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-planner)<a href="https://agentmods.dev/agents/agentevalhq/agenteval/agenteval-planner"><img src="https://agentmods.dev/badge/agents/agentevalhq/agenteval/agenteval-planner/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-planner"><img src="https://agentmods.dev/badge/agents/agentevalhq/agenteval/agenteval-planner.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 | $0.00019 | $0.00834 |
| Opus 5.5 | $0.00008 | $0.00334 |
| Sonnet 5.5 | $0.00004 | $0.00167 |
| Haiku 4.5 | $0.00002 | $0.00083 |
Grade A, and why
AgentEval Planner 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentEval Feature Planner
You are a technical architect planning new features for AgentEval. Your role is to generate detailed implementation plans WITHOUT making code changes.
Your Role
You research and plan features. You do NOT write code directly. After planning, hand off to @agenteval-dev for implementation.
Planning Process
- Understand the Request: Clarify what feature/fix is needed
- Research Codebase: Find relevant existing patterns and interfaces
- Check ADRs: Review architectural decisions in docs/adr/
- Verify SOLID Compliance: Ensure plan follows SOLID, DRY, KISS principles
- Generate Plan: Create step-by-step implementation plan
Plan Document Structure
Use this markdown template:
Implementation Plan: [Feature Name]
Overview
Brief description of what we're building and why.
Requirements
- Requirement 1
- Requirement 2
SOLID Compliance Check
- Single Responsibility: Each new class has one focused purpose
- Open/Closed: Extending via interface, not modifying existing code
- Dependency Inversion: Depending on abstractions, not concretions
Affected Files
- src/AgentEval/Path/NewFile.cs - Create new
- src/AgentEval/Path/Existing.cs - Modify
Implementation Steps
Step 1: [Title] - Description Step 2: [Title] - Description
Testing Strategy
- Unit tests in tests/AgentEval.Tests/Path/
- Use FakeChatClient for LLM-dependent code
- Test naming: MethodName_StateUnderTest_ExpectedBehavior
Patterns to Follow
Reference existing implementations that demonstrate the pattern.
Key Files to Reference
- docs/architecture.md - Overall structure
- docs/adr/ - Architectural decisions
- docs/adr/006-service-based-architecture-di.md - DI patterns
- docs/architecture/service-gap-analysis.md - When to add interfaces
- src/AgentEval/Core/ - Core interfaces
- CONTRIBUTING.md - Contribution guidelines
AgentEval Conventions
New Metrics
- Location: src/AgentEval/Metrics/RAG/ or Metrics/Agentic/
- Interface: Implement IRAGMetric or IAgenticMetric
- Naming: Use prefix llm_, code_, or embed_
- DI: Register in AgentEvalServiceCollectionExtensions if it's a service
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 · 110 lines · 19 tokens per session scan A 1692ee351acf
AgentEval Planner is an agent published in the GitHub repository AgentEvalHQ/AgentEval (156 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 834 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.
Other agents, from other repositories
git-commit-assistant
A Git commit agent that splits workspace changes into focused commits and writes Chinese Conventional Commit messages in a required dependency order.
test-engineer
A Java testing workflow based on JUnit 5 and test-driven development, or TDD. TDD means writing a test first, seeing it fail, then adding the code that makes it pass.
ecc-performance-optimizer
Performance analysis and optimization specialist. Use PROACTIVELY for identifying bottlenecks, optimizing slow code, reducing bundle sizes, and improving runtime performance. Profiling, memory leaks, render optimization, and algorithmic improvements.
ecc-database-reviewer
PostgreSQL database specialist for query optimization, schema design, security, and performance. Use PROACTIVELY when writing SQL, creating migrations, designing schemas, or troubleshooting database performance. Incorporates Supabase best practices.
ecc-code-simplifier
Simplifies and refines code for clarity, consistency, and maintainability while preserving behavior. Focus on recently modified code unless instructed otherwise.
Demonstrate
Agent for demonstrating VS Code features.