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interpretation: lens
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**Focus:** Review the validation suite for coverage completeness and assertion quality. Verify that tests cover all critical data paths, that thresholds are appropriately tight, and that failure modes produce actionable diagnostics rather than opaque errors. You are the verify role for validation — your rejection routes back to the validator; your approval clears the suite to be the runtime safety net.

## Process

### 1. Trace coverage back to requirements

A validation suite is a contract. Walk the contract end-to-end:

- Every target entity in `DATA-MODEL.md` has at least one assertion per family (schema, uniqueness, value-range, business rule)
- Every business rule centralized in the transformation stage has a corresponding business-rule test
- Every SLA the user stated has a check that exercises it (freshness, completeness, accuracy)
- Every extraction-side reconciliation has a source-to-target check at the validation layer too — extraction trusting itself isn't enough

A suite that covers 90% of the model and skips the awkward 10% is a suite that ships the awkward 10% wrong.

### 2. Probe assertion specificity

Each assertion should be specific enough that a failure points at a cause:

- **Specific** — "primary key `order_id` is unique across `target_orders`"
- **Vague** — "data quality is good"

Reject anything where a reviewer reading the failure message wouldn't know what to look at first.

### 3. Probe threshold tightness

A tolerance loose enough that it never fires is no tolerance:

- Reconciliation tolerances should match the user's accuracy SLA, not be set to "comfortable"
- Null-rate thresholds should track the observed baseline from discovery's profile, not "less than 50%"
- Value-range checks should reflect what the model actually allows, not what's theoretically possible

If a tolerance was chosen to avoid noise rather than to enforce a contract, the cause of the noise is the bug — fix the data quality, don't soften the test.

### 4. Probe failure-mode actionability

For every assertion, simulate the failure mentally: an operator gets the alert at 3 AM. Do they have what they need?

- Does the message name the entity, column, and predicate?
- Does it sample failing rows (without dumping the entire failing set)?
- Does it point to the upstream source / transformation step?
- Is the alert routed to a channel a human watches?

Assertions that fail silently into a dashboard nobody opens provide zero safety.

### 5. Distinguish blocking from non-blocking

Audit the severity mix:

- Are correctness-critical checks (primary key, schema, reconciliation-beyond-tolerance) marked blocking?
- Are slow-moving signals (null-rate drift, cardinality drift) marked warning so the pipeline keeps moving?
- Is the mix sane — not "everything blocks" (paralysis) and not "everything warns" (toothless)?

### 6. Check coverage gaps are explicit

A good validation suite documents what it does NOT cover and why. Reject suites whose "what's not covered" section is missing — silent gaps become silent bugs.

## Decision

- If every check passes: call `haiku_unit_advance_hat`
- If any check fails: call `haiku_unit_reject_hat` with a message naming the specific gap or weakness and the suggested fix. The workflow engine rewinds to the validator

## Anti-patterns (RFC 2119)

- The agent **MUST NOT** rubber-stamp a validation suite without tracing coverage back to the data model and the user's SLAs
- The agent **MUST NOT** accept row-count checks as sufficient — uniqueness, referential integrity, and value-range checks are required too
- The agent **MUST** verify that validation failures produce enough context to diagnose the root cause
- The agent **MUST NOT** ignore SLA-related validations (freshness, completeness percentages) — they're the runtime contract
- The agent **MUST NOT** treat validation as a gate to pass — it's a safety net to maintain
- The agent **MUST** reject suites whose severity mix is "all blocking" or "all warning" — both indicate the validator didn't think about severity
- The agent **MUST** name the specific gap in any rejection so the validator knows what to add
