refactor: extract skills from commands across 8 plugins

Refactored commands to extract reusable skills following the
Commands → Skills separation pattern. Each command is now <50 lines
and references skill files for detailed knowledge.

Plugins refactored:
- claude-config-maintainer: 5 commands → 7 skills
- code-sentinel: 3 commands → 2 skills
- contract-validator: 5 commands → 6 skills
- data-platform: 10 commands → 6 skills
- doc-guardian: 5 commands → 6 skills (replaced nested dir)
- git-flow: 8 commands → 7 skills

Skills contain: workflows, validation rules, conventions,
reference data, tool documentation

Commands now contain: YAML frontmatter, agent assignment,
skills list, brief workflow steps, parameters

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

plugins/data-platform/commands/data-quality.md

@@ -1,18 +1,13 @@
 # /data-quality - Data Quality Assessment
 
+## Skills to Load
+- skills/data-profiling.md
+- skills/mcp-tools-reference.md
+- skills/visual-header.md
+
 ## Visual Output
 
-When executing this command, display the plugin header:
-
-```
-┌──────────────────────────────────────────────────────────────────┐
-│  📊 DATA-PLATFORM · Data Quality                                  │
-└──────────────────────────────────────────────────────────────────┘
-```
-
-Then proceed with the assessment.
-
-Comprehensive data quality check for DataFrames with pass/warn/fail scoring.
+Display header: `DATA-PLATFORM - Data Quality`
 
 ## Usage
 
@@ -22,72 +17,18 @@ Comprehensive data quality check for DataFrames with pass/warn/fail scoring.
 
 ## Workflow
 
-1. **Get data reference**:
-   - If no data_ref provided, use `list_data` to show available options
-   - Validate the data_ref exists
+Execute `skills/data-profiling.md` quality assessment:
 
-2. **Null analysis**:
-   - Calculate null percentage per column
-   - **PASS**: < 5% nulls
-   - **WARN**: 5-20% nulls
-   - **FAIL**: > 20% nulls
-
-3. **Duplicate detection**:
-   - Check for fully duplicated rows
-   - **PASS**: 0% duplicates
-   - **WARN**: < 1% duplicates
-   - **FAIL**: >= 1% duplicates
-
-4. **Type consistency**:
-   - Identify mixed-type columns (object columns with mixed content)
-   - Flag columns that could be numeric but contain strings
-   - **PASS**: All columns have consistent types
-   - **FAIL**: Mixed types detected
-
-5. **Outlier detection** (numeric columns):
-   - Use IQR method (values beyond 1.5 * IQR)
-   - Report percentage of outliers per column
-   - **PASS**: < 1% outliers
-   - **WARN**: 1-5% outliers
-   - **FAIL**: > 5% outliers
-
-6. **Generate quality report**:
-   - Overall quality score (0-100)
-   - Per-column breakdown
-   - Recommendations for remediation
-
-## Report Format
-
-```
-=== Data Quality Report ===
-Dataset: sales_data
-Rows: 10,000 | Columns: 15
-Overall Score: 82/100 [PASS]
-
---- Column Analysis ---
-| Column       | Nulls | Dups | Type     | Outliers | Status |
-|--------------|-------|------|----------|----------|--------|
-| customer_id  | 0.0%  | -    | int64    | 0.2%     | PASS   |
-| email        | 2.3%  | -    | object   | -        | PASS   |
-| amount       | 15.2% | -    | float64  | 3.1%     | WARN   |
-| created_at   | 0.0%  | -    | datetime | -        | PASS   |
-
---- Issues Found ---
-[WARN] Column 'amount': 15.2% null values (threshold: 5%)
-[WARN] Column 'amount': 3.1% outliers detected
-[FAIL] 1.2% duplicate rows detected (12 rows)
-
---- Recommendations ---
-1. Investigate null values in 'amount' column
-2. Review outliers in 'amount' - may be data entry errors
-3. Remove or deduplicate 12 duplicate rows
-```
+1. **Get data reference**: Use `list_data` if none provided
+2. **Run quality checks**: Nulls, duplicates, types, outliers
+3. **Calculate score**: Apply weighted scoring formula
+4. **Generate report**: Issues and recommendations
 
 ## Options
 
 | Flag | Description |
 |------|-------------|
-| `--strict` | Use stricter thresholds (WARN at 1% nulls, FAIL at 5%) |
+| `--strict` | Stricter thresholds (WARN at 1%, FAIL at 5% nulls) |
 
 ## Examples
 
@@ -96,20 +37,12 @@ Overall Score: 82/100 [PASS]
 /data-quality df_customers --strict
 ```
 
-## Scoring
+## Quality Thresholds
 
-| Component | Weight | Scoring |
-|-----------|--------|---------|
-| Nulls | 30% | 100 - (avg_null_pct * 2) |
-| Duplicates | 20% | 100 - (dup_pct * 50) |
-| Type consistency | 25% | 100 if clean, 0 if mixed |
-| Outliers | 25% | 100 - (avg_outlier_pct * 10) |
+See `skills/data-profiling.md` for detailed thresholds and scoring.
 
-Final score: Weighted average, capped at 0-100
+## Required MCP Tools
 
-## Available Tools
-
-Use these MCP tools:
-- `describe` - Get statistical summary (for outlier detection)
+- `describe` - Get statistical summary
 - `head` - Preview data
 - `list_data` - List available DataFrames