feat(data-platform): add data-integrity-audit skill

Define audit rules, severity classification, scanning strategies,
and report templates for data integrity validation.

Covers:
- Schema validity (PostgreSQL tables, columns, types)
- dbt project health (parse, compile, test, lineage)
- PostGIS compliance (SRID, geometry types, extent)
- Data type consistency (DataFrame dtypes)
- Query safety patterns

Closes #373

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
2026-02-02 01:24:54 -05:00
parent 192f808f48
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---
name: data-integrity-audit
description: Rules and patterns for auditing data integrity, schema validity, and dbt compliance
---
# Data Integrity Audit
## Purpose
Defines what "data valid" means for the data-platform domain. This skill is loaded by the `data-advisor` agent for both review and gate modes during sprint execution and standalone audits.
---
## What to Check
| Check Category | What It Validates | MCP Tools Used |
|----------------|-------------------|----------------|
| **Schema Validity** | Tables exist, columns have correct types, constraints present, no orphaned columns | `pg_tables`, `pg_columns`, `pg_schemas` |
| **dbt Project Health** | Project parses without errors, models compile, tests defined for critical models | `dbt_parse`, `dbt_compile`, `dbt_test`, `dbt_ls` |
| **Lineage Integrity** | No orphaned models (referenced but missing), no circular dependencies, upstream sources exist | `dbt_lineage`, `dbt_ls` |
| **Data Type Consistency** | DataFrame dtypes match expected schema, no silent type coercion, date formats consistent | `describe`, `head`, `pg_columns` |
| **PostGIS Compliance** | Spatial tables have correct SRID, geometry types match expectations, extent is reasonable | `st_tables`, `st_geometry_type`, `st_srid`, `st_extent` |
| **Query Safety** | SELECT queries used for reads (not raw SQL for mutations), parameterized patterns | Code review - manual pattern check |
---
## Common Violations
### FAIL-Level Violations (Block Gate)
| Violation | Detection Method | Example |
|-----------|-----------------|---------|
| dbt parse failure | `dbt_parse` returns error | Project YAML invalid, missing ref targets |
| dbt compilation error | `dbt_compile` fails | SQL syntax error, undefined column reference |
| Missing table/column | `pg_tables`, `pg_columns` lookup | Code references `census_raw` but table doesn't exist |
| Type mismatch | Compare `pg_columns` vs dbt schema | Column is `varchar` in DB but model expects `integer` |
| Broken lineage | `dbt_lineage` shows orphaned refs | Model references `stg_old_format` which doesn't exist |
| PostGIS SRID mismatch | `st_srid` returns unexpected value | Geometry column has SRID 0 instead of 4326 |
| Unreasonable spatial extent | `st_extent` returns global bbox | Toronto data shows coordinates in China |
### WARN-Level Violations (Report, Don't Block)
| Violation | Detection Method | Example |
|-----------|-----------------|---------|
| Missing dbt tests | `dbt_ls` shows model without test | `dim_customers` has no `unique` test on `customer_id` |
| Undocumented columns | dbt schema.yml missing descriptions | Model columns have no documentation |
| Schema drift | `pg_columns` vs dbt schema.yml | Column exists in DB but not in dbt YAML |
| Hardcoded SQL | Scan Python for string concatenation | `f"SELECT * FROM {table}"` without parameterization |
| Orphaned model | `dbt_lineage` shows no downstream | `stg_legacy` has no consumers and no exposure |
### INFO-Level Violations (Suggestions Only)
| Violation | Detection Method | Example |
|-----------|-----------------|---------|
| Missing indexes | Query pattern suggests need | Frequent filter on non-indexed column |
| Documentation gaps | dbt docs incomplete | Missing model description |
| Unused models | `dbt_ls` vs actual queries | Model exists but never selected |
| Optimization opportunity | `describe` shows data patterns | Column has low cardinality, could be enum |
---
## Severity Classification
| Severity | When to Apply | Gate Behavior |
|----------|--------------|---------------|
| **FAIL** | Broken lineage, models that won't compile, missing tables/columns, data type mismatches that cause runtime errors, invalid SRID | Blocks issue completion |
| **WARN** | Missing dbt tests, undocumented columns, schema drift, hardcoded SQL, orphaned models | Does NOT block gate, included in review report |
| **INFO** | Optimization opportunities, documentation gaps, unused models | Review report only |
### Severity Decision Tree
```
Is the dbt project broken (parse/compile fails)?
YES -> FAIL
NO -> Does code reference non-existent tables/columns?
YES -> FAIL
NO -> Would this cause a runtime error?
YES -> FAIL
NO -> Does it violate data quality standards?
YES -> WARN
NO -> Is it an optimization/documentation suggestion?
YES -> INFO
NO -> Not a violation
```
---
## Scanning Strategy
### For dbt Projects
1. **Parse validation** (ALWAYS FIRST)
```
dbt_parse → if fails, immediate FAIL (project is broken)
```
2. **Catalog resources**
```
dbt_ls → list all models, tests, sources, exposures
```
3. **Lineage check**
```
dbt_lineage on changed models → check upstream/downstream integrity
```
4. **Compilation check**
```
dbt_compile on changed models → verify SQL renders correctly
```
5. **Test execution**
```
dbt_test --select <changed_models> → verify tests pass
```
6. **Test coverage audit**
```
Cross-reference dbt_ls tests against model list → flag models without tests (WARN)
```
### For PostgreSQL Schema Changes
1. **Table verification**
```
pg_tables → verify expected tables exist
```
2. **Column validation**
```
pg_columns on affected tables → verify types match expectations
```
3. **Schema comparison**
```
Compare pg_columns output against dbt schema.yml → flag drift
```
### For PostGIS/Spatial Data
1. **Spatial table scan**
```
st_tables → list tables with geometry columns
```
2. **SRID validation**
```
st_srid → verify SRID is correct for expected region
Expected: 4326 (WGS84) for GPS data, local projections for regional data
```
3. **Geometry type check**
```
st_geometry_type → verify expected types (Point, Polygon, etc.)
```
4. **Extent sanity check**
```
st_extent → verify bounding box is reasonable for expected region
Toronto data should be ~(-79.6 to -79.1, 43.6 to 43.9)
```
### For DataFrame/pandas Operations
1. **Data quality check**
```
describe → check for unexpected nulls, type issues, outliers
```
2. **Structure verification**
```
head → verify data structure matches expectations
```
3. **Memory management**
```
list_data → verify no stale DataFrames from previous failed runs
```
### For Python Code (Manual Scan)
1. **SQL injection patterns**
- Scan for f-strings with table/column names
- Check for string concatenation in queries
- Look for `.format()` calls with SQL
2. **Mutation safety**
- `pg_execute` usage should be intentional, not accidental
- Verify DELETE/UPDATE have WHERE clauses
3. **Credential exposure**
- No hardcoded connection strings
- No credentials in code (check for `.env` usage)
---
## Report Templates
### Gate Mode (Compact)
```
DATA GATE: PASS
No blocking data integrity violations found.
```
or
```
DATA GATE: FAIL
Blocking Issues (N):
1. <location> - <violation description>
Fix: <actionable fix>
2. <location> - <violation description>
Fix: <actionable fix>
Run /data-review for full audit report.
```
### Review Mode (Detailed)
```
+----------------------------------------------------------------------+
| DATA-PLATFORM - Data Integrity Audit |
| [Target Path] |
+----------------------------------------------------------------------+
Target: <scanned path or project>
Scope: N files scanned, N models checked, N tables verified
FINDINGS
FAIL (N)
1. [location] violation description
Fix: actionable fix
2. [location] violation description
Fix: actionable fix
WARN (N)
1. [location] warning description
Suggestion: improvement suggestion
2. [location] warning description
Suggestion: improvement suggestion
INFO (N)
1. [location] info description
Note: context
SUMMARY
Schema: Valid | N issues
Lineage: Intact | N orphaned
dbt: Passes | N failures
PostGIS: Valid | N issues | Not applicable
VERDICT: PASS | FAIL (N blocking issues)
```
---
## Skip Patterns
Do not flag violations in:
- `**/tests/**` - Test files may have intentional violations
- `**/__pycache__/**` - Compiled files
- `**/fixtures/**` - Test fixtures
- `**/.scratch/**` - Temporary working files
- Files with `# noqa: data-audit` comment
- Migration files marked as historical
---
## Error Handling
| Scenario | Behavior |
|----------|----------|
| Database not reachable (`pg_connect` fails) | WARN, skip PostgreSQL checks, continue with file-based |
| dbt not configured (no `dbt_project.yml`) | Skip dbt checks entirely, not an error |
| No PostGIS tables found | Skip PostGIS checks, not an error |
| MCP tool call fails | Report as WARN with tool name, continue with remaining checks |
| No data files in scanned path | Report "No data artifacts found" - PASS (nothing to fail) |
| Empty directory | Report "No files found in path" - PASS |
---
## Integration Notes
### projman Orchestrator
When called as a domain gate:
1. Orchestrator detects `Domain/Data` label on issue
2. Orchestrator identifies changed files
3. Orchestrator invokes `/data-gate <path>`
4. Agent runs gate mode scan
5. Returns PASS/FAIL to orchestrator
6. Orchestrator decides whether to complete issue
### Standalone Usage
For manual audits:
1. User runs `/data-review <path>`
2. Agent runs full review mode scan
3. Returns detailed report with all severity levels
4. User decides on actions