Batch Evaluation Quick Start Guide

Batch Evaluation Quick Start Guide

Status: Phase 0 Complete ✅ Last Updated: February 11, 2026

Overview

The batch evaluation infrastructure allows you to evaluate multiple skills at once with automatic test data generation, parallel execution, and comprehensive reporting.

Key Features:

• ⚡ Parallel evaluation (5-20 workers)
• 🤖 Automatic test data generation from schemas
• 📊 Aggregate reports (domain-level and session-level)
• 🔍 Failure analysis with actionable recommendations
• 🚀 Full library evaluation in ~3-4 minutes

Installation

Ensure dependencies are installed:

source .venv/bin/activate
pip install -r requirements-test.txt

Quick Start

1. Evaluate a Single Domain

# Evaluate all skills in ecosystem domain
python scripts/batch_eval_skills.py --domain ecosystem --parallel 5

# Output:
# ============================================================
# Batch Skill Evaluation
# ============================================================
# Discovered 16 skills across 1 domains
# [1/16] ✓ ecosystem/elg_mdf_tracker
# [2/16] ✓ ecosystem/elg_partner_tier_manager
# ...
# ============================================================
# Evaluation Complete
# ============================================================
# Total: 16, Successful: 12, Failed: 4
# Success Rate: 75.0%

2. Evaluate Multiple Domains

# Evaluate 3 business-critical domains
python scripts/batch_eval_skills.py \
  --domains ecosystem,marketing,revops \
  --parallel 10

3. Evaluate Specific Skills

# Evaluate just 2 skills
python scripts/batch_eval_skills.py \
  --skills elg_mdf_tracker,elg_partner_tier_manager

4. Analyze Failures

# After batch eval, analyze failures
python scripts/analyze_eval_failures.py \
  --session batch_20260211_171252 \
  --prioritize

# Output:
# ============================================================
# Failure Analysis Complete
# ============================================================
# Total Failures: 4
# Top Categories:
#   unknown: 4
# Action Items: 1
# Report: reports/evals/failure_analysis_batch_20260211_171252.md

Common Workflows

Workflow 1: Evaluate New Domain

# 1. Generate test data only (fast, no evaluation)
python scripts/batch_eval_skills.py --domain marketing --generate-only

# 2. Review generated test data
ls -lh test_cases/

# 3. Run evaluation using generated test data
python scripts/batch_eval_skills.py --domain marketing --use-existing-test-data --parallel 10

# 4. Check results
cat reports/evals/batch_*_aggregate.md

# 5. Analyze failures
python scripts/analyze_eval_failures.py --domain marketing --prioritize

Workflow 2: Re-evaluate After Fixes

# After fixing schema issues, re-evaluate same domain
python scripts/batch_eval_skills.py \
  --domain ecosystem \
  --parallel 10

# Compare with previous session
diff reports/evals/batch_OLD_aggregate.md reports/evals/batch_NEW_aggregate.md

Workflow 3: Full Library Sweep

# Evaluate ALL 765 skills (takes ~3-4 minutes)
python scripts/batch_eval_skills.py \
  --domains ecosystem,marketing,revops,plg,monetization,customer_success,ai_ops,product_ops,support_ops,finops,security,community,compliance,data_ops,people_ops,development,vcf,devex,superpowers,anthropic_official,cursor_rules,plg_frameworks,scientific \
  --parallel 20

# Or use a bash loop
for domain in ecosystem marketing revops plg monetization; do
  python scripts/batch_eval_skills.py --domain $domain --parallel 10
done

Output Files

Test Data

Location: test_cases/

Example: test_cases/elg_mdf_tracker_test_data.json

{
  "skill_id": "elg_mdf_tracker",
  "generated_at": "2026-02-11T17:12:52.129965",
  "num_cases": 3,
  "test_cases": [
    {
      "inputs": {
        "partnerId": "test-abc123",
        "action": "check_budget"
      },
      "label": "valid_case_1",
      "expected_valid": true
    }
  ]
}

Per-Skill Reports

Location: reports/evals/skills/

Example: reports/evals/skills/elg_mdf_tracker_eval.md

Aggregate Reports

Location: reports/evals/

Files:

• batch_{timestamp}_aggregate.md - Human-readable summary
• batch_{timestamp}_aggregate.json - Machine-readable data

Example:

# Batch Evaluation Report: batch_20260211_171252

## Overall Summary

- **Total Skills**: 16
- **Successful**: 12 (75.0%)
- **Failed**: 4 (25.0%)

## Domain Breakdown

| Domain    | Total | Success | Failed | Avg Success Rate | Avg Auto-Act Rate |
| --------- | ----- | ------- | ------ | ---------------- | ----------------- |
| ecosystem | 16    | 12      | 4      | 100.0%           | 90.0%             |

Failure Analysis Reports

Location: reports/evals/

Example: failure_analysis_batch_20260211_171252.md

# Evaluation Failure Analysis

## Summary Statistics

- **Total Failures**: 4

## Action Plan (Prioritized)

1. Schema Missing (Priority 5/5)
   - Fix: Add inputSchema/outputSchema to skill.json
   - Affected Skills: 4

Advanced Options

Parallel Workers

# More workers = faster, but higher CPU usage
python scripts/batch_eval_skills.py --domain ecosystem --parallel 20

Recommendation: Use 5-10 workers on laptop, 20+ on server.

Use Existing Test Data

# Faster (skips test data generation)
python scripts/batch_eval_skills.py --domain ecosystem --use-existing-test-data

Use Case: Re-running evaluation after code changes (not schema changes).

Verbose Output

# Show detailed progress
python scripts/batch_eval_skills.py --domain ecosystem --verbose

Output:

[1/16] ✓ ecosystem/elg_mdf_tracker
    Success: 100.0%, Auto-act: 33.3%

Custom Directories

# Use custom paths
python scripts/batch_eval_skills.py \
  --domain ecosystem \
  --test-data-dir /tmp/test_cases \
  --report-dir /tmp/reports

Troubleshooting

Issue: "Skill not found"

Cause: Skill ID doesn't match directory structure.

Fix: Check skill ID in skill.json matches directory name.

# Find skill directories
find skills-library/ecosystem -name "skill.json" -exec jq -r '.id' {} \;

Issue: "No test cases generated"

Cause: Skill missing inputSchema in skill.json.

Fix: Add inputSchema field:

{
  "id": "my_skill",
  "inputSchema": {
    "type": "object",
    "properties": {
      "action": { "type": "string" }
    },
    "required": ["action"]
  }
}

Issue: "No metrics collected"

Cause: Validation failed (often due to empty inputs).

Fix: Ensure schema has at least one required field, or adjust confidence thresholds.

Issue: Slow evaluation

Cause: Too many workers or CPU-bound.

Fix: Reduce --parallel workers:

python scripts/batch_eval_skills.py --domain ecosystem --parallel 3

Performance Tips

1. Generate Test Data Once

# Generate test data upfront
python scripts/batch_eval_skills.py --domains ecosystem,marketing,revops --generate-only

# Then run multiple evaluations using cached data
python scripts/batch_eval_skills.py --domain ecosystem --use-existing-test-data

2. Evaluate in Batches

# Small batches are easier to debug
python scripts/batch_eval_skills.py --domain ecosystem
python scripts/batch_eval_skills.py --domain marketing
python scripts/batch_eval_skills.py --domain revops

3. Parallel Execution

# Run multiple domains in parallel (separate terminals)
python scripts/batch_eval_skills.py --domain ecosystem &
python scripts/batch_eval_skills.py --domain marketing &
python scripts/batch_eval_skills.py --domain revops &
wait

Integration with Existing Tools

With Single Skill Eval

# Use single-skill eval for detailed debugging
python scripts/run_eval_harness.py eval-skill --skill-id elg_mdf_tracker

# Then batch eval for regression testing
python scripts/batch_eval_skills.py --domain ecosystem

With loom CLI

(Coming soon: loom eval batch --domain ecosystem)

With CI/CD

# GitHub Actions example
name: Eval Harness
on: [pull_request]
jobs:
  eval:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - run: pip install -r requirements-test.txt
      - run: python scripts/batch_eval_skills.py --domain ecosystem

FAQ

Q: How long does full library evaluation take? A: ~3-4 minutes with 20 parallel workers (765 skills @ 0.5 sec/skill).

Q: Can I evaluate skills without implementations? A: Yes! The eval harness validates schemas and generates test data without requiring actual skill logic.

Q: What if my skill has no required fields? A: The generator will create empty inputs {}. Consider adding at least one required field for better validation.

Q: How do I export failures to CSV? A: Use --export flag:

python scripts/analyze_eval_failures.py --session batch_20260211_171252 --export failures.csv

Q: Can I run this in CI/CD? A: Yes! See "Integration with Existing Tools" section above.

Q: What's the difference between batch eval and single eval? A:

• Single eval (run_eval_harness.py): Deep dive on one skill with tracing
• Batch eval (batch_eval_skills.py): Fast validation of many skills

Next Steps

1. Run Phase 1 Pilot (5 domains, 50 skills):

   python scripts/batch_eval_skills.py \
     --domains customer_success,ai_ops,product_ops,support_ops,finops \
     --parallel 10
   

2. Fix schema gaps identified in failures

3. Tune confidence thresholds for better auto-act rates

4. Roll out to all 765 skills in ~10 weeks

Resources

• Phase 0 Completion Report: EVAL_HARNESS_PHASE_0_COMPLETE.md
• Original Rollout Plan: Check project documentation
• Eval Harness README: eval_harness/README.md
• Skills Catalog: skills-library/SKILLS_CATALOG.md

Questions or issues? Open an issue or check the troubleshooting guide.