uzysjung Eval Harness Skill
A formal evaluation framework for Claude Code sessions, implementing eval-driven development (EDD) principles.
When to Activate
- Setting up eval-driven development (EDD) for AI-assisted workflows
- Defining pass/fail criteria for Claude Code task completion
- Measuring agent reliability with pass@k metrics
- Creating regression test suites for prompt or agent changes
- Benchmarking agent performance across model versions
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
- Use pass@k metrics for reliability measurement
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: SHA or checkpoint 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:
# Check if file contains expected pattern
grep -q "export function handleAuth" src/auth.ts && echo "PASS" || echo "FAIL"
# Check if tests pass
npm test -- --testPathPattern="auth" && echo "PASS" || echo "FAIL"
# Check if build succeeds
npm run build && echo "PASS" || echo "FAIL"
2. Model-Based Grader
Use Claude to evaluate open-ended outputs:
[MODEL GRADER PROMPT]
Evaluate the following code change:
1. Does it solve the stated problem?
2. Is it well-structured?
3. Are edge cases handled?
4. Is error handling appropriate?
Score: 1-5 (1=poor, 5=excellent)
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
Metrics
pass@k
"At least one success in k attempts"
- pass@1: First attempt success rate
- pass@3: Success within 3 attempts
- Typical target: pass@3 > 90%
pass^k
"All k trials succeed"
- Higher bar for reliability
- pass^3: 3 consecutive successes
- Use for critical paths
Eval Workflow
1. Define (Before Coding)
Write the spec to a file (.claude/evals/<feature>.md) before implementing. Give every eval a
stable ID — C1..Cn for capability, R1..Rn for regression — so the same identifier carries
from definition to the post-implementation status line, and a reviewer can check them off one by
one. Prose-only lists ("Can create new user account") can't be referenced or scored.
# EVAL: <feature name> (<phase/PR>)
**Feature**: <one line>
**Baseline**: commit <sha> # what "regression" is measured against
**Target**: pass@1 = 100% for capability evals
## Capability Evals
### C1: <name>
- <concrete, checkable expectation — inputs → expected output>
### C2: <name>
- <expectation>
## Regression Evals
### R1: <existing behavior that must not move>
- <expectation>
## Test Command
```bash
pytest tests/test_<area>.py -v -k "<selector>"
```
## Status (after implementation)
- C1-Cn: PASS via <tests (N개) / route check / manual>
- R1-Rn: PASS
**Overall: pass@1 = <x>%**
Two fields carry most of the weight. Baseline commit makes "regression" falsifiable — without it, R-evals are opinions about the past. Test Command makes the spec re-runnable by someone who didn't write it; an eval nobody can re-run is documentation, not a gate.
Fill the Status section after implementing, in the same file. A spec whose status is still empty at merge time means the evals were written and never used.
2. Implement
Write code to pass the defined evals.
3. Evaluate
# Run capability evals
[Run each capability eval, record PASS/FAIL]
# Run regression evals
npm test -- --testPathPattern="existing"
# Generate report
4. Report
EVAL REPORT: feature-xyz
========================
Capability Evals:
create-user: PASS (pass@1)
validate-email: PASS (pass@2)
hash-password: PASS (pass@1)
Overall: 3/3 passed
Regression Evals:
login-flow: PASS
session-mgmt: PASS
logout-flow: PASS
Overall: 3/3 passed
Metrics:
pass@1: 67% (2/3)
pass@3: 100% (3/3)
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 (.md + .log Pair Format)
각 평가 항목은 <topic>.md (설계) + <topic>.log (실행 결과) 쌍으로 저장. 강제. 단독 .md만 있으면 재현 불가.
.claude/
evals/
feature-xyz.md # Eval definition (Capability/Regression/Test 3섹션 필수)
feature-xyz.log # Eval run history (실행 시각, grader, pass/fail)
session-YYYYMMDD.md # 세션 단위 회고 + 차기 backlog
session-YYYYMMDD.log # 동일 세션의 grader 출력
baseline.json # Regression baselines (선택)
eval 산출물은
docs/evals/*.{md,log}로 모은다 — 실행 로그와 판정을 같은 자리에 둔다.
.md 파일 의무 섹션 (3개)
# Eval: <topic>
## Capability
[새 능력 — Claude/agent가 무엇을 할 수 있는지]
- AC: [측정 가능 기준]
- Grader: code-based / model-based / human
## Regression
[기존 기능 보호 — 변경으로 깨지면 안 되는 baseline]
- Baseline: <SHA or checkpoint>
- Tests: [목록]
## Test
[실행 절차 — 누가 다시 돌려도 동일 결과 나와야 함]
- Setup: [사전 조건]
- Run: `bash run-eval.sh <topic>` 또는 명시적 명령
- Expected: [기대 출력]
.log 파일 형식
각 실행마다 append. 시간순 누적.
=== 2026-04-19 14:32 (run #1) ===
Capability: 3/3 PASS (pass@1)
Regression: 5/5 PASS (pass^3)
Status: SHIP READY
=== 2026-04-20 09:15 (run #2 — after refactor) ===
Capability: 3/3 PASS
Regression: 4/5 PASS (login-flow regressed at SHA abc123)
Status: BLOCKED — fix login-flow first
Best Practices
- Define evals BEFORE coding - Forces clear thinking about success criteria
- Run evals frequently - Catch regressions early
- Track pass@k over time - Monitor reliability trends
- 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 (pass@3: 100%)
Regression: 3/3 passed (pass^3: 100%)
Status: SHIP IT