkit for AI agents, built primarily for Microsoft Agent Framework (MAF) with Microsoft.Extensions.AI. What RAGAS and DeepEval do for Python, AgentEval does for .NET—plus tool chain evaluation, behavioral policies, and calibrated multi-judge scoring.
Architecture Overview
AgentEval/
├── src/
│ ├── AgentEval.Abstractions/ # Public contracts: IMetric, IEvaluableAgent, models
│ ├── AgentEval.Core/ # Implementations: metrics, assertions, comparison, tracing
│ ├── AgentEval.DataLoaders/ # Data loaders, exporters, output formatting
│ ├── AgentEval.MAF/ # Microsoft Agent Framework integration
│ ├── AgentEval.RedTeam/ # Security scanning, attack types, compliance
│ └── AgentEval/ # Umbrella packaging project (NuGet: AgentEval)
├── tests/AgentEval.Tests/ # xUnit tests, mirrors src/ structure
└── samples/AgentEval.Samples/ # runnable samples (organised in groups A–J)
All library sub-projects use RootNamespace=AgentEval to preserve original namespaces. Only the umbrella is IsPackable=true; the single NuGet package embeds every sub-project DLL per TFM. The CLI lives in this repository at src/AgentEval.Cli/ and is the canonical source for the AgentEval.Cli NuGet package (packed as the agenteval dotnet tool); the former external AgentEvalHQ/AgentEval.Cli repository is being retired.
Environment Setup
Required environment variables for samples and integration tests:
$env:AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com/"
$env:AZURE_OPENAI_API_KEY = "your-api-key"
# Optional: Secondary models for comparison
$env:AZURE_OPENAI_DEPLOYMENT = "gpt-4o" # Primary model
$env:AZURE_OPENAI_DEPLOYMENT_2 = "gpt-4o-mini" # Secondary model
Build & Test Commands
dotnet build # Build all projects
dotnet test # Run all tests (×3 TFMs)
dotnet run --project samples/AgentEval.Samples # Run samples
dotnet pack src/AgentEval # Create NuGet package
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.
- yesterday First seen · 307 lines · 2,994 tokens per session scan A 66a5d6ea958e
AgentEval copilot-instructions.md is an instructions file published in the GitHub repository AgentEvalHQ/AgentEval (138 stars, last pushed yesterday), licensed MIT. It adds 2,994 tokens to every session, about $0.0150 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
zeroclaw CLAUDE.md
Instructions for zeroclaw-labs/zeroclaw, covering claude.md — zeroclaw (claude code), claude code settings, hooks and slash commands.
openagent CLAUDE.md
Claude Code instructions for the-open-agent/openagent, covering claude.md, commands, architecture, backend (go / beego) and frontend (react).
Tracely-ai CLAUDE.md
Claude Code instructions for Jwuthri/Tracely-ai, covering claude.md, commands, architecture, hard rules and gotchas.
nuwax AGENTS.md
Instructions for nuwax-ai/nuwax, covering ai agent system documentation, 系统概述, ai agent 架构, 核心组件 and ai 功能特性.
zhin zhin-plugin.instructions.md
Instructions for zhinjs/zhin, covering zhin plugin runtime authoring, package contract, convention directories, imports and native typescript and command routes.
OpenPersona AGENTS.md
Instructions for acnlabs/OpenPersona, covering agents.md, project overview, setup, project structure and architecture rules.