AI Eval Review

AI Eval Review

Audit the eval layer of an AI product or feature against a structured set of design-completeness questions. Each of the seven blocks is a gap detector — the value of this skill is whether the elicitation surfaces eval decisions the builder hasn't made yet, not whether the artifact looks complete.

The job is eval-design-completeness: have we designed how we'll know if this works? Covers offline criteria, ground-truth quality, online signal, cohort breakdowns and disparate impact, adversarial / robustness coverage, and drift detection. Regulatory rigor (EU AI Act, FDA SaMD, FTC) is a cross-cutting lens applied across blocks, not a separate block.

This skill is the eval-side companion to ai-ux-review. Same shape, same elicitation pattern, different subject — ai-ux-review audits the human-AI design surface (was the experience intentionally designed?); this skill audits the measurement layer behind it (do we have signal for whether the design works?).

This is not an implementation tool. It does not write eval code, label datasets, set up monitoring dashboards, or compute metrics. It names the gaps; you take them to your tools or your team to close.

What this skill produces

Always produced under the resolved review root (default docs/ai-ux/ — same folder as ai-ux-review for clean composition):

1. ai-eval-review.md — canonical, editable Markdown with one top-level section per block (seven blocks total), plus a ## Gap Summary section listing the eval decisions still unmade. Headings are load-bearing if downstream tools (e.g., a future eval-plan or eval-task generator) need to grep them.
2. ai-eval-review.html — single self-contained HTML rendering the seven blocks visually, with [GAP] chips on blocks with unresolved decisions and a Gap Summary footer. Opens in any browser, prints cleanly to PDF, zero network dependencies.

Both files carry the same content — the HTML is the visual primary; the Markdown is the source of truth.

What this skill is NOT

• Not an eval implementation tool. Block 1 names what success means; Block 3 names how you'd measure it offline. The skill does not write the code, build the labeling pipeline, configure W&B / Evidently / Arize, or compute the metric. For implementation, point at HELM, Anthropic's eval cookbook, OpenAI Evals, or your team's preferred eval platform.
• Not a benchmark suite. It doesn't run MMLU, HELM, or your custom eval set. It helps you decide which benchmark or custom set fits your problem.
• Not a substitute for ai-ux-review. UX design and eval design are different surfaces. A product can have great evals on a poorly designed UX (users distrust accurate outputs); it can also have great UX on poorly evaluated AI (users trust outputs that are silently wrong). Both matter; use the two skills together.
• Not a regulatory compliance assessment. It surfaces regulatory hooks (EU AI Act, FDA SaMD, FTC AI guidance) as a cross-block lens, but does not certify compliance. For formal regulatory submissions, escalate to team-composer with @legal_compliance_advisor.
• Not a model-quality benchmark. It doesn't tell you whether your model is good. It tells you whether you have the design in place to know whether your model is good.
• Not a pitch-deck builder. Pitch construction belongs to pitch-deck.

Skill Boundaries

If both skills are run, treat them as a pair: ai-ux-review asks "did we design for X?", ai-eval-review asks "do we measure X?". The boundary is design vs. measurement.

When ai-ux-review.md already exists in the working folder, this skill reads it as context — Block 7 (Success & Evaluation) gaps seed Block 1 here. Don't re-elicit what's already been named.

Influences. This skill is informed by several open eval frameworks and regulatory texts:

• Holistic Evaluation of Language Models (HELM) — Stanford CRFM, Apache 2.0. Multi-dimensional eval (accuracy, calibration, robustness, fairness, bias, toxicity, efficiency).
• Anthropic's claude-cookbooks — MIT. Patterns for designing and running LLM evals.
• OpenAI Evals — MIT. Framework + registry of benchmarks.
• GoogleChrome/modern-web-guidance-src — Apache-2.0. Closed-loop calibration methodology: paired gold-standard + negative fixtures to prove a test discriminates the rule it claims to test, plus opportunity (100% − unguided-pass) and uplift (guided − unguided) vocabulary for measuring whether a feature actually helps. Applied here to Block 3 (offline eval set construction must include hard negatives) and Block 4 (online metrics should be framed against an unguided baseline when feasible).
• EU AI Act — regulatory text, reusable with attribution. High-risk system requirements drive Block 5 cohort + Block 6 robustness probes.
• FTC AI guidance, FDA SaMD eval expectations — US federal works, public domain.

The skill uses general eval-design concepts (the kinds of decisions to make, not specific benchmark scores) and authors its own elicitation flow, probes, and acceptance criteria. No verbatim content lifted from any of the above sources. See README.md for the full influences note and the copyright-vs-derivative-work reasoning.

For the gold+negative fixture pattern in concrete form, cross-reference skill-evaluator/references/calibration-loop.md rather than duplicating the methodology here.

The Seven Blocks

Claude must internalize all seven before interviewing. Blocks follow the reasoning order from "what is success?" → "what do we measure against?" → "how do we measure?" → "what do we measure in production?" → "who does it work for?" → "how does it fail?" → "how do we know it's still working?".

Cross-block lens — Regulatory rigor. EU AI Act (high-risk systems), FDA SaMD (risk-class-proportional eval), FTC AI guidance (disparate impact, deception). Not its own block — applied as a Phase 2 check across the seven, surfacing where regulatory rigor changes the bar for Blocks 1, 5, and 6 specifically.

See references/blocks/01-necessity-success.md through references/blocks/07-drift-monitoring.md for each block's deep probe questions, acceptance criteria, and common gap patterns. The skill body uses these references lazily — read them when the phase calls for them, not all up front.

Phase 0: Intake (RUN FIRST, BEFORE ANY BLOCK WORK)

Goal: establish context — what's being reviewed, what AI type, what lifecycle stage, what regulatory context, what adjacent artifacts exist.

Step 0.1 — Resolve the review root

Look for these in order; first match wins:

1. Explicit output_dir arg → use as-is.
2. STARTUP_KIT_DOCS_ROOT env var → ${STARTUP_KIT_DOCS_ROOT}/ai-ux/.
3. Smart default — docs/startup-kit/ exists → docs/startup-kit/ai-ux/.
4. Smart default — docs/ai-ux/ exists → docs/ai-ux/ (sibling of an existing ai-ux-review run).
5. Solo fallback → docs/ai-ux/.

Both ai-ux-review and ai-eval-review share the ai-ux/ folder by convention — the human-AI design layer and its measurement layer belong side-by-side.

If ai-eval-review.md already exists at the resolved root, this is an update-mode run — see "Update mode" below.

Step 0.2 — Scan for adjacent artifacts

Before asking questions, check for files this skill can read as context:

• <resolved-root>/ai-ux-review.md — if present, read it. Block 7 (Success & Evaluation) is the load-bearing input: any [Gap — …] markers there seed this skill's Block 1. Mirror back the gaps and ask: "ai-ux-review flagged these eval gaps: [list]. Should I seed Block 1 from them, or are you starting fresh?"
• <resolved-canvas-root>/validation-canvas.md — if present, this is an AI startup. Block 5 (Cohort breakdown) reads Customer Segments to anchor per-segment eval questions.
• <resolved-brand-root>/DESIGN.md — for HTML token styling, same pattern as ai-ux-review.
• Eval plan / metric dashboard description / labeling guide — if attached, read it. Don't re-ask for information already there.

Step 0.3 — Establish four intake facts

Ask only the ones not already obvious from context:

1. What's the AI doing? One sentence. (Pulled from ai-ux-review.md Block 1 if it exists.)
2. What kind of AI? LLM, classical ML (classification / ranking / regression / recommendation), CV, multi-modal, agentic. This determines which Block 6 probes apply.
3. Where in the lifecycle? Pre-MVP / prototype / in development / shipped. Sets eval-rigor expectations.
4. What regulatory context? Unregulated / consumer-trust-sensitive / EU AI Act limited-risk / EU AI Act high-risk / FDA SaMD (any class) / FTC consent decree adjacent. This drives Phase 2's regulatory cross- check.

Step 0.4 — Confirm framing in one line

Before proceeding to block work, mirror back the framing:

"Reviewing the eval layer for an LLM-powered email-draft feature, in development, no formal regulatory regime. Reading ai-ux-review.md Block 7 gaps — there are three. I'll seed Block 1 from them. Stop me if I should reframe."

Then proceed to Phase 1.

Hard rules for Phase 0

• Never start block work without the four intake facts (or their context-inferred equivalents).
• Never re-elicit ai-ux-review Block 7 gaps when the file is present. Read them. Mirror them back as Block 1 seeds. Confirm.
• Never invent the regulatory context. Ask. Misclassifying an EU AI Act high-risk system as unregulated invalidates the rest of the review.

Phase 1: Block-by-Block Elicitation

Goal: Walk the seven blocks in order. For each, the output is either a concrete eval decision or an explicit gap marker ([Gap — what hasn't been decided yet]).

Role setup

Block reasoning order (mandatory)

Do not jump around. Each block sets up constraints later blocks inherit.

1. Eval necessity & success definition (anchors everything — what is the AI supposed to do, and what does "doing it" look like)
2. Ground truth & label quality (the measurement substrate — without ground truth, nothing else has meaning)
3. Offline eval design (pre-launch rigor — composition, coverage, leakage, power)
4. Online metrics & signal (production rigor — direct vs. proxy, failure signal)
5. Cohort breakdown & disparate impact (per-segment performance — fairness, harm distribution)
6. Adversarial & robustness (failure modes — red team, injection, OOD, drift-resistance)
7. Drift detection & monitoring (continued vigilance — alerting, retraining cadence)

Per-block protocol

For each block:

1. State the block in one sentence from references/blocks/<NN>-<name>.md.
2. Ask the block's primary probe (one question). Probes are in the reference file.
3. Listen for specificity. "We measure accuracy" is not specific. "Token-level F1 on our held-out test set of 800 hand-labeled examples" is. Push back on category answers.
4. Walk secondary probes if the primary was thin.
5. Apply acceptance criteria. Each block has 3–5. Note met vs. gap.
6. Mark gaps explicitly. [Gap — what hasn't been decided: <what>].

What "enough" looks like

Each block has at least one specific, testable eval decision OR an explicit gap marker. "We'll figure out metrics later" is a gap, not an answer.

What to do when the builder doesn't know

Mark [Gap — <what hasn't been decided>: <why it matters>]. A review with honest gaps is more useful than one with invented confidence.

Pacing

Full seven-block walk takes 30–60 minutes depending on AI type and lifecycle stage. Single-block work is ~10 minutes — but still invoke the skill, since adjacent blocks stress-test the one being reviewed.

Phase 2: Cross-Block Stress Test

Goal: Surface contradictions, dependencies, and gaps that only show up when blocks are read together. Six mandatory checks plus the regulatory cross-cutting lens.

1. Necessity ↔ Online metrics. If Block 1 says success is "users find drafts useful," does Block 4 measure that, or measure a proxy? Proxy metrics treated as direct are the dominant failure mode here.
2. Ground truth ↔ Offline eval. If Block 2 says ground truth is hand-labeled with single annotator, does Block 3 acknowledge the resulting label-noise ceiling on offline eval signal?
3. Cohort ↔ Ground truth. Are per-segment evals in Block 5 actually labeled per-segment (Block 2)? Aggregate ground truth can hide segment-level imbalance.
4. Adversarial ↔ Output integrity (ai-ux-review Block 6 boundary). If ai-ux-review.md exists and Block 6 names prompt-injection mitigations, does this skill's Block 6 actually measure injection resistance? Designed mitigations without eval signal are theater.
5. Drift ↔ Online metrics. If Block 7 says drift is detected via metric Z, is Z actually one of the online metrics in Block 4? A drift signal that lives nowhere in production observability is aspirational.
6. All blocks ↔ Lifecycle stage. Pre-MVP gaps in Block 5 (cohort eval) are different urgency than post-launch gaps in Block 5. Tag urgency by lifecycle.

Regulatory cross-cutting lens (mandatory when regulatory context is non-trivial): EU AI Act high-risk requires data quality, human oversight, accuracy + robustness, and post-market monitoring — these map to Blocks 2, 4, 6, 7 respectively. FDA SaMD ties eval-rigor to risk class. FTC AI guidance emphasizes disparate impact (Block 5) and truthfulness (Block 6). Surface where regulatory rigor changes the bar, not just where regulators have an opinion.

Output of Phase 2

Append a ## Gap Summary section with the 3–5 most urgent unmade eval decisions. For each:

• The gap in plain language ("No ground-truth labels for refusal cases")
• Why it matters ("Block 1 said refusal-handling is part of success; Block 5 says cohort 'declined requests' has no metrics; without labels, neither can be measured")
• The cheapest experiment to resolve ("Label 100 refusal cases this week with 2 annotators; if agreement >0.7, proceed to scale; if lower, the refusal taxonomy itself is the gap")

Phase 3: Render & Ship

Same structure as ai-ux-review. Produce two files at the resolved root, present via present_files or computer:// links, close with three lines.

Step 1 — Produce ai-eval-review.md

Structure (headings must match exactly):

# AI Eval Review — [Product / Feature Name]

> Generated on [YYYY-MM-DD]. AI type: [LLM | classical-ML | CV | multi-modal | agentic]. Lifecycle: [pre-MVP | prototype | development | shipped]. Regulatory: [unregulated | consumer-trust-sensitive | EU AI Act limited-risk | EU AI Act high-risk | FDA SaMD class N | FTC adjacent].

## Block 1 — Eval Necessity & Success Definition
- ...

## Block 2 — Ground Truth & Label Quality
- ...

## Block 3 — Offline Eval Design
- ...

## Block 4 — Online Metrics & Signal
- ...

## Block 5 — Cohort Breakdown & Disparate Impact
- ...

## Block 6 — Adversarial & Robustness
- ...

## Block 7 — Drift Detection & Monitoring
- ...

---

## Gap Summary

1. **[Gap in one line]**
   - Why it matters: ...
   - Cheapest experiment to resolve: ...

Step 2 — Produce ai-eval-review.html

Read the template at templates/ai-eval-review.html and produce a single self-contained HTML that:

• Renders the seven blocks as cards in a 3+3+1 grid (mirrors ai-ux-review for visual parity when both reviews exist for the same product).
• Marks any block with unresolved gaps using a [GAP] chip.
• Reads brand tokens from <resolved-brand-root>/DESIGN.md if present.
• Includes Gap Summary footer.
• Prints cleanly to PDF via CSS paged media.
• Carries zero network dependencies, no localStorage.

Step 3 — Save to the resolved review folder

• <review-root>/ai-eval-review.md
• <review-root>/ai-eval-review.html

Side-by-side with ai-ux-review.md + ai-ux-review.html when present.

Step 4 — Present to the user

End with three lines:

1. "The unresolved eval decision with the highest urgency is: …"
2. "The cheapest experiment to resolve it is: …" — typically one of: label N examples, run baseline against eval set, instrument metric Y, add cohort segmentation, run red-team for failure mode Z.
3. "Next step: …" — pick from: "implement eval pipeline for metric X using your team's tools (HELM / W&B / Evidently / custom)," "if regulatory rigor is in scope, escalate to team-composer with @legal_compliance_advisor for compliance pass," "if ai-ux-review hasn't been run for this product, run it next to close the design side."

Update mode (loop-back)

When ai-eval-review.md already exists:

1. Read the existing file first. Don't overwrite blocks the builder hasn't asked to change.
2. Confirm scope. "Your last review had gaps in Block 5 (Cohorts) and Block 7 (Drift). Update those, or re-run the full seven?"
3. Apply the same rigor. Glib revisions get push-back.
4. Mark changes visibly. <!-- updated YYYY-MM-DD: <reason> -->.

Output Files

<review-root>/ai-eval-review.md     Canonical, editable source of truth
<review-root>/ai-eval-review.html   Self-contained visual review (primary deliverable)

No other files.

Quality Checklist

Phase 0 (Intake)

• Review root resolved (shared with ai-ux-review when present)
• Adjacent artifacts scanned (ai-ux-review.md, validation-canvas.md, DESIGN.md, attached eval plan)
• Four intake facts established (AI does what / type / lifecycle / regulatory)
• Framing confirmed in one line
• If ai-ux-review.md Block 7 has gaps, they're surfaced and Block 1 is seeded from them

Content (per block)

• Every block has at least one specific, testable eval decision OR an explicit [Gap — ...] marker
• Specificity gate enforced — "we measure accuracy" is rejected
• Acceptance criteria checked per block
• Block 6 probes applied to the AI type (LLM-specific for LLM products, classical-ML for tabular, etc.)
• Block 1's success definition is distinct from Block 4's online metric (one is the target, the other is the signal — proxy vs. direct named)

Cross-block (Phase 2)

• All six cross-block checks run
• Regulatory lens applied if context is non-trivial
• Gap Summary has 3–5 gaps with why-it-matters and cheapest experiment
• Gaps tagged by urgency for the lifecycle stage

Rendering

• Exact heading structure (load-bearing for any future composability)
• HTML opens in a browser, prints cleanly to PDF
• Brand tokens applied if DESIGN.md present
• Zero network deps, no localStorage

Shipping

• Both files saved to resolved review folder (sibling of ai-ux-review.md when present)
• Files presented via present_files or computer:// links
• Response ends with the three lines

Cross-Skill Integration

Principle: this skill owns the eval-design-completeness artifact — whether the measurement layer of an AI product has been intentionally designed. It does not implement the eval (that's the team's eval platform), validate beliefs (that's validation-canvas), test hypotheses (riskiest-assumption-test), pitch the product (pitch-deck), adversarially probe it (startup-grill), or audit the human-AI design surface (that's ai-ux-review — the sibling skill).

Graceful degradation: if a referenced skill is not installed, this skill still ships ai-eval-review.md + .html. Cross-skill chains are enhancements, not requirements.

Reference files

• references/blocks/01-necessity-success.md — what success means; offline criterion; AI-type-specific success patterns
• references/blocks/02-ground-truth-labels.md — label sources; quality; coverage gaps; inter-annotator agreement
• references/blocks/03-offline-eval-design.md — eval set composition; distribution coverage; leakage protection; statistical power
• references/blocks/04-online-metrics-signal.md — production metrics; proxy vs. direct; failure signal vs. engagement
• references/blocks/05-cohort-disparate-impact.md — per-segment performance; fairness; harm distribution; regulatory hooks
• references/blocks/06-adversarial-robustness.md — red-team coverage; prompt injection; OOD detection; jailbreak resistance; classical-ML adversarial
• references/blocks/07-drift-monitoring.md — model / behavior / data drift; alerting; retraining cadence; monitoring infrastructure

Read these when the phase calls for them. Do not front-load all references at once — that's the progressive disclosure pattern this repo uses (see CLAUDE.md → "Harness vocabulary").

Tags: ai, eval, evaluation, mlops, responsible-ai, fairness, drift, llm, design-review