04 AI Eval Harness Spec

AI Eval Harness Spec Skill

Use When

• Produce or update AI evaluation specification from approved project evidence.
• Resolve decisions about representative datasets, evaluators, thresholds, calibration, regression gates, and evidence.
• Prepare a reviewable handoff for AI engineering and release teams.

Do Not Use When

• The task is primarily owned by test-plan; route there and use this skill only for its named output.
• Required project evidence or decision authority is unavailable and the requester expects a pass, release, certification, or production change.

Required Inputs

Outputs

Evidence Produced

Capability and Permission Boundaries

• Minimum capabilities: read and search the authorised project sources. Execution is optional and limited to non-destructive validation.
• Assessment and planning default to read-only. Create or edit the named project document only when the request explicitly authorises it. Production mutation, publishing, destructive action, spending, external communication, or certification claims require separate explicit authority.
• Treat secrets, tenant data, incident evidence, and financial records as least-privilege inputs; expose only the minimum evidence needed for review.

Degraded Mode

If files, execution, network, rendering, environment access, fonts, or current evidence are unavailable, return the narrowest useful draft plus a gap register. Label affected checks not assessed, retain the intended acceptance oracle, and state who must supply or verify the missing evidence. Never convert an unavailable check into a pass.

Decision Rules

Workflow

1. Confirm the named deliverable, consumer, scope, environment, authority, and neighbouring-skill boundary.
2. Inventory required sources and validate provenance, freshness, internal consistency, and missing inputs. Stop the affected branch on a mandatory gap.
3. Extract traceable requirements, invariants, risks, and measurable acceptance criteria; record conflicts before choosing a design or procedure.
4. Apply the decision rules and the domain workflow below. For a failed branch, preserve evidence, choose the documented recovery path, or escalate to the named owner.
5. Draft the artefact, decision register, and evidence record together. Do not defer failure handling, rollback, security, tenancy, accessibility, or operational ownership.
6. Run available checks, review every result, repair failures, and hand off only when acceptance is observable. If recovery fails or authority is exceeded, stop and escalate without mutation.

Quality Standards

• Ground every section in a named project source, decision, measured result, or accountable owner.
• Give each requirement or procedure a deterministic oracle that another reviewer can reproduce.
• Keep assumptions, exclusions, degraded checks, residual risks, and waivers visible at handoff.
• Preserve the domain invariants and more specific controls in the existing workflow below; this contract does not replace them.
• Run the repository anti-AI-slop gate: remove filler, verify named standards and dependencies, and retain purposeful domain detail.

Anti-Patterns

• Copying a generic template without mapping it to project sources. Fix: attach each section to an approved requirement, configuration, risk, or owner.
• Choosing a threshold because it is common practice. Fix: derive it from a requirement, measured baseline, risk decision, or current verified source.
• Reporting an inaccessible or unexecuted check as passed. Fix: mark it not assessed, preserve the oracle, and name the verifier.
• Mixing the neighbouring test-plan concern into this artefact without a boundary. Fix: cross-reference its output and keep ownership explicit.
• Omitting failure, rollback, empty-state, security, tenancy, or escalation behaviour. Fix: specify the trigger, safe action, verification, and owner for each applicable case.
• Mutating a repository, environment, tenant, ledger, or external system while drafting guidance. Fix: remain read-only until the exact mutation and authority are explicit.
• Claiming compliance, certification, readiness, or release from prose alone. Fix: require source-attributed evidence and a named acceptance decision.

Worked Example

Given an approved project source and a conflicting implementation detail, record both with provenance, stop the affected branch, and obtain the accountable owner's decision. Then update the relevant contract, define a reproducible acceptance check, and retain its observed result. The artefact is accepted only when each evaluated behaviour has a representative dataset, deterministic scoring rule, threshold rationale, and retained run evidence.

References

• AI evaluation release evidence

• logic.prompt - load only when its template, logic, or detail is needed.

• README.md - load only when its template, logic, or detail is needed.

Overview

The eval harness is to AI features what unit + integration + load tests are to deterministic software: the test layer the team owns and the CI runs. This skill produces the spec.

Core Instructions

Step 1: Inventory eval suites per AI feature

For every AI FR, declare:

• Golden set (success behaviour).
• Adversarial / red-team set (failure modes; cross-link to red-team plan).
• Judge-LLM rubric.
• Calibration set (held-out examples scored by humans to verify the judge).

Step 2: Golden set construction

Provenance rule: golden examples come from production traffic snapshots, design-partner samples, or expert authorship. Each example is labelled by a named labeller. Class balance is documented. Sets are versioned.

For each example: input, expected output (or expected shape + acceptance rubric), category tag, locale, sensitivity flag.

Step 3: Metrics and thresholds

Per feature, choose metrics from:

Step 4: Judge-LLM patterns

The judge is a different model from the system under test. Rubric is short and discrete; pairwise judging beats absolute judging for noisy criteria. The judge is itself calibrated against human labels on a small set; drift in judge scoring triggers re-calibration.

Step 5: CI gate

State the gate rule: a PR cannot merge if the regression on the affected feature's golden set drops > N percentage points (typical N = 2 pp) or if any toxicity / safety metric becomes non-zero.

Step 6: Scheduled regression

Nightly golden run; weekly full red-team run. Score history is plotted; drops trigger SEV3.

Step 7: A/B prompt eval

For prompt changes that pass CI, run side-by-side eval across both prompts on the golden + production-snapshot set; require the new prompt to win on the primary metric without regressing the safety metric.

Step 8: Operational ownership

The eval harness is owned by the AI lead with a named back-up. Dataset versioning, judge model + version, and rubric versions are tracked. Eval-set changes go through PR with sign-off.

Step 9: Write the spec

AI_Eval_Harness_Spec.md sections: 1) Per-feature Suite Inventory, 2) Golden Set Construction, 3) Metrics & Thresholds, 4) Judge-LLM Patterns, 5) CI Gate, 6) Scheduled Regression, 7) A/B Prompt Eval, 8) Operational Ownership, 9) Traceability.

Standards

• OpenAI Evals
• promptfoo / Anthropic eval guide
• NIST AI RMF MEASURE
• ISO/IEC 42001 Clause 9 (performance evaluation)