ai-reliability-eval

name	ai-reliability-eval
description	Measures AI system reliability over time by defining pass/fail criteria before implementation, running capability checks, and tracking regression via pass@k metrics. Trigger for 'how reliable is this', 'did my changes break anything', 'measure AI performance', 'define success criteria', 'eval this feature', 'check skill regression'. Not for code correctness; use /ai-test instead. Not for quality gates; use /ai-verify instead — evals measure AI task completion consistency.
effort	mid
argument-hint	define|check|report|regression|--skill-set [feature]
tags	["quality","evals","improvement"]
model_tier	sonnet
mirror_family	antigravity-skills
generated_by	ai-eng sync
canonical_source	.claude/skills/ai-reliability-eval/SKILL.md
edit_policy	generated-do-not-edit

Reliability Eval

Eval-Driven Development (EDD): evals are the unit tests of AI development. Define pass/fail criteria before writing code; measure AI reliability with pass@k metrics; track regressions across prompt, agent, and model changes. Evals answer "Can the AI do this reliably?" — distinct from /ai-verify, which checks current code quality (linting, coverage, security).

Workflow

Principles applied: §10.5 TDD — EDD mirrors TDD: define the pass/fail criteria BEFORE the code, then let evals drive iteration; writing evals after implementation is the same anti-pattern as tests-after.

Mode	When	Action
define	before implementation	write capability + regression evals, set pass@k targets
check	during implementation	run current evals, report pass@k, list failures
report	after implementation	run all evals, compute pass@k + pass^k, render report
regression	prompt/agent/model change	run regression evals vs baseline, flag degradation
--skill-set	PR touching .agents/skills/**	optimizer over each .ai-engineering/evals/<skill>.jsonl, gate pass@1 vs baseline.json

--skill-set combined with --regression fails on >5 pp pass@1 drop (sub-007 M6, D-127-07). Wired into .github/workflows/skill-evals.yml.

Mode: define (before coding)

1. Identify the capability being built or changed.
2. Write capability evals (can the AI do this new thing?).
3. Write regression evals (do existing things still work?).
4. Set success metrics (pass@k targets).
5. Store at .ai-engineering/evals/<feature-name>.md:

## 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

### Success Metrics
- pass@3 > 90% for capability evals
- pass^3 = 100% for regression evals

Mode: check (during implementation)

1. Read the eval definition from .ai-engineering/evals/<feature-name>.md.
2. Run each capability eval; record PASS/FAIL.
3. Run regression evals via existing test suites.
4. Report current status with pass@k counts.
5. Identify which evals still fail and why.

Mode: report (after implementation)

Run all capability + regression evals (per check), compute pass@k + pass^k, render, then store at .ai-engineering/evals/<feature-name>.log:

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

Mode: regression

Baseline is created automatically on the first report run — if baseline.json does not exist, the current run becomes the initial baseline.

1. Load baseline from .ai-engineering/evals/baseline.json.
2. Run all regression evals against current state.
3. Compare against baseline results.
4. Flag any degradation:

[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

Grader	How it works	When to use	Example
Code	Deterministic checks (grep, test runners, build)	Verifiable / structured outputs	grep -q "export function handleAuth" src/auth.ts && echo "PASS"
Model	Claude evaluates open-ended output (score 1-5)	Prose quality, code style, creative output	Prompt: "Does it solve the stated problem? Score 1-5"
Human	Flag for manual review with risk level	Security decisions, UX judgment, ambiguous cases	[HUMAN REVIEW REQUIRED] Risk Level: HIGH

Metrics

Metric	Definition	Typical target
pass@1	First attempt success rate	Varies by difficulty
pass@3	At least one success in 3 attempts	> 90%
pass@k	At least one success in k attempts	Depends on criticality
pass^3	All 3 trials succeed	100% for critical paths
pass^k	All k trials succeed	Use for regression evals

Storage

.ai-engineering/evals/
  <feature-name>.md      # Eval definition
  <feature-name>.log     # Eval run history
  baseline.json          # Regression baselines

Best Practices

• 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.

Common Mistakes

• Skipping define and writing evals after implementation (tests-after anti-pattern).
• Setting pass@1 targets too high for genuinely hard tasks (use pass@3 instead).

Examples

Example — define evals before implementing

User: "I'm about to add a new auth flow. Define evals first."

/ai-reliability-eval define auth-flow

Walks through capability evals (can-create-account, can-validate-email, can-hash-password) and regression evals (login-still-works), sets pass@3 targets, writes .ai-engineering/evals/auth-flow.md.

Integration

Called by: user directly, /ai-build, /ai-verify (regression mode). Calls: test runners (code graders), the model (model graders), stack-specific tools. See also: /ai-test (code correctness), /ai-verify (current quality gates).

$ARGUMENTS