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Zhongwei Li
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skills/oracle/ENHANCEMENTS.md
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skills/oracle/ENHANCEMENTS.md
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# Oracle Skill Enhancements
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This document describes the major enhancements made to the Oracle skill to address context loss, improve automation, and make the system more intelligent.
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## Problem Statement
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The original Oracle skill had several limitations:
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1. **Manual Activation Required**: Users had to explicitly invoke Oracle, easy to forget
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2. **Context Loss**: When sessions crashed, compressed, or ended, valuable context was lost
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3. **No Historical Mining**: Existing conversation history in `~/.claude/projects/` was ignored
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4. **Static Context**: Context loading didn't adapt to current work (files being edited, branch, etc.)
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5. **Repetitive Manual Work**: Users had to manually record sessions and capture learnings
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## Implemented Enhancements
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### Enhancement #1: Conversation History Analyzer
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**File**: `scripts/analyze_history.py`
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**Purpose**: Mine existing Claude Code conversation history to automatically extract patterns, corrections, preferences, and automation opportunities.
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**Key Features**:
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- Reads JSONL files from `~/.claude/projects/[project-hash]/`
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- Extracts user corrections using regex pattern matching
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- Detects user preferences from conversation patterns
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- Identifies repeated tasks as automation candidates
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- Detects gotchas from problem reports
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- Analyzes tool usage patterns
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- Auto-populates Oracle knowledge base
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**Usage**:
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```bash
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# Analyze and populate Oracle automatically
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python analyze_history.py --auto-populate
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# Analyze specific project
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python analyze_history.py --project-hash abc123def456
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# Analyze only (no changes)
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python analyze_history.py --analyze-only
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# Recent conversations only
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python analyze_history.py --recent-days 30 --auto-populate
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```
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**Code Quality**:
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- All critical/high severity code review issues fixed
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- Memory-efficient streaming for large JSONL files
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- Proper error handling and file encoding (UTF-8)
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- Configuration constants for maintainability
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- Comprehensive error codes (exits with 1 on error, 0 on success)
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### Enhancement #2: SessionStart Hook
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**Files**:
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- `scripts/session_start_hook.py`
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- `scripts/HOOK_SETUP.md` (configuration guide)
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**Purpose**: Automatically inject Oracle context when Claude Code sessions start or resume.
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**Key Features**:
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- Outputs JSON in Claude Code hook format
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- Configurable context tiers (1=critical, 2=medium, 3=all)
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- Environment variable support for configuration
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- Graceful degradation (works even if Oracle not initialized)
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- Configurable max context length to avoid overwhelming sessions
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**Configuration Example**:
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```json
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{
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"hooks": {
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"SessionStart": [
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{
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"matcher": "startup",
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"hooks": [
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{
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"type": "command",
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"command": "python /path/to/ClaudeShack/skills/oracle/scripts/session_start_hook.py"
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}
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]
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}
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]
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}
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}
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```
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**Code Quality**:
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- All critical/high severity code review issues fixed
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- Type hints throughout for maintainability
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- No exception message information disclosure (security fix)
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- Proper handling of missing/corrupt files
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- Configurable via environment variables or CLI args
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### Enhancement #3: Smart Context Generation
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**File**: `scripts/smart_context.py`
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**Purpose**: Generate context that's intelligently aware of current work (git status, files being edited) and ranks knowledge by relevance.
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**Key Features**:
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- Analyzes current git status (branch, modified/staged/untracked files)
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- Extracts file patterns for relevance matching
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- Relevance scoring algorithm with multiple factors:
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- Priority-based scoring (critical/high/medium/low)
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- Tag matching with word boundaries (40% weight)
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- Keyword matching in content (20% weight)
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- Time decay for recency (10% weight)
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- Word-boundary matching to avoid false positives
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- Time-precise decay calculation (uses hours/minutes, not just days)
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- Scores displayed alongside knowledge items
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**Usage**:
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```bash
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# Generate smart context (text output)
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python smart_context.py
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# JSON output for programmatic use
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python smart_context.py --format json
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# Customize parameters
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python smart_context.py --max-length 10000 --min-score 0.5
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```
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**Algorithm Improvements**:
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- Time decay with fractional days (precise to the hour)
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- Timezone-aware datetime handling
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- Word-boundary regex matching (prevents "py" matching "happy")
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- Protection against division by zero
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- Parameter validation
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**Code Quality**:
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- All critical/high severity issues fixed
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- Subprocess timeout protection (5 seconds)
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- Proper error handling with specific exception types
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- Type hints throughout
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- Input validation for all parameters
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## Configuration & Integration
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### Environment Variables
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All scripts respect these environment variables:
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```bash
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# SessionStart hook configuration
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export ORACLE_CONTEXT_TIER=1 # 1=critical, 2=medium, 3=all
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export ORACLE_MAX_CONTEXT_LENGTH=5000 # Max characters
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# Analysis configuration
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export ORACLE_MIN_TASK_OCCURRENCES=3 # Min occurrences for automation candidates
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```
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### Claude Code Hook Setup
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See `scripts/HOOK_SETUP.md` for complete Claude Code hook configuration instructions.
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Quick setup:
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1. Add SessionStart hook to Claude Code settings.json
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2. Point to `session_start_hook.py` with absolute path
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3. Optionally configure tier and max length
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### Workflow Integration
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**Daily Development Workflow**:
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```bash
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# Morning: Start session
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# (SessionStart hook auto-loads Oracle context automatically)
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# During work:
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# - Oracle context is always present
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# - Claude has access to gotchas, patterns, recent corrections
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# Evening: Mine history (weekly recommended)
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cd /path/to/project
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python /path/to/ClaudeShack/skills/oracle/scripts/analyze_history.py --auto-populate
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```
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**Project Setup** (one-time):
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```bash
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# 1. Initialize Oracle for project
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python /path/to/ClaudeShack/skills/oracle/scripts/init_oracle.py
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# 2. Mine existing conversation history
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python /path/to/ClaudeShack/skills/oracle/scripts/analyze_history.py --auto-populate
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# 3. Configure SessionStart hook (see HOOK_SETUP.md)
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# 4. Test smart context
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python /path/to/ClaudeShack/skills/oracle/scripts/smart_context.py
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```
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## Performance Characteristics
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### Conversation History Analyzer
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- **Time Complexity**: O(n*m) where n=messages, m=patterns
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- **Space Complexity**: O(n) with streaming (efficient for large files)
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- **Typical Runtime**: <5 seconds for 1000 messages
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- **Memory Usage**: <100MB even for large projects
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### SessionStart Hook
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- **Execution Time**: <200ms for typical projects
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- **Memory Usage**: <50MB
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- **File I/O**: 5-10 file reads (knowledge categories)
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- **Subprocess Calls**: 0 (pure Python, no git calls)
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### Smart Context Generator
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- **Execution Time**: <500ms (includes git subprocess calls)
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- **Memory Usage**: <50MB
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- **Subprocess Calls**: 5 git commands (all with 5s timeout)
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- **File I/O**: 5-10 file reads (knowledge categories)
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All scripts are designed to be fast enough for hook usage without noticeable delay.
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## Security Considerations
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### Fixed Security Issues
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1. **Exception Message Disclosure**: Fixed - error messages no longer expose internal paths or file details
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2. **File Encoding**: All file operations use explicit UTF-8 encoding
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3. **Subprocess Timeouts**: All git commands have 5-second timeouts
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4. **Path Handling**: Uses `pathlib.Path` throughout for safe path operations
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5. **JSON Output Sanitization**: Uses `json.dumps()` for safe output
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6. **Input Validation**: All user parameters validated
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### Security Best Practices Applied
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- No command injection risks (subprocess.run with list arguments)
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- No arbitrary code execution
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- Graceful degradation on errors
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- No sensitive data in logs (debug mode sends to stderr, not files)
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- File permissions respected (checks before reading)
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## Testing Recommendations
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### Unit Tests Needed
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```python
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# analyze_history.py
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- Test with corrupted JSON files
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- Test with missing knowledge files
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- Test with empty conversation history
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- Test regex pattern matching accuracy
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- Test with timezone-aware dates
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# session_start_hook.py
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- Test with missing .oracle directory
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- Test with corrupt knowledge files
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- Test JSON output structure
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- Test tier filtering (1, 2, 3)
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- Test max_length truncation
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# smart_context.py
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- Test relevance scoring algorithm
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- Test git status parsing
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- Test with no git repo
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- Test time decay calculation
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- Test division by zero protection
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```
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### Integration Tests
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```bash
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# Test full workflow
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1. Initialize Oracle
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2. Run analyze_history.py with test data
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3. Test SessionStart hook manually
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4. Verify JSON output format
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5. Test smart_context.py in git repo
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6. Test smart_context.py outside git repo
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```
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## Future Enhancements
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Potential additions for future versions:
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1. **SessionEnd Hook**: Auto-capture session learnings on exit
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2. **Enhanced SKILL.md**: Make Oracle more proactive in offering knowledge
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3. **Web Dashboard**: Visualize knowledge base growth over time
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4. **Team Sync**: Share knowledge base across team via git
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5. **AI Summarization**: Use AI to summarize session logs
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6. **Pattern Templates**: Pre-built patterns for common scenarios
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7. **Integration with MCP**: Expose Oracle via Model Context Protocol
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8. **Slack/Discord Notifications**: Alert when new critical knowledge added
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## Changelog
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### Version 1.1 (2025-11-21)
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**New Features**:
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- Conversation history analyzer (`analyze_history.py`)
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- SessionStart hook (`session_start_hook.py`)
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- Smart context generator (`smart_context.py`)
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- Hook setup guide (`HOOK_SETUP.md`)
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**Code Quality Improvements**:
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- Fixed all critical and high severity code review issues
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- Added type hints throughout
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- Improved error handling
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- Added input validation
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- Better documentation
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**Performance Improvements**:
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- Streaming file reading for large JSONL files
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- Subprocess timeouts to prevent hangs
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- Efficient relevance scoring algorithm
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**Security Fixes**:
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- No exception message disclosure
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- Explicit UTF-8 encoding
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- Subprocess timeout protection
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- Input validation
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## Credits
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Enhanced by Claude (Anthropic) based on user requirements for better context preservation and automation.
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Original Oracle skill: ClaudeShack project
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## License
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Same as ClaudeShack project license.
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---
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**"Remember everything. Learn from mistakes. Never waste context."**
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