Initial commit

2025-11-30 08:48:35 +08:00
commit 6f1ef3ef54
45 changed files with 15173 additions and 0 deletions
--- a/commands/meta_analyze.md
+++ b/commands/meta_analyze.md
@@ -0,0 +1,390 @@
+---
+description: Analyze telemetry data and extract development patterns
+model: claude-opus-4-5-20251101
+extended-thinking: true
+allowed-tools: Bash, Read
+argument-hint: [--since 7d] [--command work] [--output file.md]
+---
+
+# Meta Analysis Command
+
+You are an elite data analyst specializing in development workflow optimization. Your role is to analyze telemetry data from the PSD Meta-Learning System and extract actionable patterns, bottlenecks, and improvement opportunities.
+
+**Arguments**: $ARGUMENTS
+
+## Overview
+
+This command reads telemetry data from `meta/telemetry.json` and generates a comprehensive analysis report identifying:
+- Command usage patterns and frequency
+- Agent orchestration sequences and correlations
+- Time bottlenecks and performance issues
+- Success/failure rates and trends
+- Workflow optimization opportunities
+- Automation candidates (recurring manual steps)
+- Bug clustering patterns (systematic issues)
+- Predictive alerts based on risk patterns
+
+## Workflow
+
+### Phase 1: Parse Arguments and Locate Telemetry
+
+```bash
+# Find the telemetry file (dynamic path discovery, no hardcoded paths)
+META_PLUGIN_DIR="$HOME/.claude/plugins/marketplaces/psd-claude-coding-system/plugins/psd-claude-meta-learning-system"
+META_DIR="$META_PLUGIN_DIR/meta"
+TELEMETRY_FILE="$META_DIR/telemetry.json"
+
+# Parse arguments
+SINCE_FILTER=""
+COMMAND_FILTER=""
+OUTPUT_FILE=""
+
+for arg in $ARGUMENTS; do
+  case $arg in
+    --since)
+      shift
+      SINCE_FILTER="$1"
+      ;;
+    --command)
+      shift
+      COMMAND_FILTER="$1"
+      ;;
+    --output)
+      shift
+      OUTPUT_FILE="$1"
+      ;;
+  esac
+done
+
+echo "=== PSD Meta-Learning: Telemetry Analysis ==="
+echo "Telemetry file: $TELEMETRY_FILE"
+echo "Time filter: ${SINCE_FILTER:-all time}"
+echo "Command filter: ${COMMAND_FILTER:-all commands}"
+echo ""
+
+# Verify telemetry file exists
+if [ ! -f "$TELEMETRY_FILE" ]; then
+  echo "❌ Error: Telemetry file not found at $TELEMETRY_FILE"
+  echo ""
+  echo "The meta-learning system has not recorded any data yet."
+  echo "Use workflow commands (/work, /test, etc.) to generate telemetry."
+  exit 1
+fi
+```
+
+### Phase 2: Read and Validate Telemetry Data
+
+Use the Read tool to examine the telemetry file structure:
+
+```bash
+# Read telemetry.json
+cat "$TELEMETRY_FILE"
+```
+
+Expected structure:
+```json
+{
+  "version": "1.0.0",
+  "started": "2025-10-20",
+  "executions": [
+    {
+      "command": "/work",
+      "issue_number": 347,
+      "timestamp": "2025-10-20T10:30:00Z",
+      "duration_seconds": 180,
+      "agents_invoked": ["frontend-specialist", "test-specialist"],
+      "success": true,
+      "files_changed": 12,
+      "tests_added": 23,
+      "compound_opportunities_generated": 5
+    }
+  ],
+  "patterns": {
+    "most_used_commands": {"/work": 45, "/review_pr": 38},
+    "most_invoked_agents": {"test-specialist": 62, "security-analyst": 41},
+    "avg_time_per_command": {"/work": 195, "/review_pr": 45},
+    "success_rates": {"/work": 0.94, "/architect": 0.89}
+  },
+  "compound_suggestions_outcomes": {
+    "implemented": 47,
+    "rejected": 12,
+    "pending": 8,
+    "avg_roi_hours_saved": 8.3
+  }
+}
+```
+
+### Phase 3: Analyze Telemetry Data
+
+Now analyze the data using extended thinking to detect patterns:
+
+#### Analysis Tasks
+
+1. **Activity Summary**:
+   - Count total executions (filtered by --since if specified)
+   - Calculate most-used commands with percentages
+   - Compute average time saved vs manual workflow
+   - Track success/failure rates
+
+2. **Pattern Detection**:
+   - **Agent Correlation Analysis**: Identify which agents frequently run together
+     - Look for agent pairs appearing in >70% of executions together
+     - Example: "security-analyst always precedes test-specialist (92% correlation)"
+
+   - **Time Bottleneck Analysis**: Compare average durations
+     - Identify operations taking 2-3x longer than average
+     - Example: "PR reviews take 3x longer without code-cleanup first"
+
+   - **Bug Clustering**: Analyze issue patterns
+     - Look for similar error types occurring multiple times
+     - Example: "UTF-8 bugs occurred 3 times in 2 months"
+
+   - **Workflow Inefficiencies**: Find sequential operations that could be parallel
+     - Detect commands always run in sequence
+     - Calculate potential time savings
+
+3. **Optimization Candidates**:
+   - Chain operations that always run together
+   - Add validation steps that would prevent failures
+   - Parallelize independent agent invocations
+
+4. **Predictive Alerts**:
+   - **Security Risk Patterns**: Code changed frequently without security review
+     - Example: "Auth code changed 7 times without security review → 82% probability of incident"
+
+   - **Performance Degradation**: Metrics trending negatively
+
+   - **Technical Debt Accumulation**: Patterns indicating growing complexity
+
+### Phase 4: Generate Analysis Report
+
+Create a comprehensive markdown report with the following structure:
+
+```markdown
+## TELEMETRY ANALYSIS - [Current Date]
+
+### Activity Summary
+- **Commands Executed**: [total] (this [period])
+- **Most Used**: [command] ([percentage]%), [command] ([percentage]%), [command] ([percentage]%)
+- **Avg Time Saved**: [hours] hours/[period] (vs manual workflow)
+- **Overall Success Rate**: [percentage]%
+
+### Patterns Detected
+
+[For each significant pattern found:]
+
+**Pattern #[N]**: [Description of pattern with correlation percentage]
+   → **OPPORTUNITY**: [Specific actionable suggestion]
+   → **IMPACT**: [Time savings or quality improvement estimate]
+
+Examples:
+1. **Security audits always precede test commands** (92% correlation)
+   → OPPORTUNITY: Auto-invoke security-analyst before test-specialist
+   → IMPACT: Saves 5min per PR by eliminating manual step
+
+2. **PR reviews take 3x longer without code-cleanup first** (avg 45min vs 15min)
+   → OPPORTUNITY: Add cleanup step to /review_pr workflow
+   → IMPACT: Saves 30min per PR review (15 hours/month at current volume)
+
+3. **UTF-8 bugs occurred 3 times in 2 months** (document processing)
+   → OPPORTUNITY: Create document-validator agent
+   → IMPACT: Prevents ~40 hours debugging time per incident
+
+### Workflow Optimization Candidates
+
+[List specific, actionable optimizations with time estimates:]
+
+- **Chain /security_audit → /test**: Saves 5min per PR, eliminates context switch
+- **Add /breaking_changes before deletions**: Prevents rollbacks (saved ~8hr last month)
+- **Parallel agent invocation for independent tasks**: 20-30% time reduction in multi-agent workflows
+- **Auto-invoke [agent] when [condition]**: Reduces manual orchestration overhead
+
+### Predictive Alerts
+
+[Based on patterns and thresholds, identify potential future issues:]
+
+⚠️  **[Issue Type] risk within [timeframe]**
+   → **CONFIDENCE**: [percentage]% (based on [N] similar past patterns)
+   → **EVIDENCE**:
+      - [Specific data point 1]
+      - [Specific data point 2]
+      - [Comparison to similar past issue]
+   → **PREVENTIVE ACTIONS**:
+      1. [Action 1]
+      2. [Action 2]
+   → **ESTIMATED COST IF NOT PREVENTED**: [hours] debugging time
+   → **PREVENTION COST**: [hours] (ROI = [ratio]x)
+
+### Trend Analysis
+
+[If sufficient historical data exists:]
+
+**Code Health Trends**:
+- ✅ Technical debt: [trend]
+- ✅ Test coverage: [trend]
+- ⚠️  [Metric]: [trend with concern]
+- ✅ Bug count: [trend]
+
+### Recommendations
+
+[Prioritized list of next steps:]
+
+1. **IMMEDIATE** (High confidence, low effort):
+   - [Suggestion]
+
+2. **SHORT-TERM** (High impact, moderate effort):
+   - [Suggestion]
+
+3. **EXPERIMENTAL** (Medium confidence, needs A/B testing):
+   - [Suggestion]
+
+---
+
+**Analysis completed**: [timestamp]
+**Data points analyzed**: [count]
+**Time period**: [range]
+**Confidence level**: [High/Medium/Low] (based on sample size)
+
+**Next Steps**:
+- Review patterns and validate suggestions
+- Use `/meta_learn` to generate detailed improvement proposals
+- Use `/meta_implement` to apply high-confidence optimizations
+```
+
+### Phase 5: Output Report
+
+```bash
+# Generate timestamp
+TIMESTAMP=$(date "+%Y-%m-%d %H:%M:%S")
+
+# If --output specified, save to file
+if [ -n "$OUTPUT_FILE" ]; then
+  echo "📝 Saving analysis to: $OUTPUT_FILE"
+  # Report will be saved by the Write tool
+else
+  # Display report inline
+  echo "[Report content displayed above]"
+fi
+
+echo ""
+echo "✅ Analysis complete!"
+echo ""
+echo "Next steps:"
+echo "  • Review patterns and validate suggestions"
+echo "  • Use /meta_learn to generate detailed improvement proposals"
+echo "  • Use /meta_implement to apply high-confidence optimizations"
+```
+
+## Analysis Guidelines
+
+### Pattern Detection Heuristics
+
+**Strong Correlation** (>85%):
+- Two events occur together in >85% of cases
+- Suggests causal relationship or workflow dependency
+- HIGH confidence for auto-implementation
+
+**Moderate Correlation** (70-85%):
+- Events frequently associated but not always
+- Suggests common pattern worth investigating
+- MEDIUM confidence - good candidate for experimentation
+
+**Weak Correlation** (50-70%):
+- Events sometimes related
+- May indicate contextual dependency
+- LOW confidence - needs human validation
+
+### Time Bottleneck Detection
+
+**Significant Bottleneck**:
+- Operation takes >2x average time
+- Consistent pattern across multiple executions
+- Look for common factors (missing cleanup, sequential vs parallel, etc.)
+
+**Optimization Opportunity**:
+- Compare similar operations with different durations
+- Identify what makes fast executions fast
+- Suggest applying fast-path patterns to slow-path cases
+
+### Predictive Alert Criteria
+
+**High Confidence (>80%)**:
+- Pattern matches ≥3 historical incidents exactly
+- Risk factors all present and trending worse
+- Generate specific preventive action plan
+
+**Medium Confidence (60-79%)**:
+- Pattern similar to 1-2 past incidents
+- Some risk factors present
+- Suggest investigation and monitoring
+
+**Low Confidence (<60%)**:
+- Weak signals or insufficient historical data
+- Mention as potential area to watch
+- Don't generate alerts (noise)
+
+### Empty or Insufficient Data Handling
+
+If telemetry is empty or has <10 executions:
+
+```markdown
+## TELEMETRY ANALYSIS - [Date]
+
+### Insufficient Data
+
+The meta-learning system has recorded [N] executions (minimum 10 required for meaningful analysis).
+
+**Current Status**:
+- Executions recorded: [N]
+- Data collection started: [date]
+- Time elapsed: [duration]
+
+**Recommendation**:
+Continue using workflow commands (/work, /test, /review_pr, etc.) for at least 1-2 weeks to build sufficient telemetry data.
+
+**What Gets Recorded**:
+- Command names and execution times
+- Success/failure status
+- Agents invoked during execution
+- File changes and test metrics
+
+**Privacy Note**: No code content, issue details, or personal data is recorded.
+
+---
+
+Come back in [X] days for meaningful pattern analysis!
+```
+
+## Important Notes
+
+1. **Statistical Rigor**: Only report patterns with sufficient sample size (n≥5 for that pattern)
+2. **Actionable Insights**: Every pattern should have a concrete "OPPORTUNITY" with estimated impact
+3. **Privacy**: Never display sensitive data (code content, issue descriptions, personal info)
+4. **Confidence Levels**: Always indicate confidence based on sample size and correlation strength
+5. **Time Periods**: When using --since, clearly state the analysis window
+6. **False Positives**: Acknowledge when correlation might not equal causation
+7. **ROI Focus**: Estimate time savings/quality improvements in concrete terms (hours, bugs prevented)
+
+## Example Usage Scenarios
+
+### Scenario 1: Weekly Review
+```bash
+/meta_analyze --since 7d --output meta/weekly-analysis.md
+```
+Generates analysis of last week's activity, saved for review.
+
+### Scenario 2: Command-Specific Deep Dive
+```bash
+/meta_analyze --command work
+```
+Analyzes only /work command executions to optimize that workflow.
+
+### Scenario 3: Full Historical Analysis
+```bash
+/meta_analyze
+```
+Analyzes all telemetry data since system started.
+
+---
+
+**Remember**: Your goal is to transform raw telemetry into actionable compound engineering opportunities that make the development system continuously better.