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name: snowflake-data-quality-sentinel description: | Assess trusted Snowflake data metric function coverage and expectation evidence without reading customer rows. Use when evaluations, definitions, schedules, notifications, anomaly state, or monitoring coverage may be incomplete or unhealthy. Trigger with "Snowflake data quality", "DMF expectation", "definition drift", "missing evaluation", or "notification gap". allowed-tools: Read, Bash(python3:*) argument-hint: "[schema-2-evidence.json]" model: inherit effort: high version: 3.16.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT compatibility: "Model-neutral; requires Python 3.10+. Optional collection requires Snowflake CLI with an existing read-only profile." tags: [saas, snowflake, data-quality, governance, observability, incident-response]

Snowflake Data Quality Sentinel

Purpose

Produce separate configuration, history-observation, and history-completeness verdicts from an owner-approved requirement denominator and trusted schema-2 collector receipts. Missing or untrusted evidence never becomes a pass, and this history surface never supports an unqualified present-tense quality pass.

Read the input contract before assembly and consult the source notes when interpreting provider semantics.

Prerequisites

Use Python 3.10+, an owner-approved policy, and Snowflake CLI with an existing least-privilege profile. Never accept credentials or request customer rows, failed-row payloads, metric values, or SQL text.

Workflow

1. Collect the exact per-object receipt set.
2. Record independently trusted evidence and owner-policy digests.
3. Analyze at the policy-bound time and preserve every non-claim.

Step 1: Collect evidence

Collect one bounded history receipt. For every distinct governed object, collect one selector-bound live association receipt and one selector-bound live expectation receipt. Also collect one notification receipt for every distinct governed object whose notification_required is true:

# Example fixed UTC window; replace with the audited interval.
python3 "${CLAUDE_SKILL_DIR}/scripts/collect_snowflake_evidence.py" \
  --surface data-quality --connection readonly-observer \
  --window-start 2026-09-01T00:00:00Z --window-end 2026-09-02T00:00:00Z \
  --output ./dq-history.json

python3 "${CLAUDE_SKILL_DIR}/scripts/collect_snowflake_evidence.py" \
  --surface data-quality-associations-current --connection readonly-observer \
  --data-quality-object GOVERNED_DB.GOVERNED_SCHEMA.GOVERNED_TABLE \
  --data-quality-domain TABLE \
  --output ./dq-associations.json

python3 "${CLAUDE_SKILL_DIR}/scripts/collect_snowflake_evidence.py" \
  --surface data-quality-expectations-current --connection readonly-observer \
  --data-quality-object GOVERNED_DB.GOVERNED_SCHEMA.GOVERNED_TABLE \
  --data-quality-domain TABLE \
  --output ./dq-expectations.json

python3 "${CLAUDE_SKILL_DIR}/scripts/collect_snowflake_evidence.py" \
  --surface data-quality-notification-current --connection readonly-observer \
  --data-quality-object GOVERNED_DB.GOVERNED_SCHEMA.GOVERNED_TABLE \
  --data-quality-domain TABLE --output ./dq-notification.json

The object selector is used only in reviewed local SQL. The receipt retains its scoped object hash and domain, never the raw selector. Do not escalate roles when a source is permission-blocked.

Step 2: Establish trust and analyze

Maintain the owner-approved policy as a separate policy.json. Assemble exactly schema_version, the byte-equivalent parsed policy, and collector_receipts in the evidence wrapper. Record the evidence and policy digests independently when each crosses its trusted local boundary. The embedded receipt checksums are not trust anchors.

# Record these at their independent trusted local boundaries.
python3 "${CLAUDE_SKILL_DIR}/scripts/analyze_data_quality.py" evidence.json \
  --print-input-sha256
python3 "${CLAUDE_SKILL_DIR}/scripts/analyze_data_quality.py" \
  --policy-file policy.json --print-policy-sha256

python3 "${CLAUDE_SKILL_DIR}/scripts/analyze_data_quality.py" evidence.json \
  --policy-file policy.json \
  --evaluated-at 2026-09-03T12:00:00Z \
  --trusted-input-sha256 sha256:RECORD_FROM_EVIDENCE_BOUNDARY \
  --trusted-policy-sha256 sha256:RECORD_FROM_POLICY_BOUNDARY --pretty

Supplying digests recomputed from an already suspect file does not establish trust. The policy's analysis_as_of_utc must equal --evaluated-at, preventing a trusted old policy from being replayed against a shifted clock.

Step 3: Interpret the result

Preserve configuration_status, history_observation_status, history_completeness_status, every finding code and hashed scope, evidence integrity and coverage, the evaluated denominator, receipt hashes, and fixed non-claims. Do not convert an inconclusive result or satisfied observation into operational approval. If remediation would mutate Snowflake, produce a dry-run proposal and obtain separate authorization.

Decision boundaries

• EXPECTATION_VIOLATED=false is SATISFIED_OBSERVATION, never quality PASS. Snowflake publishes no finality SLA for the history surface, so history_completeness_status remains UNPROVEN_NO_PROVIDER_SLA, pass_supported remains false, and settled_through_utc remains null.
• No matching result is DQ_NO_EVALUATION, not a pass. true is a violation; null is an evaluation failure.
• Definition, schedule, role, filter, object-domain, grouping, or group-limit drift blocks a healthy verdict.
• Trigger-on-change freshness and grouped-result completeness remain inconclusive until separately reviewed evidence proves them.
• Anomaly objectives remain inconclusive until a separate trusted anomaly surface exists. Do not infer anomaly health from association configuration.
• Notification ENABLED proves configuration only. Delivery always remains NOT_OBSERVED; missing visibility differs from disabled configuration.
• One association-surface receipt and one expectation-surface receipt must cover every distinct governed object exactly. Multiple expectations may share an association, but the (association_key_sha256, expectation_key_sha256) policy key is unique.
• Missing surfaces, stale or mixed contexts, caps, truncation, invalid schemas, duplicates, offline receipts, or either trust mismatch suppress classification.

Output

The report includes the three status axes, pass_supported, settled_through_utc, integrity and coverage, denominator counts, deterministic findings, safe provenance, fixed non-claims, and report_sha256. Findings contain fixed text and validated requirement hashes only.

Error Handling

• Exit 2 is a fixed generic malformed-input error and never reflects rejected input.
• evidence_integrity_status=INVALID means a trust or receipt check failed; recollect.
• evidence_complete remains false because history completeness is not provider-proven.
• Permission-filtered notification evidence is a visibility gap, not proof of disabled configuration.
• Edition or privilege failures do not authorize automatic role escalation.

Example

False produces SATISFIED_OBSERVATION, while a missing row produces DQ_NO_EVALUATION; neither produces quality PASS. A true row produces VIOLATION_OBSERVED and quality FAIL.

Safety

The analyzer and collector are read-only. A remediation request ends at a dry-run change proposal unless the caller separately authorizes mutation. Findings use validated requirement hashes as scopes and fixed text, so rejected receipt fields or values are never reflected into output.

Resources

• Schema-2 input contract
• Primary-source semantics and latency
• Snowflake data-quality monitoring