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/skills/data-profiling.md

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+# Data Profiling
+
+## Profiling Workflow
+
+1. **Get data reference** via `list_data`
+2. **Generate statistics** via `describe`
+3. **Analyze quality** (nulls, duplicates, types, outliers)
+4. **Calculate score** and generate report
+
+## Quality Checks
+
+### Null Analysis
+- Calculate null percentage per column
+- **PASS**: < 5% nulls
+- **WARN**: 5-20% nulls
+- **FAIL**: > 20% nulls
+
+### Duplicate Detection
+- Check for fully duplicated rows
+- **PASS**: 0% duplicates
+- **WARN**: < 1% duplicates
+- **FAIL**: >= 1% duplicates
+
+### Type Consistency
+- Identify mixed-type columns
+- Flag numeric columns with string values
+- **PASS**: Consistent types
+- **FAIL**: Mixed types detected
+
+### Outlier Detection (IQR Method)
+- Calculate Q1, Q3, IQR = Q3 - Q1
+- Outliers: values < Q1 - 1.5*IQR or > Q3 + 1.5*IQR
+- **PASS**: < 1% outliers
+- **WARN**: 1-5% outliers
+- **FAIL**: > 5% outliers
+
+## Quality Scoring
+
+| Component | Weight | Formula |
+|-----------|--------|---------|
+| 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) |
+
+Final score: Weighted average, capped at 0-100
+
+## Report Format
+
+```
+=== Data Quality Report ===
+Dataset: [data_ref]
+Rows: X | Columns: Y
+Overall Score: XX/100 [PASS/WARN/FAIL]
+
+--- Column Analysis ---
+| Column | Nulls | Dups | Type | Outliers | Status |
+|--------|-------|------|------|----------|--------|
+| col1   | X.X%  | -    | type | X.X%     | PASS   |
+
+--- Issues Found ---
+[WARN/FAIL] Column 'X': Issue description
+
+--- Recommendations ---
+1. Suggested remediation steps
+```
+
+## Strict Mode
+
+With `--strict` flag:
+- **WARN** at 1% nulls (vs 5%)
+- **FAIL** at 5% nulls (vs 20%)