eval-recipes Runner Skill

Purpose

Run Microsoft's eval-recipes benchmarks to validate amplihack improvements against baseline agents.

When to Use

• User asks to "test with eval-recipes"

• User says "run the evals" or "benchmark this change"

• User wants to validate improvements against codex/claude_code

• Testing a PR branch to prove it improves scores

Capabilities

I can run eval-recipes benchmarks to:

• Test specific amplihack branches

• Compare against baseline agents (codex, claude_code)

• Run specific tasks (linkedin_drafting, email_drafting, etc.)

• Compare before/after scores for PRs

• Generate reports with score improvements

How It Works

Setup (One-Time)

Clone eval-recipes from Microsoft

git clone https://github.com/microsoft/eval-recipes.git ~/eval-recipes cd ~/eval-recipes

Copy our agent configs

cp -r $(pwd)/.claude/agents/eval-recipes/* data/agents/

Install dependencies

uv sync

Running Benchmarks

Test a specific branch:

Update install.dockerfile to use specific branch

Then run benchmark

cd ~/eval-recipes uv run eval_recipes/main.py --agent amplihack --task linkedin_drafting --trials 3

Compare before/after:

Test baseline (main)

uv run eval_recipes/main.py --agent amplihack --task linkedin_drafting

Test PR branch (edit install.dockerfile to checkout PR branch)

uv run eval_recipes/main.py --agent amplihack_pr1443 --task linkedin_drafting

Compare scores

Available Tasks

Common tasks from eval-recipes:

• linkedin_drafting

• Create tool for LinkedIn posts (scored 6.5/100 before PR #1443)

• email_drafting

• Create CLI tool for emails (scored 26/100 before)

• arxiv_paper_summarizer

• Research tool

• github_docs_extractor

• Documentation tool

• Many more in ~/eval-recipes/data/tasks/

Typical Workflow

When user says "test this change with eval-recipes":

• Identify the branch/PR to test

• Update agent config to use that branch:

In .claude/agents/eval-recipes/amplihack/install.dockerfile

RUN git clone https://github.com/rysweet/...git /tmp/amplihack &&
cd /tmp/amplihack &&
git checkout BRANCH_NAME &&
pip install -e .

• Copy to eval-recipes: cp -r .claude/agents/eval-recipes/* ~/eval-recipes/data/agents/

• Run benchmark: cd ~/eval-recipes uv run eval_recipes/main.py --agent amplihack --task TASK_NAME --trials 3

• Report scores and compare with baseline

Expected Scores

Baseline (main branch):

• Overall: 40.6/100

• LinkedIn: 6.5/100

• Email: 26/100

With PR #1443 (task classification):

• Expected: 55-60/100 (+15-20 points)

• LinkedIn: 30-40/100 (creates actual tool)

• Email: 45/100 (consistent execution)

Example Usage

User says: "Test PR #1443 with eval-recipes on the LinkedIn task"

I do:

• Update install.dockerfile to checkout feat/issue-1435-task-classification

• Copy to eval-recipes: cp -r .claude/agents/eval-recipes/* ~/eval-recipes/data/agents/

• Run: cd ~/eval-recipes && uv run eval_recipes/main.py --agent amplihack --task linkedin_drafting --trials 3

• Report results: "Score: 35.2/100 (up from 6.5 baseline)"

Prerequisites

• eval-recipes cloned to ~/eval-recipes

• API key in environment: export ANTHROPIC_API_KEY=sk-ant-...

• Docker installed (for containerized runs)

• uv installed: curl -LsSf https://astral.sh/uv/install.sh | sh

Notes

• Benchmarks take 2-15 minutes per task depending on complexity

• Multiple trials (3-5) give more reliable averages

• Docker builds can be cached for speed

• Results saved to .benchmark_results/ in eval-recipes repo

Automation

For fully autonomous testing:

Test suite for a PR

tasks="linkedin_drafting email_drafting arxiv_paper_summarizer" for task in $tasks; do uv run eval_recipes/main.py --agent amplihack --task $task --trials 3 done

Compare results

cat .benchmark_results//amplihack//score.txt