미드저니 프롬프트 엔지니어링

---title: "Reflection & Session Lifecycle"impact: "high"tags: ["reflection", "contrastive", "auto-reflection", "subagent"]---# Reflection & Session LifecycleHow sessions are closed, how automatic reflection extracts patterns, the reflection subagent, and the role of the `/reflect` command for deeper cross-session analysis.## Session Lifecycle & Automatic Reflection### Session Close DetectionWhen the user signals session completion, the agent should:1. Update the session status2. Trigger automatic reflection (inline or via subagent)3. Confirm to the user that patterns were captured**Recognition signals — the agent should detect these naturally:**| User Signal | Session Status | Action ||-------------|---------------|--------|| "Perfect", "That works", "I'm happy with this" | `success` | Mark success + trigger reflection || "Good enough", "Let's move on" | `success` | Mark success + trigger reflection || "This isn't working", "Let's try something else" | `abandoned` | Mark abandoned + trigger reflection || "Start fresh", "New session" | Close current as `abandoned` | Trigger reflection + start new || Starting `/new-session` with active session | Depends on user response | Ask, then trigger reflection |### Automatic Reflection FlowWhen a session is closed (success or abandoned), run this extraction:1. **Query all iterations for the session:**   ```sql   SELECT * FROM iterations WHERE session_id = ? ORDER BY iteration_number   ```2. **Identify successes and failures:**   - Successes: iterations where `success = 1` OR `result_assessment` average > 0.75   - Failures: iterations where `success = 0` AND average < 0.653. **Extract keyword patterns:**   - Parse prompts from all iterations   - For each keyword, record: context, success/failure, score   - Upsert into `keyword_effectiveness` (increment `times_used`, `times_effective`)4. **Extract technique patterns:**   - Parse `what_worked` and `what_failed` JSON arrays from all iterations   - Count frequency of each technique   - If a technique appears 2+ times in `what_worked`: create candidate pattern (use category matching the technique type: `keyword`, `technique`, `parameters`, etc.)   - If a technique appears 2+ times in `what_failed`: create candidate pattern with `category = 'failure-mode'`. This exact category value is required for failure-modes.md generation.4.5. **Extract action decision patterns:**   - For each iteration with `action_type` set, compare its scores to the previous iteration   - Record whether the action improved, maintained, or degraded the result   - If a pattern emerges (e.g., "vary_strong improved scores when batch_avg > 0.8"), create a `workflow` category candidate pattern5. **Write patterns with low confidence, auto-extracted:**   ```sql   INSERT INTO patterns (id, category, problem, solution, confidence, auto_extracted, is_reviewed, is_active, discovered_at)   VALUES (?, ?, ?, ?, 'low', 1, 1, 1, datetime('now'))   ```   No manual review gate — patterns are active and reviewed on insertion. Confidence graduates automatically based on `times_tested` and `success_rate`.6. **Check for duplicates** against existing active patterns before inserting. Skip if the same problem/solution already exists.7. **Auto-graduate pattern confidence.** After inserting new patterns and updating evidence, run confidence graduation on ALL active patterns (see `rules/learn-pattern-lifecycle.md`).8. **Mark session as reflected:**   ```sql   UPDATE sessions SET reflected = 1 WHERE id = ?   ```9. **Regenerate knowledge base markdown files** (see `rules/learn-pattern-lifecycle.md`).## Reflection SubagentFor background processing, spawn a general-purpose subagent:```Task(  subagent_type="general-purpose",  run_in_background=true,  prompt="You are the reflection subagent for the Midjourney learning system.    Database: mydatabase.db (via sqlite-simple MCP)    Session ID: {session_id}    1. Query all iterations for this session    2. Extract patterns from what_worked/what_failed    3. Insert new patterns with confidence='low', auto_extracted=1, is_reviewed=1    4. Update keyword_effectiveness for keywords used    5. Auto-graduate pattern confidence (see thresholds in rules/learn-pattern-lifecycle.md)    6. Mark session reflected=1    7. Regenerate knowledge base markdown files (learned-patterns.md, keyword-effectiveness.md, failure-modes.md)    No manual review needed. Extract, write, regenerate.")```**Important:** If MCP tools aren't available to subagents, fall back to inline reflection (the main agent runs steps 1-9 directly). Test MCP access first.## Reflection TechniquesThe reflection process incorporates three research-informed techniques:- **Contrastive refinement** (from MACLA): Compare successes against failures to produce conditional patterns ("works when X, fails when Y") instead of unconditional rules.- **Integration pass** (from BREW): Deduplicate, merge, and resolve contradictions before adding new patterns. Prevents knowledge base bloat and conflicting advice.- **Relevance context** (from BREW/MACLA): Patterns should carry enough context to determine when they apply, not just what they recommend.## `/reflect` Command (Updated Role)`/reflect` is no longer the primary pattern extraction mechanism. Its new role:- **Run contrastive analysis:** The deeper analysis (success vs failure comparison) that auto-extraction skips- **Handle cross-session patterns:** Compare patterns across multiple sessions- **Resolve contradictions:** When patterns conflict, do contrastive refinement to produce conditional rules- **Force full regeneration:** Re-run markdown generation if needed## Related Rules- `learn-data-model` — Database schema and query patterns- `learn-pattern-lifecycle` — Confidence graduation and knowledge generation- `core-assessment-scoring` — Produces the scores reflection analyzes