Borrowing it
Nothing to install: this file belongs to BayramAnnakov/eval-coach. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/BayramAnnakov/eval-coach/master/CLAUDE.mdgit clone --depth 1 https://github.com/BayramAnnakov/eval-coachWrote 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/instructions/bayramannakov/eval-coach/claude-md)<a href="https://agentmods.dev/instructions/bayramannakov/eval-coach/claude-md"><img src="https://agentmods.dev/badge/instructions/bayramannakov/eval-coach/claude-md/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/instructions/bayramannakov/eval-coach/claude-md"><img src="https://agentmods.dev/badge/instructions/bayramannakov/eval-coach/claude-md.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 | $0.00887 | $0.00887 |
| Opus 5.5 | $0.00355 | $0.00355 |
| Sonnet 5.5 | $0.00177 | $0.00177 |
| Haiku 4.5 | $0.00089 | $0.00089 |
Grade A, and why
eval-coach CLAUDE.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 2mo 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
Eval Coach is an Agent Skill that guides users through building AI evaluation strategies using Evaluation-Driven Development (EDD). It follows the Agent Skills open standard (SKILL.md format).
This is primarily a documentation and template repository - it provides guidance, templates, and examples rather than a runnable application.
Key Concepts
5-Step Framework
- Define Success - Map business goals to measurable metrics
- Design Dataset - Create test cases (50% happy path, 35% edge cases, 15% adversarial)
- Select Methods - Choose Automated, LLM-as-Judge, or Human evaluation
- Plan Automation - Integrate evals into CI/CD
- Monitor Production - Track drift and collect feedback
50-40-10 Rule (Swiss Cheese Model)
- 50% Automated - Schema validation, keyword checks, latency ($0.00/run)
- 40% LLM-as-Judge - Semantic quality, relevance ($0.01-0.05/run)
- 10% Human - Subjective quality, edge cases ($5-50/run)
Silent Failures
The most critical insight: agents can silently reconcile contradictory data instead of flagging mismatches. The input_data_consistency_evaluator was created specifically to catch this.
Repository Structure
.claude-plugin/
plugin.json # Plugin manifest for marketplace compatibility
templates/
dataset.py # LangSmith dataset creation with test case examples
evaluators.py # 10 evaluators: automated, LLM-as-Judge, performance
compare.py # Experiment comparison utilities
examples/
research_agent_eval.md # Complete evaluation plan example
SKILL.md # Agent skill definition (triggers, workflow steps)
Template Dependencies
pip install langsmith langchain-google-genai
Configuration
export GOOGLE_API_KEY="your-api-key"
export JUDGE_MODEL="gemini-3-flash-preview" # Default
export JUDGE_MODEL="gemini-3-pro-preview" # For higher quality
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.
- 2mo ago First seen · 100 lines · 887 tokens per session scan A d05809944110
eval-coach CLAUDE.md is an instructions file published in the GitHub repository BayramAnnakov/eval-coach (4 stars, last pushed 9mo ago), licensed MIT. It adds 887 tokens to every session, about $0.0035 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 2026-08-31.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deno copilot-instructions.md
Copilot instructions for denoland/deno, covering deno development guide for github copilot, network access, high level overview, key directories and key files to understand first.