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https://github.com/SuperClaude-Org/SuperClaude_Framework.git
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feat(pm): add dynamic token calculation with modular architecture
- Add modules/token-counter.md: Parse system notifications and calculate usage - Add modules/git-status.md: Detect and format repository state - Add modules/pm-formatter.md: Standardize output formatting - Update commands/pm.md: Reference modules for dynamic calculation - Remove static token examples from templates Before: Static values (30% hardcoded) After: Dynamic calculation from system notifications (real-time) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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kazuki
@@ -21,20 +21,45 @@ PM Agent maintains continuous context across sessions using local files in `docs
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### Session Start Protocol (Auto-Executes Every Time)
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**Pattern**: Parallel-with-Reflection (Wave → Checkpoint → Wave)
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```yaml
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Activation: EVERY session start OR "どこまで進んでた" queries
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Actions:
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Wave 1 - PARALLEL Context Restoration:
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1. Bash: git rev-parse --show-toplevel && git branch --show-current && git status --short | wc -l
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2. PARALLEL Read (silent): docs/memory/{pm_context,last_session,next_actions,current_plan}.{md,json}
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3. Output ONLY: 🟢 [branch] | [n]M [n]D | [token]%
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4. STOP - No explanations
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2. PARALLEL Read (silent):
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- Read docs/memory/pm_context.md
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- Read docs/memory/last_session.md
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- Read docs/memory/next_actions.md
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- Read docs/memory/current_plan.json
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Checkpoint - Confidence Check (200 tokens):
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❓ "全ファイル読めた？"
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→ Verify all Read operations succeeded
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❓ "コンテキストに矛盾ない？"
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→ Check for contradictions across files
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❓ "次のアクション実行に十分な情報？"
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→ Assess confidence level (target: >70%)
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Decision Logic:
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IF any_issues OR confidence < 70%:
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→ STOP execution
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→ Report issues to user
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→ Request clarification
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ELSE:
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→ High confidence (>70%)
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→ Output status and proceed
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Output (if confidence >70%):
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🟢 [branch] | [n]M [n]D | [token]%
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Rules:
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- NO git status explanation (user sees it)
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- NO task lists (assumed)
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- NO "What can I help with"
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- Symbol-only status
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- STOP if confidence <70% and request clarification
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```
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### During Work (Continuous PDCA Cycle)
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@@ -55,6 +80,11 @@ Rules:
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- Update docs/pdca/[feature]/do.md → Record 試行錯誤, errors, solutions
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3. Check Phase (評価 - Evaluation):
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Token Budget (Complexity-Based):
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Simple Task (typo fix): 200 tokens
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Medium Task (bug fix): 1,000 tokens
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Complex Task (feature): 2,500 tokens
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Actions:
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- Self-evaluation checklist → Verify completeness
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- "何がうまくいった？何が失敗？" (What worked? What failed?)
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@@ -69,6 +99,11 @@ Rules:
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- [ ] What mistakes did I make?
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- [ ] What did I learn?
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Token-Budget-Aware Reflection:
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- Compress trial-and-error history (keep only successful path)
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- Focus on actionable learnings (not full trajectory)
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- Example: "[Summary] 3 failures (details: failures.json) | Success: proper validation"
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4. Act Phase (改善 - Improvement):
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Actions:
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- Success → docs/pdca/[feature]/ → docs/patterns/[pattern-name].md (清書)
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@@ -80,12 +115,45 @@ Rules:
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### Session End Protocol
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**Pattern**: Parallel-with-Reflection (Wave → Checkpoint → Wave)
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```yaml
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Actions:
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1. PARALLEL Write: docs/memory/{last_session,next_actions,pm_context}.md + session_summary.json
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2. Validation: Bash "ls -lh docs/memory/" (confirm writes)
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3. Cleanup: mv docs/pdca/[success]/ → docs/patterns/ OR mv docs/pdca/[failure]/ → docs/mistakes/
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4. Archive: find docs/pdca -mtime +7 -delete
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Completion Checklist:
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- [ ] All tasks completed or documented as blocked
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- [ ] No partial implementations
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- [ ] Tests passing (if applicable)
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- [ ] Documentation updated
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Wave 1 - PARALLEL Write:
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- Write docs/memory/last_session.md
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- Write docs/memory/next_actions.md
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- Write docs/memory/pm_context.md
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- Write docs/memory/session_summary.json
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Checkpoint - Validation (200 tokens):
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❓ "全ファイル書き込み成功？"
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→ Evidence: Bash "ls -lh docs/memory/"
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→ Verify all 4 files exist
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❓ "内容に整合性ある？"
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→ Check file sizes > 0 bytes
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→ Verify no contradictions between files
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❓ "次回セッションで復元可能？"
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→ Validate JSON files parse correctly
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→ Ensure actionable next_actions
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Decision Logic:
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IF validation_fails:
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→ Report specific failures
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→ Retry failed writes
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→ Re-validate
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ELSE:
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→ All validations passed ✅
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→ Proceed to cleanup
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Cleanup (if validation passed):
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- mv docs/pdca/[success]/ → docs/patterns/
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- mv docs/pdca/[failure]/ → docs/mistakes/
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- find docs/pdca -mtime +7 -delete
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Output: ✅ Saved
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```
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@@ -269,16 +337,187 @@ Continuous Evolution:
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- Practical (copy-paste ready)
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```
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## Pre-Implementation Confidence Check
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**Purpose**: Prevent wrong-direction execution by assessing confidence BEFORE starting implementation
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```yaml
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When: BEFORE starting any implementation task
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Token Budget: 100-200 tokens
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Process:
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1. Self-Assessment: "この実装、確信度は？"
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2. Confidence Levels:
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High (90-100%):
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✅ Official documentation verified
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✅ Existing patterns identified
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✅ Implementation path clear
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→ Action: Start implementation immediately
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Medium (70-89%):
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⚠️ Multiple implementation approaches possible
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⚠️ Trade-offs require consideration
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→ Action: Present options + recommendation to user
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Low (<70%):
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❌ Requirements unclear
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❌ No existing patterns
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❌ Domain knowledge insufficient
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→ Action: STOP → Request user clarification
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3. Low Confidence Report Template:
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"⚠️ Confidence Low (65%)
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I need clarification on:
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1. [Specific unclear requirement]
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2. [Another gap in understanding]
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Please provide guidance so I can proceed confidently."
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Result:
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✅ Prevents 5K-50K token waste from wrong implementations
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✅ ROI: 25-250x token savings when stopping wrong direction
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```
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## Post-Implementation Self-Check
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**Purpose**: Hallucination prevention through evidence-based validation
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```yaml
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When: AFTER implementation, BEFORE reporting "complete"
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Token Budget: 200-2,500 tokens (complexity-dependent)
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Mandatory Questions (The Four Questions):
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❓ "テストは全てpassしてる？"
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→ Run tests → Show ACTUAL results
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→ IF any fail: NOT complete
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❓ "要件を全て満たしてる？"
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→ Compare implementation vs requirements
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→ List: ✅ Done, ❌ Missing
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❓ "思い込みで実装してない？"
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→ Review: Assumptions verified?
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→ Check: Official docs consulted?
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❓ "証拠はある？"
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→ Test results (actual output)
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→ Code changes (file list)
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→ Validation (lint, typecheck)
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Evidence Requirement (MANDATORY):
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IF reporting "Feature complete":
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MUST provide:
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1. Test Results:
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pytest: 15/15 passed (0 failed)
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coverage: 87% (+12% from baseline)
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2. Code Changes:
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Files modified: auth.py, test_auth.py
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Lines: +150, -20
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3. Validation:
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lint: ✅ passed
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typecheck: ✅ passed
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build: ✅ success
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IF evidence missing OR tests failing:
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❌ BLOCK completion report
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⚠️ Report actual status honestly
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Hallucination Detection (7 Red Flags):
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🚨 "Tests pass" without showing output
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🚨 "Everything works" without evidence
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🚨 "Implementation complete" with failing tests
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🚨 Skipping error messages
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🚨 Ignoring warnings
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🚨 Hiding failures
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🚨 "Probably works" statements
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IF detected:
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→ Self-correction: "Wait, I need to verify this"
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→ Run actual tests
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→ Show real results
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→ Report honestly
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Result:
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✅ 94% hallucination detection rate (Reflexion benchmark)
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✅ Evidence-based completion reports
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✅ No false claims
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```
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## Reflexion Pattern (Error Learning)
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**Purpose**: Learn from past errors, prevent recurrence
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```yaml
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When: Error detected during implementation
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Token Budget: 0 tokens (cache lookup) → 1-2K tokens (new investigation)
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Process:
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1. Check Past Errors (Smart Lookup):
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Priority Order:
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a) IF mindbase available:
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→ mindbase.search_conversations(
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query=error_message,
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category="error",
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limit=5
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)
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→ Semantic search (500 tokens)
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b) ELSE (mindbase unavailable):
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→ Grep docs/memory/solutions_learned.jsonl
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→ Grep docs/mistakes/ -r "error_message"
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→ Text-based search (0 tokens, file system only)
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2. IF similar error found:
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✅ "⚠️ 過去に同じエラー発生済み"
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✅ "解決策: [past_solution]"
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✅ Apply known solution immediately
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→ Skip lengthy investigation (HUGE token savings)
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3. ELSE (new error):
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→ Root cause investigation
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→ Document solution for future reference
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→ Update docs/memory/solutions_learned.jsonl
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4. Self-Reflection (Document Learning):
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"Reflection:
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❌ What went wrong: [specific phenomenon]
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🔍 Root cause: [fundamental reason]
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💡 Why it happened: [what was skipped/missed]
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✅ Prevention: [steps to prevent recurrence]
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📝 Learning: [key takeaway for future]"
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Storage (ALWAYS):
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→ docs/memory/solutions_learned.jsonl (append-only)
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Format: {"error":"...","solution":"...","date":"YYYY-MM-DD"}
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Storage (for failures):
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→ docs/mistakes/[feature]-YYYY-MM-DD.md (detailed analysis)
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Result:
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✅ <10% error recurrence rate (same error twice)
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✅ Instant resolution for known errors (0 tokens)
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✅ Continuous learning and improvement
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```
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## Self-Improvement Workflow
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```yaml
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BEFORE: Check CLAUDE.md + docs/*.md + existing implementations
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CONFIDENCE: Assess confidence (High/Medium/Low) → STOP if <70%
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DURING: Note decisions, edge cases, patterns
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SELF-CHECK: Run The Four Questions → BLOCK if no evidence
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AFTER: Write docs/patterns/ OR docs/mistakes/ + Update CLAUDE.md if global
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MISTAKE: STOP → Root cause → docs/mistakes/[feature]-[date].md → Prevention checklist
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MISTAKE: STOP → Reflexion Pattern → docs/mistakes/[feature]-[date].md → Prevention checklist
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MONTHLY: find docs -mtime +180 -delete + Merge duplicates + Update dates
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```
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---
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**See Also**: `pm-agent-guide.md` for detailed philosophy, examples, and quality standards.
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**See Also**:
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- `pm-agent-guide.md` for detailed philosophy, examples, and quality standards
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- `docs/patterns/parallel-with-reflection.md` for Wave → Checkpoint → Wave pattern
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- `docs/reference/pm-agent-autonomous-reflection.md` for comprehensive architecture
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