| name | eval-harness |
| description | Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles. |
| when_to_use | Use when the user wants formal evals, pass/fail regression checks, or eval-driven development for AI behavior. |
Eval Harness Skill
A formal evaluation framework for Claude Code sessions, implementing eval-driven development (EDD) principles.
Philosophy
Eval-Driven Development treats evals as the "unit tests of AI development":
- Define expected behavior BEFORE implementation
- Run evals continuously during development
- Track regressions with each change
Eval Types
Capability Evals
Test if Claude can do something it couldn't before:
[CAPABILITY EVAL: feature-name]
Task: Description of what Claude should accomplish
Success Criteria:
- [ ] Criterion 1
- [ ] Criterion 2
- [ ] Criterion 3
Expected Output: Description of expected result
Regression Evals
Ensure changes don't break existing functionality:
[REGRESSION EVAL: feature-name]
Baseline: git SHA (or a /checkpoint milestone name)
Tests:
- existing-test-1: PASS/FAIL
- existing-test-2: PASS/FAIL
- existing-test-3: PASS/FAIL
Result: X/Y passed (previously Y/Y)
Grader Types
1. Code-Based Grader
Deterministic checks using code:
grep -q "export function handleAuth" src/auth.ts && echo "PASS" || echo "FAIL"
npm test -- --testPathPattern="auth" && echo "PASS" || echo "FAIL"
npm run build && echo "PASS" || echo "FAIL"
2. Model-Based Grader
Have a separate instance (subagent) review open-ended outputs against a checklist:
[MODEL GRADER PROMPT]
Review the following code change and answer each question with YES/NO plus evidence:
1. Does it solve the stated problem?
2. Is it well-structured?
3. Are edge cases handled?
4. Is error handling appropriate?
Verdict: PASS/FAIL
Reasoning: [explanation]
3. Human Grader
Flag for manual review:
[HUMAN REVIEW REQUIRED]
Change: Description of what changed
Reason: Why human review is needed
Risk Level: LOW/MEDIUM/HIGH
Note: statistical metrics like pass@k require running the same task k times independently. If you truly need them, implement an automated script (e.g., a headless claude -p loop), not manual bookkeeping in an interactive session.
Eval Workflow
1. Define (Before Coding)
## EVAL DEFINITION: feature-xyz
### Capability Evals
1. Can create new user account
2. Can validate email format
3. Can hash password securely
### Regression Evals
1. Existing login still works
2. Session management unchanged
3. Logout flow intact
2. Implement
Write code to pass the defined evals.
3. Evaluate
[Run each capability eval, record PASS/FAIL]
npm test -- --testPathPattern="existing"
4. Report
EVAL REPORT: feature-xyz
========================
Capability Evals:
create-user: PASS (attempts: 1)
validate-email: PASS (attempts: 2)
hash-password: PASS (attempts: 1)
Overall: 3/3 passed
Regression Evals:
login-flow: PASS
session-mgmt: PASS
logout-flow: PASS
Overall: 3/3 passed
Status: READY FOR REVIEW
Integration Patterns
Pre-Implementation
/eval define feature-name
Creates eval definition file at .claude/evals/feature-name.md
During Implementation
/eval check feature-name
Runs current evals and reports status
Post-Implementation
/eval report feature-name
Generates full eval report
Eval Storage
Store evals in project:
.claude/
evals/
feature-xyz.md # Eval definition
feature-xyz.log # Eval run history
baseline.json # Regression baselines
Best Practices
- Define evals BEFORE coding - Forces clear thinking about success criteria
- Run evals frequently - Catch regressions early
- Use code graders when possible - Deterministic > probabilistic
- Human review for security - Never fully automate security checks
- Keep evals fast - Slow evals don't get run
- Version evals with code - Evals are first-class artifacts
Example: Adding Authentication
## EVAL: add-authentication
### Phase 1: Define (10 min)
Capability Evals:
- [ ] User can register with email/password
- [ ] User can login with valid credentials
- [ ] Invalid credentials rejected with proper error
- [ ] Sessions persist across page reloads
- [ ] Logout clears session
Regression Evals:
- [ ] Public routes still accessible
- [ ] API responses unchanged
- [ ] Database schema compatible
### Phase 2: Implement (varies)
[Write code]
### Phase 3: Evaluate
Run: /eval check add-authentication
### Phase 4: Report
EVAL REPORT: add-authentication
==============================
Capability: 5/5 passed
Regression: 3/3 passed
Status: SHIP IT