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Add workflow-skill-memory command and skill aggregation prompt
- Implemented the workflow-skill-memory command for generating SKILL packages from archived workflow sessions. - Defined a 4-phase execution process for reading sessions, extracting data, organizing information, and generating SKILL files. - Created a detailed prompt for skill aggregation, outlining tasks for analyzing archived sessions, aggregating lessons learned, conflict patterns, and implementation summaries. - Established output formats for aggregated lessons, conflict patterns, and implementation summaries to ensure structured and actionable insights.
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You are aggregating workflow session history to generate a progressive SKILL package.
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## Your Task
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Analyze archived workflow sessions and aggregate:
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1. **Lessons Learned** - Successes, challenges, and watch patterns
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2. **Conflict Patterns** - Recurring conflicts and resolutions
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3. **Implementation Summaries** - Key outcomes by functional domain
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## Input Data
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You will receive:
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- Session metadata (session_id, description, tags, metrics)
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- Lessons from each session (successes, challenges, watch_patterns)
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- IMPL_PLAN summaries
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- Context package metadata (keywords, tech_stack, complexity)
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## Output Requirements
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### 1. Aggregated Lessons
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**Successes by Category**:
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- Group successful patterns by functional domain (auth, testing, performance, etc.)
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- Identify practices that succeeded across multiple sessions
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- Mark best practices (success in 3+ sessions)
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**Challenges by Severity**:
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- HIGH: Blocked development for >4 hours OR repeated in 3+ sessions
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- MEDIUM: Required significant rework OR repeated in 2 sessions
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- LOW: Minor issues resolved quickly
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**Watch Patterns**:
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- Identify patterns mentioned in 2+ sessions
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- Prioritize by frequency and severity
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- Mark CRITICAL patterns (appeared in 3+ sessions with HIGH severity)
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**Format**:
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```json
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{
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"successes_by_category": {
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"auth": ["JWT implementation with refresh tokens (3 sessions)", ...],
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"testing": ["TDD reduced bugs by 60% (2 sessions)", ...]
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},
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"challenges_by_severity": {
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"high": [
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{
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"challenge": "Token refresh edge cases",
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"sessions": ["WFS-user-auth", "WFS-jwt-refresh"],
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"frequency": 2
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}
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],
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"medium": [...],
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"low": [...]
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},
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"watch_patterns": [
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{
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"pattern": "Token concurrency issues",
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"frequency": 3,
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"severity": "CRITICAL",
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"sessions": ["WFS-user-auth", "WFS-jwt-refresh", "WFS-oauth"]
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}
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]
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}
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```
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### 2. Conflict Patterns
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**Analysis**:
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- Group conflicts by type (architecture, dependencies, testing, performance)
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- Identify recurring patterns (same conflict in different sessions)
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- Link successful resolutions to specific sessions
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**Format**:
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```json
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{
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"architecture": [
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{
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"pattern": "Multiple authentication strategies conflict",
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"description": "Different auth methods (JWT, OAuth, session) cause integration issues",
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"sessions": ["WFS-user-auth", "WFS-oauth"],
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"resolution": "Unified auth interface with strategy pattern",
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"code_impact": ["src/auth/interface.ts", "src/auth/jwt.ts", "src/auth/oauth.ts"],
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"frequency": 2,
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"severity": "high"
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}
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],
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"dependencies": [...],
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"testing": [...],
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"performance": [...]
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}
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```
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### 3. Implementation Summary
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**By Functional Domain**:
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- Group sessions by primary tag/domain
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- Summarize key accomplishments
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- Link to context packages and plans
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**Format**:
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```json
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{
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"auth": {
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"session_count": 3,
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"sessions": [
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{
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"session_id": "WFS-user-auth",
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"description": "JWT authentication implementation",
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"key_outcomes": [
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"JWT token generation and validation",
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"Refresh token mechanism",
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"Secure password hashing with bcrypt"
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],
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"context_package": ".workflow/.archives/WFS-user-auth/.process/context-package.json",
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"metrics": {"task_count": 5, "success_rate": 100, "duration_hours": 4.5}
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}
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],
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"cumulative_metrics": {
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"total_tasks": 15,
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"avg_success_rate": 95,
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"total_hours": 12.5
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}
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},
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"payment": {...},
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"ui": {...}
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}
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```
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## Analysis Guidelines
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1. **Identify Patterns**: Look for recurring themes across sessions
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2. **Prioritize by Impact**: Focus on high-frequency, high-impact patterns
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3. **Link Sessions**: Connect related sessions (same domain, similar challenges)
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4. **Extract Wisdom**: Surface actionable insights from lessons learned
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5. **Maintain Context**: Keep references to original sessions and files
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## Quality Criteria
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- ✅ All sessions processed and categorized
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- ✅ Patterns identified and frequency counted
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- ✅ Severity levels assigned based on impact
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- ✅ Resolutions linked to specific sessions
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- ✅ Output is valid JSON with no missing fields
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- ✅ References (paths) are accurate and complete
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## Important Notes
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- **NO hallucination**: Only aggregate data from provided sessions
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- **Preserve detail**: Keep specific session references for traceability
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- **Smart grouping**: Group similar patterns even if wording differs slightly
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- **Frequency matters**: Prioritize patterns that appear in multiple sessions
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- **Context preservation**: Keep context package paths for on-demand loading
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