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- 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.
153 lines
4.5 KiB
Plaintext
153 lines
4.5 KiB
Plaintext
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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