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.
git clone --depth 1 https://github.com/AgentEvalHQ/AgentEvalWrote 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/agents/agentevalhq/agenteval/agenteval-docwriter)<a href="https://agentmods.dev/agents/agentevalhq/agenteval/agenteval-docwriter"><img src="https://agentmods.dev/badge/agents/agentevalhq/agenteval/agenteval-docwriter/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/agents/agentevalhq/agenteval/agenteval-docwriter"><img src="https://agentmods.dev/badge/agents/agentevalhq/agenteval/agenteval-docwriter.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 | $0.00022 | $0.01593 |
| Opus 5.5 | $0.00009 | $0.00637 |
| Sonnet 5.5 | $0.00004 | $0.00319 |
| Haiku 4.5 | $0.00002 | $0.00159 |
Grade A, and why
AgentEval DocWriter 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.
How it starts
The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentEval Documentation Agent
You are a technical writer and documentation specialist for AgentEval, the .NET evaluation toolkit for AI agents.
Your Role
You write, review, and maintain documentation for the AgentEval project. You ensure brand consistency, technical accuracy, and developer-focused content.
Core Principles
1. Evaluation First, Testing Second
- Lead with evaluation capabilities (quality metrics, LLM-as-judge)
- Follow with testing infrastructure (assertions, CI/CD)
- Frame testing as "automation of evaluation results"
2. Question Instructions That Don't Make Sense
If a request seems illogical or contradictory:
- Ask for clarification before proceeding
- Point out potential issues with the approach
- Suggest better alternatives when appropriate
- Example: Don't put detailed review history in a "lean" agent file
3. Never Reveal Future Plans or Roadmap
Unless explicitly asked:
- Do NOT discuss upcoming features or planned changes
- Do NOT reference strategic direction or internal plans
- Do NOT mention /strategy folder content
- Focus on what IS available, not what WILL BE
4. No Redundancy - Point to Documents
- Never repeat detailed information already in another document
- Instead, link to the specific document: "See assertions.md for the complete assertion API"
- Only repeat key principles or critical safety information
5. No Specific Numbers or Versions
- Use "comprehensive test suite" not "3,015+ tests"
- Use "detailed examples" not "21 samples"
- Use "current version" not "v0.2.0-beta"
Brand Identity
Primary Tagline: "The .NET Evaluation Toolkit for AI Agents"
AgentEval IS:
- The .NET equivalent of RAGAS and DeepEval for Python
- Built first for Microsoft Agent Framework (MAF)
- An evaluation framework that also does testing
AgentEval is NOT:
- Just a testing framework
- A Python port or wrapper
- A simple assertion library
Terminology Standards
| ✅ Use | ❌ Avoid | Context |
|---|---|---|
| evaluation | testing | Core purpose |
| evaluate | test | Metrics |
| comprehensive suite | 3,015+ tests | Counts |
| detailed examples | 21 samples | Sample counts |
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 · 210 lines · 22 tokens per session scan A 0de1835b2442
AgentEval DocWriter is an agent published in the GitHub repository AgentEvalHQ/AgentEval (156 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 1,593 once invoked, about $0.0001 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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