CLAUDE.md Auto-Updater Skill

CLAUDE.md Auto-Updater Skill

Overview

This skill maintains documentation accuracy by detecting when robo-trader's CLAUDE.md files diverge from actual codebase patterns. It analyzes both Python backend and TypeScript/​React frontend code to identify:

• New Patterns: When 3+ code instances share a similar pattern not yet documented
• Violations: When code breaks documented CLAUDE.md constraints
• Stale Documentation: When documented patterns no longer appear in the codebase
• Anti-Patterns: When repeated mistakes could be prevented with documentation

The skill generates specific, evidence-based recommendations with markdown diffs showing exactly what to change in which CLAUDE.md file. Each recommendation includes confidence scores, file:line references proving why the change is needed, and a learning mechanism to improve accuracy based on accepted/​rejected feedback.

When to Use This Skill

Invoke this skill when:

1. Feature Completion - After completing major features that introduce new architectural patterns
2. Code Reviews - Before merging PRs that change architecture or patterns
3. Periodic Maintenance - Weekly/​monthly scans to catch documentation drift
4. Manual Request - On-demand analysis when unsure if CLAUDE.md is current
5. Bulk Changes - After refactoring or restructuring significant portions of the codebase

Core Capabilities

1. Pattern Detection

Automatically scans the codebase to find:

• New Patterns (Python + TypeScript/​React): When 3+ similar implementations exist without documentation
• Critical Violations: When code directly violates documented rules (e.g., direct DB access when docs say use locked methods)
• Stale Documentation: When documented patterns haven't appeared in code for 30+ days
• Anti-Patterns: Repeated mistakes (e.g., 4+ instances of time.sleep() in async code)

2. Recommendation Generation

For each detection, generates:

• Specific Markdown Diff: Exact text to add/​modify/​remove in CLAUDE.md
• Evidence: File:line references proving the pattern exists
• Confidence Score: 0-100% based on evidence quantity and clarity
• Rationale: Human-readable explanation of why the change is needed
• Affected File: Which CLAUDE.md file(s) need updates

3. Validation & Safety

Before proposing changes:

• Consistency Check: Verifies no conflicting rules across CLAUDE.md files
• Reference Validation: Confirms all referenced files still exist in codebase
• Example Verification: Checks that code examples in recommendations actually work
• Impact Analysis: Assesses consequences of proposed changes

4. Learning System

Tracks feedback to improve future recommendations:

• Records which recommendations you accepted/​rejected
• Analyzes patterns in helpful vs. noisy suggestions
• Adjusts confidence scoring based on accuracy history
• Improves pattern detection thresholds over time

How to Use This Skill

Basic Workflow

1. Trigger Analysis: Call the skill with your codebase path and analysis scope
2. Review Recommendations: Examine generated markdown diffs with evidence
3. Accept/​Reject: Approve recommendations and optionally apply them
4. Track Feedback: Provide acceptance/​rejection feedback to train the learning system
5. Update CLAUDE.md: Apply approved recommendations to your documentation

Example Usage

Analyze robo-trader codebase for CLAUDE.md updates
- Scope: Full backend (src/) + frontend (ui/​src/)
- Output: Markdown diffs with >70% confidence

Result:

📋 RECOMMENDATION 1: New Queue Handler Pattern
File: src/​CLAUDE.md
Confidence: 92%
Evidence: Found 3 new queue handlers (FORECAST, SENTIMENT, SIGNAL) at:
- src/​services/​scheduler/​handlers/​forecast_handler.py:12
- src/​services/​scheduler/​handlers/​sentiment_handler.py:15
- src/​services/​scheduler/​handlers/​signal_handler.py:18

Suggested addition to "Sequential Queue Architecture (CRITICAL)" section:
+ **Handler Examples**: RECOMMENDATION_GENERATION, PORTFOLIO_SYNC, DATA_FETCHER, FORECAST_ANALYSIS, SENTIMENT_ANALYSIS, SIGNAL_DETECTION

Detection Patterns

The skill looks for specific patterns in your codebase:

Python Backend Patterns

• Queue handler decorators (@task_handler())
• Coordinator class definitions (inheriting from BaseCoordinator)
• Database access methods (detecting direct vs. locked access)
• SDK usage patterns (detecting ClaudeSDKClientManager usage)
• Error handling (custom exception types)
• Async operations (detecting sleep vs. condition polling)

TypeScript/​React Frontend Patterns

• Component organization (checking feature-based structure)
• Hook patterns (custom hooks, Zustand stores)
• State management patterns
• API integration patterns
• Error handling approaches

Output Format

Each recommendation includes:

## RECOMMENDATION [#]: [Title]
**File**: [Which CLAUDE.md to update]
**Confidence**: [0-100%]

### Evidence
[File:line references proving the pattern exists]

### Why This Update?
[Human-readable explanation]

### Proposed Change
[Markdown diff showing exact text to add/​modify/​remove]

### Related Changes
[Other CLAUDE.md files that might need updates]

Bundled Resources

scripts/​detector.py

Core pattern detection engine that scans Python and TypeScript/​React code to identify new patterns, violations, stale documentation, and anti-patterns. Returns structured JSON with all findings.

Usage:

python scripts/​detector.py /​path/​to/​robo-trader --scope full

scripts/​analyzer.py

Analyzes detector output, assigns confidence scores, identifies affected CLAUDE.md files, and prioritizes recommendations.

Usage:

python scripts/​analyzer.py detector_output.json

scripts/​generator.py

Converts analyzed findings into specific CLAUDE.md update proposals with markdown diffs, evidence references, and human-readable rationale.

Usage:

python scripts/​generator.py analysis_output.json --output-format markdown

scripts/​validator.py

Validates recommendations before proposing them: checks for conflicting rules, verifies examples work, confirms referenced files exist, and performs impact analysis.

Usage:

python scripts/​validator.py recommendations.json

scripts/​feedback_tracker.py

Records accepted/​rejected recommendations and updates confidence scoring based on feedback patterns to improve accuracy over time.

Usage:

python scripts/​feedback_tracker.py --record-feedback recommendation_id accepted

references/​ROBO_TRADER_PATTERNS.md

Comprehensive reference of robo-trader-specific patterns to detect: queue handlers, coordinators, database access, SDK usage, anti-patterns, and learning rules.

references/​CLAUDE_MD_STRUCTURE.md

Reference for CLAUDE.md file organization, required sections per file type, metadata format, and naming conventions.

references/​DETECTION_RULES.md

Detailed detection rules including AST patterns for Python, regex/​AST patterns for TypeScript, confidence thresholds, and evidence collection requirements.

assets/​claude-md-template-section.md

Template snippets for adding new sections to CLAUDE.md: new pattern sections, anti-pattern sections, quick reference templates, code example templates.

assets/​feedback-schema.json

Schema for tracking feedback on recommendations: recommendation ID, accepted/​rejected status, user notes, timestamp, confidence impact.

Integration Points

This skill is designed to integrate with:

• GitHub Actions: Automated analysis on every PR
• Git Hooks: Pre-commit validation
• Claude Code CLI: Manual /​claude-md-update command
• robo-trader-dev MCP: Runtime pattern validation
• Feedback Loop: Learning system improves from acceptance/​rejection patterns

Next Steps

1. Run Initial Scan: Analyze your codebase to get baseline recommendations
2. Review High-Confidence Suggestions: Accept/​reject >80% confidence recommendations first
3. Apply Changes: Update your CLAUDE.md files with approved recommendations
4. Provide Feedback: Record which recommendations were helpful
5. Iterate: Run periodic scans to keep documentation synchronized

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Remember: This skill proposes changes—it never auto-commits. Always review recommendations before updating your CLAUDE.md files.