feat: Integrate Learning Atomizer Core (LAC) and master instructions
Add persistent knowledge system that enables Atomizer to learn from every session and improve over time. ## New Files - knowledge_base/lac.py: LAC class with optimization memory, session insights, and skill evolution tracking - knowledge_base/__init__.py: Package initialization - .claude/skills/modules/learning-atomizer-core.md: Full LAC skill documentation - docs/07_DEVELOPMENT/ATOMIZER_CLAUDE_CODE_INSTRUCTIONS.md: Master instructions ## Updated Files - CLAUDE.md: Added LAC section, communication style, AVERVS execution framework, error classification, and "Atomizer Claude" identity - 00_BOOTSTRAP.md: Added session startup/closing checklists with LAC integration - 01_CHEATSHEET.md: Added LAC CLI and Python API quick reference - 02_CONTEXT_LOADER.md: Added LAC query section and anti-pattern ## LAC Features - Query similar past optimizations before starting new ones - Record insights (failures, success patterns, workarounds) - Record optimization outcomes for future reference - Suggest protocol improvements based on discoveries - Simple JSONL storage (no database required) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Antoine
@@ -19,12 +19,44 @@ requires_skills: []
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**Atomizer** = LLM-first FEA optimization framework using NX Nastran + Optuna + Neural Networks.
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**Your Role**: Help users set up, run, and analyze structural optimization studies through conversation.
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**Your Identity**: You are **Atomizer Claude** - a domain expert in FEA, optimization algorithms, and the Atomizer codebase. Not a generic assistant.
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**Core Philosophy**: "Talk, don't click." Users describe what they want; you configure and execute.
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---
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## Session Startup Checklist
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On **every new session**, complete these steps:
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```
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┌─────────────────────────────────────────────────────────────────────┐
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│ SESSION STARTUP │
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├─────────────────────────────────────────────────────────────────────┤
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│ │
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│ STEP 1: Environment Check │
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│ □ Verify conda environment: conda activate atomizer │
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│ □ Check current directory context │
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│ │
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│ STEP 2: Context Loading │
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│ □ CLAUDE.md loaded (system instructions) │
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│ □ This file (00_BOOTSTRAP.md) for task routing │
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│ □ Check for active study in studies/ directory │
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│ │
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│ STEP 3: Knowledge Query (LAC) │
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│ □ Query knowledge_base/lac/ for relevant prior learnings │
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│ □ Note any pending protocol updates │
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│ │
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│ STEP 4: User Context │
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│ □ What is the user trying to accomplish? │
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│ □ Is there an active study context? │
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│ □ What privilege level? (default: user) │
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│ │
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└─────────────────────────────────────────────────────────────────────┘
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```
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---
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## Task Classification Tree
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When a user request arrives, classify it:
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@@ -213,3 +245,62 @@ docs/protocols/
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2. If unclear → Ask user clarifying question
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3. If complex task → Read `01_CHEATSHEET.md` for quick reference
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4. If need detailed loading rules → Read `02_CONTEXT_LOADER.md`
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---
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## Session Closing Checklist
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Before ending a session, complete:
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```
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┌─────────────────────────────────────────────────────────────────────┐
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│ SESSION CLOSING │
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├─────────────────────────────────────────────────────────────────────┤
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│ │
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│ 1. VERIFY WORK IS SAVED │
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│ □ All files committed or saved │
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│ □ Study configs are valid │
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│ □ Any running processes noted │
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│ │
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│ 2. RECORD LEARNINGS TO LAC │
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│ □ Any failures and their solutions → failure.jsonl │
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│ □ Success patterns discovered → success_pattern.jsonl │
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│ □ User preferences noted → user_preference.jsonl │
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│ □ Protocol improvements → suggested_updates.jsonl │
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│ │
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│ 3. RECORD OPTIMIZATION OUTCOMES │
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│ □ If optimization completed, record to optimization_memory/ │
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│ □ Include: method, geometry_type, converged, convergence_trial │
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│ │
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│ 4. SUMMARIZE FOR USER │
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│ □ What was accomplished │
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│ □ Current state of any studies │
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│ □ Recommended next steps │
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│ │
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└─────────────────────────────────────────────────────────────────────┘
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```
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### Session Summary Template
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```markdown
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# Session Summary
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**Date**: {YYYY-MM-DD}
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**Study Context**: {study_name or "General"}
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## Accomplished
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- {task 1}
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- {task 2}
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## Current State
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- Study: {status}
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- Trials: {N completed}
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- Next action needed: {action}
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## Learnings Recorded
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- {insight 1}
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## Recommended Next Steps
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1. {step 1}
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2. {step 2}
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```
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@@ -174,6 +174,41 @@ python -c "import optuna; s=optuna.load_study('my_study', 'sqlite:///2_results/s
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---
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## LAC (Learning Atomizer Core) Commands
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```bash
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# View LAC statistics
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python knowledge_base/lac.py stats
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# Generate full LAC report
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python knowledge_base/lac.py report
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# View pending protocol updates
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python knowledge_base/lac.py pending
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# Query insights for a context
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python knowledge_base/lac.py insights "bracket mass optimization"
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```
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### Python API Quick Reference
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```python
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from knowledge_base.lac import get_lac
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lac = get_lac()
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# Query prior knowledge
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insights = lac.get_relevant_insights("bracket mass")
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similar = lac.query_similar_optimizations("bracket", ["mass"])
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rec = lac.get_best_method_for("bracket", n_objectives=1)
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# Record learning
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lac.record_insight("success_pattern", "context", "insight", confidence=0.8)
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# Record optimization outcome
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lac.record_optimization_outcome(study_name="...", geometry_type="...", ...)
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```
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---
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## Error Quick Fixes
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| Error | Likely Cause | Quick Fix |
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@@ -22,6 +22,29 @@ requires_skills:
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2. **Expand on demand**: Load additional modules when signals detected
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3. **Single source of truth**: Each concept defined in ONE place
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4. **Layer progression**: Bootstrap → Operations → System → Extensions
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5. **Learn from history**: Query LAC for relevant prior knowledge
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---
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## Knowledge Base Query (LAC)
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**Before starting any task**, check LAC for relevant insights:
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```python
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from knowledge_base.lac import get_lac
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lac = get_lac()
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# Query relevant insights for the task
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insights = lac.get_relevant_insights("bracket mass optimization")
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# Check similar past optimizations
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similar = lac.query_similar_optimizations("bracket", ["mass"])
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# Get method recommendation
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rec = lac.get_best_method_for("bracket", n_objectives=1)
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```
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**Full LAC documentation**: `.claude/skills/modules/learning-atomizer-core.md`
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---
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@@ -321,3 +344,4 @@ When multiple modules could apply, load in this order:
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3. **Don't skip core skill**: For study creation, always load core first
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4. **Don't mix incompatible protocols**: P10 (single-obj) vs P11 (multi-obj)
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5. **Don't load deprecated docs**: Only use docs/protocols/* structure
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6. **Don't skip LAC query**: Always check prior knowledge before starting
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234
.claude/skills/modules/learning-atomizer-core.md
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234
.claude/skills/modules/learning-atomizer-core.md
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@@ -0,0 +1,234 @@
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---
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skill_id: SKILL_MODULE_LAC
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version: 1.0
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last_updated: 2025-12-11
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type: module
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code_dependencies:
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- knowledge_base/lac.py
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requires_skills:
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- SKILL_000
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---
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# Learning Atomizer Core (LAC) Integration
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**Version**: 1.0
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**Updated**: 2025-12-11
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**Purpose**: Enable Claude to learn from every session and improve over time.
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---
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## Overview
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LAC is Atomizer's persistent memory system. It stores:
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- **Optimization outcomes** - What methods worked for what problems
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- **Session insights** - Learnings, failures, and workarounds
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- **Skill evolution** - Suggested protocol improvements
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---
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## Directory Structure
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```
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knowledge_base/lac/
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├── optimization_memory/ # What worked for what geometry
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│ ├── bracket.jsonl
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│ ├── beam.jsonl
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│ └── mirror.jsonl
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├── session_insights/ # Learnings from sessions
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│ ├── failure.jsonl
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│ ├── success_pattern.jsonl
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│ ├── user_preference.jsonl
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│ └── protocol_clarification.jsonl
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└── skill_evolution/ # Protocol improvements
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└── suggested_updates.jsonl
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```
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---
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## When to Use LAC
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### At Session Start
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Query LAC for relevant prior knowledge:
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```python
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from knowledge_base.lac import get_lac
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lac = get_lac()
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# Before starting a bracket optimization
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similar = lac.query_similar_optimizations(
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geometry_type="bracket",
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objectives=["mass"],
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converged_only=True
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)
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# Get method recommendation
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rec = lac.get_best_method_for("bracket", n_objectives=1)
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if rec:
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print(f"Recommended: {rec['method']} (success rate: {rec['success_rate']:.0%})")
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# Get relevant insights
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insights = lac.get_relevant_insights("bracket mass optimization")
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```
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### During Session
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Record learnings as they occur:
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```python
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# Record a failure and its solution
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lac.record_insight(
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category="failure",
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context="Modal analysis with CMA-ES sampler",
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insight="CMA-ES struggles with discrete frequency targets. TPE works better.",
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confidence=0.8,
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tags=["cma-es", "modal", "frequency"]
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)
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# Record a success pattern
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lac.record_insight(
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category="success_pattern",
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context="Bracket optimization with 5+ design variables",
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insight="20 startup trials before TPE improves convergence by 30%",
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confidence=0.85,
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tags=["tpe", "startup_trials", "bracket"]
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)
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# Record user preference
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lac.record_insight(
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category="user_preference",
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context="Report generation",
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insight="User prefers Pareto plots with actual values instead of normalized",
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confidence=0.9,
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tags=["plotting", "pareto", "reporting"]
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)
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```
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### At Session End
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Record optimization outcomes:
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```python
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lac.record_optimization_outcome(
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study_name="bracket_v3",
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geometry_type="bracket",
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method="TPE",
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objectives=["mass"],
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design_vars=4,
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trials=100,
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converged=True,
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convergence_trial=67,
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best_value=2.34,
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notes="Good convergence with 20 startup trials"
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)
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```
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---
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## Insight Categories
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| Category | Use When | Example |
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|----------|----------|---------|
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| `failure` | Something failed and you found the cause | "CMA-ES fails on discrete targets" |
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| `success_pattern` | An approach worked particularly well | "TPE with n_startup=20 converges faster" |
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| `user_preference` | User expressed a preference | "User prefers minimal output" |
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| `protocol_clarification` | A protocol needed interpretation | "SYS_12 unclear on Zernike subcase numbering" |
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| `performance` | Performance-related observation | "GNN inference 100x faster than FEA" |
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| `workaround` | Found a workaround for a known issue | "Load _i.prt before UpdateFemodel()" |
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---
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## Protocol Update Suggestions
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When you discover a protocol could be improved:
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```python
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lac.suggest_protocol_update(
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protocol="SYS_15_METHOD_SELECTOR.md",
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section="Modal Optimization",
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current_text="Use TPE or CMA-ES for frequency optimization",
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suggested_text="Use TPE for frequency optimization. CMA-ES struggles with discrete frequency targets.",
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reason="Discovered during bracket_modal study - CMA-ES failed to converge"
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)
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```
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Review pending updates:
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```python
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pending = lac.get_pending_updates()
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for p in pending:
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print(f"- {p['protocol']}: {p['reason']}")
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```
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---
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## CLI Commands
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```bash
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# View LAC statistics
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python knowledge_base/lac.py stats
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# Generate full report
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python knowledge_base/lac.py report
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# View pending protocol updates
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python knowledge_base/lac.py pending
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# Query insights for a context
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python knowledge_base/lac.py insights "bracket mass optimization"
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```
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---
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## Integration with Protocols
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### Method Selection (SYS_15)
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Before recommending a method, check LAC:
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```python
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rec = lac.get_best_method_for(geometry_type, n_objectives)
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# Use recommendation if available, else fall back to protocol defaults
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```
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### Troubleshooting (OP_06)
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Check if this error has been seen before:
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```python
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insights = lac.get_relevant_insights(error_message, categories=["failure", "workaround"])
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```
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### Study Creation (OP_01)
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Query similar past studies for configuration hints:
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```python
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similar = lac.query_similar_optimizations(geometry_type, objectives)
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```
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---
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## Best Practices
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1. **Record failures immediately** - Don't wait until session end
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2. **Be specific** - Include enough context to be useful later
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3. **Tag appropriately** - Tags enable better retrieval
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4. **Set confidence** - Low (0.5) for hunches, high (0.9) for verified patterns
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5. **Suggest protocol updates** - Don't just note issues, propose fixes
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---
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## Example Session Flow
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```
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SESSION START
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│
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├── Query LAC for similar optimizations
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├── Query LAC for relevant insights
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├── Note any pending protocol updates
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│
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├── [User requests work]
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│
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├── During work:
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│ ├── Encounter issue? → Record to failure.jsonl
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│ ├── Find workaround? → Record to workaround.jsonl
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│ ├── User states preference? → Record to user_preference.jsonl
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│ └── Protocol unclear? → Record to protocol_clarification.jsonl
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│
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└── SESSION END
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├── Record optimization outcome (if optimization ran)
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├── Suggest any protocol updates discovered
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└── Summarize learnings for user
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```
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Reference in New Issue
Block a user