kazuki nakai 00706f0ea9
feat: comprehensive framework improvements (#447)
* refactor(docs): move core docs into framework/business/research (move-only)

- framework/: principles, rules, flags (思想・行動規範)
- business/: symbols, examples (ビジネス領域)
- research/: config (調査設定)
- All files renamed to lowercase for consistency

* docs: update references to new directory structure

- Update ~/.claude/CLAUDE.md with new paths
- Add migration notice in core/MOVED.md
- Remove pm.md.backup
- All @superclaude/ references now point to framework/business/research/

* fix(setup): update framework_docs to use new directory structure

- Add validate_prerequisites() override for multi-directory validation
- Add _get_source_dirs() for framework/business/research directories
- Override _discover_component_files() for multi-directory discovery
- Override get_files_to_install() for relative path handling
- Fix get_size_estimate() to use get_files_to_install()
- Fix uninstall/update/validate to use install_component_subdir

Fixes installation validation errors for new directory structure.

Tested: make dev installs successfully with new structure
  - framework/: flags.md, principles.md, rules.md
  - business/: examples.md, symbols.md
  - research/: config.md

* refactor(modes): update component references for docs restructure

* chore: remove redundant docs after PLANNING.md migration

Cleanup after Self-Improvement Loop implementation:

**Deleted (21 files, ~210KB)**:
- docs/Development/ - All content migrated to PLANNING.md & TASK.md
  * ARCHITECTURE.md (15KB) → PLANNING.md
  * TASKS.md (3.7KB) → TASK.md
  * ROADMAP.md (11KB) → TASK.md
  * PROJECT_STATUS.md (4.2KB) → outdated
  * 13 PM Agent research files → archived in KNOWLEDGE.md
- docs/PM_AGENT.md - Old implementation status
- docs/pm-agent-implementation-status.md - Duplicate
- docs/templates/ - Empty directory

**Retained (valuable documentation)**:
- docs/memory/ - Active session metrics & context
- docs/patterns/ - Reusable patterns
- docs/research/ - Research reports
- docs/user-guide*/ - User documentation (4 languages)
- docs/reference/ - Reference materials
- docs/getting-started/ - Quick start guides
- docs/agents/ - Agent-specific guides
- docs/testing/ - Test procedures

**Result**:
- Eliminated redundancy after Root Documents consolidation
- Preserved all valuable content in PLANNING.md, TASK.md, KNOWLEDGE.md
- Maintained user-facing documentation structure

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* refactor: relocate PM modules to commands/modules

- Move modules to superclaude/commands/modules/
- Organize command-specific modules under commands/

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat: add self-improvement loop with 4 root documents

Implements Self-Improvement Loop based on Cursor's proven patterns:

**New Root Documents**:
- PLANNING.md: Architecture, design principles, 10 absolute rules
- TASK.md: Current tasks with priority (🔴🟡🟢)
- KNOWLEDGE.md: Accumulated insights, best practices, failures
- README.md: Updated with developer documentation links

**Key Features**:
- Session Start Protocol: Read docs → Git status → Token budget → Ready
- Evidence-Based Development: No guessing, always verify
- Parallel Execution Default: Wave → Checkpoint → Wave pattern
- Mac Environment Protection: Docker-first, no host pollution
- Failure Pattern Learning: Past mistakes become prevention rules

**Cleanup**:
- Removed: docs/memory/checkpoint.json, current_plan.json (migrated to TASK.md)
- Enhanced: setup/components/commands.py (module discovery)

**Benefits**:
- LLM reads rules at session start → consistent quality
- Past failures documented → no repeats
- Progressive knowledge accumulation → continuous improvement
- 3.5x faster execution with parallel patterns

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* test: validate Self-Improvement Loop workflow

Tested complete cycle: Read docs → Extract rules → Execute task → Update docs

Test Results:
- Session Start Protocol:  All 6 steps successful
- Rule Extraction:  10/10 absolute rules identified from PLANNING.md
- Task Identification:  Next tasks identified from TASK.md
- Knowledge Application:  Failure patterns accessed from KNOWLEDGE.md
- Documentation Update:  TASK.md and KNOWLEDGE.md updated with completed work
- Confidence Score: 95% (exceeds 70% threshold)

Proved Self-Improvement Loop closes: Execute → Learn → Update → Improve

* refactor: responsibility-driven component architecture

Rename components to reflect their responsibilities:
- framework_docs.py → knowledge_base.py (KnowledgeBaseComponent)
- modes.py → behavior_modes.py (BehaviorModesComponent)
- agents.py → agent_personas.py (AgentPersonasComponent)
- commands.py → slash_commands.py (SlashCommandsComponent)
- mcp.py → mcp_integration.py (MCPIntegrationComponent)

Each component now clearly documents its responsibility:
- knowledge_base: Framework knowledge initialization
- behavior_modes: Execution mode definitions
- agent_personas: AI agent personality definitions
- slash_commands: CLI command registration
- mcp_integration: External tool integration

Benefits:
- Self-documenting architecture
- Clear responsibility boundaries
- Easy to navigate and extend
- Scalable for future hierarchical organization

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: add project-specific CLAUDE.md with UV rules

- Document UV as required Python package manager
- Add common operations and integration examples
- Document project structure and component architecture
- Provide development workflow guidelines

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fix: resolve installation failures after framework_docs rename

## Problems Fixed
1. **Syntax errors**: Duplicate docstrings in all component files (line 1)
2. **Dependency mismatch**: Stale framework_docs references after rename to knowledge_base

## Changes
- Fix docstring format in all component files (behavior_modes, agent_personas, slash_commands, mcp_integration)
- Update all dependency references: framework_docs → knowledge_base
- Update component registration calls in knowledge_base.py (5 locations)
- Update install.py files in both setup/ and superclaude/ (5 locations total)
- Fix documentation links in README-ja.md and README-zh.md

## Verification
 All components load successfully without syntax errors
 Dependency resolution works correctly
 Installation completes in 0.5s with all validations passing
 make dev succeeds

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat: add automated README translation workflow

## New Features
- **Auto-translation workflow** using GPT-Translate
- Automatically translates README.md to Chinese (ZH) and Japanese (JA)
- Triggers on README.md changes to master/main branches
- Cost-effective: ~¥90/month for typical usage

## Implementation Details
- Uses OpenAI GPT-4 for high-quality translations
- GitHub Actions integration with gpt-translate@v1.1.11
- Secure API key management via GitHub Secrets
- Automatic commit and PR creation on translation updates

## Files Added
- `.github/workflows/translation-sync.yml` - Auto-translation workflow
- `docs/Development/translation-workflow.md` - Setup guide and documentation

## Setup Required
Add `OPENAI_API_KEY` to GitHub repository secrets to enable auto-translation.

## Benefits
- 🤖 Automated translation on every README update
- 💰 Low cost (~$0.06 per translation)
- 🛡️ Secure API key storage
- 🔄 Consistent translation quality across languages

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fix(mcp): update airis-mcp-gateway URL to correct organization

Fixes #440

## Problem
Code referenced non-existent `oraios/airis-mcp-gateway` repository,
causing MCP installation to fail completely.

## Root Cause
- Repository was moved to organization: `agiletec-inc/airis-mcp-gateway`
- Old reference `oraios/airis-mcp-gateway` no longer exists
- Users reported "not a python/uv module" error

## Changes
- Update install_command URL: oraios → agiletec-inc
- Update run_command URL: oraios → agiletec-inc
- Location: setup/components/mcp_integration.py lines 37-38

## Verification
 Correct URL now references active repository
 MCP installation will succeed with proper organization
 No other code references oraios/airis-mcp-gateway

## Related Issues
- Fixes #440 (Airis-mcp-gateway url has changed)
- Related to #442 (MCP update issues)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

* feat: replace cloud translation with local Neural CLI

## Changes

### Removed (OpenAI-dependent)
-  `.github/workflows/translation-sync.yml` - GPT-Translate workflow
-  `docs/Development/translation-workflow.md` - OpenAI setup docs

### Added (Local Ollama-based)
-  `Makefile`: New `make translate` target using Neural CLI
-  `docs/Development/translation-guide.md` - Neural CLI guide

## Benefits

**Before (GPT-Translate)**:
- 💰 Monthly cost: ~¥90 (OpenAI API)
- 🔑 Requires API key setup
- 🌐 Data sent to external API
- ⏱️ Network latency

**After (Neural CLI)**:
-  **$0 cost** - Fully local execution
-  **No API keys** - Zero setup friction
-  **Privacy** - No external data transfer
-  **Fast** - ~1-2 min per README
-  **Offline capable** - Works without internet

## Technical Details

**Neural CLI**:
- Built in Rust with Tauri
- Uses Ollama + qwen2.5:3b model
- Binary size: 4.0MB
- Auto-installs to ~/.local/bin/

**Usage**:
```bash
make translate  # Translates README.md → README-zh.md, README-ja.md
```

## Requirements

- Ollama installed: `curl -fsSL https://ollama.com/install.sh | sh`
- Model downloaded: `ollama pull qwen2.5:3b`
- Neural CLI built: `cd ~/github/neural/src-tauri && cargo build --bin neural-cli --release`

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

---------

Co-authored-by: kazuki <kazuki@kazukinoMacBook-Air.local>
Co-authored-by: Claude <noreply@anthropic.com>
2025-10-18 20:28:10 +05:30

446 lines
9.4 KiB
Markdown

# Deep Research Configuration
## Default Settings
```yaml
research_defaults:
planning_strategy: unified
max_hops: 5
confidence_threshold: 0.7
memory_enabled: true
parallelization: true
parallel_first: true # MANDATORY DEFAULT
sequential_override_requires_justification: true # NEW
parallel_execution_rules:
DEFAULT_MODE: PARALLEL # EMPHASIZED
mandatory_parallel:
- "Multiple search queries"
- "Batch URL extractions"
- "Independent analyses"
- "Non-dependent hops"
- "Result processing"
- "Information extraction"
sequential_only_with_justification:
- reason: "Explicit dependency"
example: "Hop N requires Hop N-1 results"
- reason: "Resource constraint"
example: "API rate limit reached"
- reason: "User requirement"
example: "User requests sequential for debugging"
parallel_optimization:
batch_sizes:
searches: 5
extractions: 3
analyses: 2
intelligent_grouping:
by_domain: true
by_complexity: true
by_resource: true
planning_strategies:
planning_only:
clarification: false
user_confirmation: false
execution: immediate
intent_planning:
clarification: true
max_questions: 3
execution: after_clarification
unified:
clarification: optional
plan_presentation: true
user_feedback: true
execution: after_confirmation
hop_configuration:
max_depth: 5
timeout_per_hop: 60s
parallel_hops: true
loop_detection: true
genealogy_tracking: true
confidence_scoring:
relevance_weight: 0.5
completeness_weight: 0.5
minimum_threshold: 0.6
target_threshold: 0.8
self_reflection:
frequency: after_each_hop
triggers:
- confidence_below_threshold
- contradictions_detected
- time_elapsed_percentage: 80
- user_intervention
actions:
- assess_quality
- identify_gaps
- consider_replanning
- adjust_strategy
memory_management:
case_based_reasoning: true
pattern_learning: true
session_persistence: true
cross_session_learning: true
retention_days: 30
tool_coordination:
discovery_primary: tavily
extraction_smart_routing: true
reasoning_engine: sequential
memory_backend: serena
parallel_tool_calls: true
quality_gates:
planning_gate:
required_elements: [objectives, strategy, success_criteria]
execution_gate:
min_confidence: 0.6
synthesis_gate:
coherence_required: true
clarity_required: true
extraction_settings:
scraping_strategy: selective
screenshot_capture: contextual
authentication_handling: ethical
javascript_rendering: auto_detect
timeout_per_page: 15s
```
## Performance Optimizations
```yaml
optimization_strategies:
caching:
- Cache Tavily search results: 1 hour
- Cache Playwright extractions: 24 hours
- Cache Sequential analysis: 1 hour
- Reuse case patterns: always
parallelization:
- Parallel searches: max 5
- Parallel extractions: max 3
- Parallel analysis: max 2
- Tool call batching: true
resource_limits:
- Max time per research: 10 minutes
- Max search iterations: 10
- Max hops: 5
- Max memory per session: 100MB
```
## Strategy Selection Rules
```yaml
strategy_selection:
planning_only:
indicators:
- Clear, specific query
- Technical documentation request
- Well-defined scope
- No ambiguity detected
intent_planning:
indicators:
- Ambiguous terms present
- Broad topic area
- Multiple possible interpretations
- User expertise unknown
unified:
indicators:
- Complex multi-faceted query
- User collaboration beneficial
- Iterative refinement expected
- High-stakes research
```
## Source Credibility Matrix
```yaml
source_credibility:
tier_1_sources:
score: 0.9-1.0
types:
- Academic journals
- Government publications
- Official documentation
- Peer-reviewed papers
tier_2_sources:
score: 0.7-0.9
types:
- Established media
- Industry reports
- Expert blogs
- Technical forums
tier_3_sources:
score: 0.5-0.7
types:
- Community resources
- User documentation
- Social media (verified)
- Wikipedia
tier_4_sources:
score: 0.3-0.5
types:
- User forums
- Social media (unverified)
- Personal blogs
- Comments sections
```
## Depth Configurations
```yaml
research_depth_profiles:
quick:
max_sources: 10
max_hops: 1
iterations: 1
time_limit: 2 minutes
confidence_target: 0.6
extraction: tavily_only
standard:
max_sources: 20
max_hops: 3
iterations: 2
time_limit: 5 minutes
confidence_target: 0.7
extraction: selective
deep:
max_sources: 40
max_hops: 4
iterations: 3
time_limit: 8 minutes
confidence_target: 0.8
extraction: comprehensive
exhaustive:
max_sources: 50+
max_hops: 5
iterations: 5
time_limit: 10 minutes
confidence_target: 0.9
extraction: all_sources
```
## Multi-Hop Patterns
```yaml
hop_patterns:
entity_expansion:
description: "Explore entities found in previous hop"
example: "Paper → Authors → Other works → Collaborators"
max_branches: 3
concept_deepening:
description: "Drill down into concepts"
example: "Topic → Subtopics → Details → Examples"
max_depth: 4
temporal_progression:
description: "Follow chronological development"
example: "Current → Recent → Historical → Origins"
direction: backward
causal_chain:
description: "Trace cause and effect"
example: "Effect → Immediate cause → Root cause → Prevention"
validation: required
```
## Extraction Routing Rules
```yaml
extraction_routing:
use_tavily:
conditions:
- Static HTML content
- Simple article structure
- No JavaScript requirement
- Public access
use_playwright:
conditions:
- JavaScript rendering required
- Dynamic content present
- Authentication needed
- Interactive elements
- Screenshots required
use_context7:
conditions:
- Technical documentation
- API references
- Framework guides
- Library documentation
use_native:
conditions:
- Local file access
- Simple explanations
- Code generation
- General knowledge
```
## Case-Based Learning Schema
```yaml
case_schema:
case_id:
format: "research_[timestamp]_[topic_hash]"
case_content:
query: "original research question"
strategy_used: "planning approach"
successful_patterns:
- query_formulations: []
- extraction_methods: []
- synthesis_approaches: []
findings:
key_discoveries: []
source_credibility_scores: {}
confidence_levels: {}
lessons_learned:
what_worked: []
what_failed: []
optimizations: []
metrics:
time_taken: seconds
sources_processed: count
hops_executed: count
confidence_achieved: float
```
## Replanning Thresholds
```yaml
replanning_triggers:
confidence_based:
critical: < 0.4
low: < 0.6
acceptable: 0.6-0.7
good: > 0.7
time_based:
warning: 70% of limit
critical: 90% of limit
quality_based:
insufficient_sources: < 3
contradictions: > 30%
gaps_identified: > 50%
user_based:
explicit_request: immediate
implicit_dissatisfaction: assess
```
## Output Format Templates
```yaml
output_formats:
summary:
max_length: 500 words
sections: [key_finding, evidence, sources]
confidence_display: simple
report:
sections: [executive_summary, methodology, findings, synthesis, conclusions]
citations: inline
confidence_display: detailed
visuals: included
academic:
sections: [abstract, introduction, methodology, literature_review, findings, discussion, conclusions]
citations: academic_format
confidence_display: statistical
appendices: true
```
## Error Handling
```yaml
error_handling:
tavily_errors:
api_key_missing: "Check TAVILY_API_KEY environment variable"
rate_limit: "Wait and retry with exponential backoff"
no_results: "Expand search terms or try alternatives"
playwright_errors:
timeout: "Skip source or increase timeout"
navigation_failed: "Mark as inaccessible, continue"
screenshot_failed: "Continue without visual"
quality_errors:
low_confidence: "Trigger replanning"
contradictions: "Seek additional sources"
insufficient_data: "Expand search scope"
```
## Integration Points
```yaml
mcp_integration:
tavily:
role: primary_search
fallback: native_websearch
playwright:
role: complex_extraction
fallback: tavily_extraction
sequential:
role: reasoning_engine
fallback: native_reasoning
context7:
role: technical_docs
fallback: tavily_search
serena:
role: memory_management
fallback: session_only
```
## Monitoring Metrics
```yaml
metrics_tracking:
performance:
- search_latency
- extraction_time
- synthesis_duration
- total_research_time
quality:
- confidence_scores
- source_diversity
- coverage_completeness
- contradiction_rate
efficiency:
- cache_hit_rate
- parallel_execution_rate
- memory_usage
- api_cost
learning:
- pattern_reuse_rate
- strategy_success_rate
- improvement_trajectory
```