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feat: restore complete SuperClaude framework from commit d4a17fc

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Comprehensive restoration of all agents, modes, MCP integrations, and documentation.

## 🤖 Agents Restored (20 total)
Added 17 new agent definitions to existing 3:
- backend-architect, business-panel-experts, deep-research-agent
- devops-architect, frontend-architect, learning-guide
- performance-engineer, pm-agent, python-expert
- quality-engineer, refactoring-expert, requirements-analyst
- root-cause-analyst, security-engineer, socratic-mentor
- system-architect, technical-writer

## 🎨 Behavioral Modes (7)
- MODE_Brainstorming - Multi-perspective ideation
- MODE_Business_Panel - Executive strategic analysis
- MODE_DeepResearch - Autonomous research
- MODE_Introspection - Meta-cognitive analysis
- MODE_Orchestration - Tool coordination
- MODE_Task_Management - Systematic organization
- MODE_Token_Efficiency - Context optimization

## 🔌 MCP Server Integration (8)
Documentation and configs for:
- Tavily (web search)
- Serena (session persistence)
- Sequential (token-efficient reasoning)
- Context7 (documentation lookup)
- Playwright (browser automation)
- Magic (UI components)
- Morphllm (model transformation)
- Chrome DevTools (performance)

## 📚 Core Documentation (6)
- PRINCIPLES.md, RULES.md, FLAGS.md
- RESEARCH_CONFIG.md
- BUSINESS_PANEL_EXAMPLES.md, BUSINESS_SYMBOLS.md

## 📖 Documentation Restored (152 files)
- User-Guide (en, jp, kr, zh) - 24 files
- Developer-Guide - 5 files
- Development docs - 10 files
- Reference docs - 10 files
- Getting-Started - 2 files
- Plus examples and templates

## 📦 Package Configuration
Updated pyproject.toml and MANIFEST.in to include:
- modes/**/*.md
- mcp/**/*.md, **/*.json
- core/**/*.md
- examples/**/*.md
- Comprehensive docs in distribution

## 📁 Directory Structure
plugins/superclaude/ and src/superclaude/:
- agents/ (20 files)
- modes/ (7 files)
- mcp/ (8 docs + 8 configs)
- core/ (6 files)
- examples/ (workflow examples)

docs/:
- 152 markdown files
- Multi-language support (en, jp, kr, zh)
- Comprehensive guides and references

## 📊 Statistics
- Commands: 30
- Agents: 20
- Modes: 7
- MCP Servers: 8
- Documentation Files: 152
- Total Resource Files: 200+

Created docs/reference/comprehensive-features.md with complete inventory.

Source: commit d4a17fc
Total changes: 150+ files added/modified

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

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
mithun50
2025-11-13 16:16:05 +01:00
parent ab8904bc8c
commit 3762d6ab24
158 changed files with 32205 additions and 19 deletions
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plugins/superclaude/examples/deep_research_workflows.md Normal file
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# Deep Research Workflows
## Example 1: Planning-Only Strategy
### Scenario
Clear research question: "Latest TensorFlow 3.0 features"
### Execution
```bash
/sc:research "Latest TensorFlow 3.0 features" --strategy planning-only --depth standard
```
### Workflow
```yaml
1. Planning (Immediate):
- Decompose: Official docs, changelog, tutorials
- No user clarification needed
2. Execution:
- Hop 1: Official TensorFlow documentation
- Hop 2: Recent tutorials and examples
- Confidence: 0.85 achieved
3. Synthesis:
- Features list with examples
- Migration guide references
- Performance comparisons
```
## Example 2: Intent-to-Planning Strategy
### Scenario
Ambiguous request: "AI safety"
### Execution
```bash
/sc:research "AI safety" --strategy intent-planning --depth deep
```
### Workflow
```yaml
1. Intent Clarification:
Questions:
- "Are you interested in technical AI alignment, policy/governance, or current events?"
- "What's your background level (researcher, developer, general interest)?"
- "Any specific AI systems or risks of concern?"
2. User Response:
- "Technical alignment for LLMs, researcher level"
3. Refined Planning:
- Focus on alignment techniques
- Academic sources priority
- Include recent papers
4. Multi-Hop Execution:
- Hop 1: Recent alignment papers
- Hop 2: Key researchers and labs
- Hop 3: Emerging techniques
- Hop 4: Open problems
5. Self-Reflection:
- Coverage: Complete ✓
- Depth: Adequate ✓
- Confidence: 0.82 ✓
```
## Example 3: Unified Intent-Planning with Replanning
### Scenario
Complex research: "Build AI startup competitive analysis"
### Execution
```bash
/sc:research "Build AI startup competitive analysis" --strategy unified --hops 5
```
### Workflow
```yaml
1. Initial Plan Presentation:
Proposed Research Areas:
- Current AI startup landscape
- Funding and valuations
- Technology differentiators
- Market positioning
- Growth strategies
"Does this cover your needs? Any specific competitors or aspects to focus on?"
2. User Adjustment:
"Focus on code generation tools, include pricing and technical capabilities"
3. Revised Multi-Hop Research:
- Hop 1: List of code generation startups
- Hop 2: Technical capabilities comparison
- Hop 3: Pricing and business models
- Hop 4: Customer reviews and adoption
- Hop 5: Investment and growth metrics
4. Mid-Research Replanning:
- Low confidence on technical details (0.55)
- Switch to Playwright for interactive demos
- Add GitHub repository analysis
5. Quality Gate Check:
- Technical coverage: Improved to 0.78 ✓
- Pricing data: Complete 0.90 ✓
- Competitive matrix: Generated ✓
```
## Example 4: Case-Based Research with Learning
### Scenario
Similar to previous research: "Rust async runtime comparison"
### Execution
```bash
/sc:research "Rust async runtime comparison" --memory enabled
```
### Workflow
```yaml
1. Case Retrieval:
Found Similar Case:
- "Go concurrency patterns" research
- Successful pattern: Technical benchmarks + code examples + community feedback
2. Adapted Strategy:
- Use similar structure for Rust
- Focus on: Tokio, async-std, smol
- Include benchmarks and examples
3. Execution with Known Patterns:
- Skip broad searches
- Direct to technical sources
- Use proven extraction methods
4. New Learning Captured:
- Rust community prefers different metrics than Go
- Crates.io provides useful statistics
- Discord communities have valuable discussions
5. Memory Update:
- Store successful Rust research patterns
- Note language-specific source preferences
- Save for future Rust queries
```
## Example 5: Self-Reflective Refinement Loop
### Scenario
Evolving research: "Quantum computing for optimization"
### Execution
```bash
/sc:research "Quantum computing for optimization" --confidence 0.8 --depth exhaustive
```
### Workflow
```yaml
1. Initial Research Phase:
- Academic papers collected
- Basic concepts understood
- Confidence: 0.65 (below threshold)
2. Self-Reflection Analysis:
Gaps Identified:
- Practical implementations missing
- No industry use cases
- Mathematical details unclear
3. Replanning Decision:
- Add industry reports
- Include video tutorials for math
- Search for code implementations
4. Enhanced Research:
- Hop 1→2: Papers → Authors → Implementations
- Hop 3→4: Companies → Case studies
- Hop 5: Tutorial videos for complex math
5. Quality Achievement:
- Confidence raised to 0.82 ✓
- Comprehensive coverage achieved
- Multiple perspectives included
```
## Example 6: Technical Documentation Research with Playwright
### Scenario
Research the latest Next.js 14 App Router features
### Execution
```bash
/sc:research "Next.js 14 App Router complete guide" --depth deep --scrape selective --screenshots
```
### Workflow
```yaml
1. Tavily Search:
- Find official docs, tutorials, blog posts
- Identify JavaScript-heavy documentation sites
2. URL Analysis:
- Next.js docs → JavaScript rendering required
- Blog posts → Static content, Tavily sufficient
- Video tutorials → Need transcript extraction
3. Playwright Navigation:
- Navigate to official documentation
- Handle interactive code examples
- Capture screenshots of UI components
4. Dynamic Extraction:
- Extract code samples
- Capture interactive demos
- Document routing patterns
5. Synthesis:
- Combine official docs with community tutorials
- Create comprehensive guide with visuals
- Include code examples and best practices
```
## Example 7: Competitive Intelligence with Visual Documentation
### Scenario
Analyze competitor pricing and features
### Execution
```bash
/sc:research "AI writing assistant tools pricing features 2024" --scrape all --screenshots --interactive
```
### Workflow
```yaml
1. Market Discovery:
- Tavily finds: Jasper, Copy.ai, Writesonic, etc.
- Identify pricing pages and feature lists
2. Complexity Assessment:
- Dynamic pricing calculators detected
- Interactive feature comparisons found
- Login-gated content identified
3. Playwright Extraction:
- Navigate to each pricing page
- Interact with pricing sliders
- Capture screenshots of pricing tiers
4. Feature Analysis:
- Extract feature matrices
- Compare capabilities
- Document limitations
5. Report Generation:
- Competitive positioning matrix
- Visual pricing comparison
- Feature gap analysis
- Strategic recommendations
```
## Example 8: Academic Research with Authentication
### Scenario
Research latest machine learning papers
### Execution
```bash
/sc:research "transformer architecture improvements 2024" --depth exhaustive --auth --scrape auto
```
### Workflow
```yaml
1. Academic Search:
- Tavily finds papers on arXiv, IEEE, ACM
- Identify open vs. gated content
2. Access Strategy:
- arXiv: Direct access, no auth needed
- IEEE: Institutional access required
- ACM: Mixed access levels
3. Extraction Approach:
- Public papers: Tavily extraction
- Gated content: Playwright with auth
- PDFs: Download and process
4. Citation Network:
- Follow reference chains
- Identify key contributors
- Map research lineage
5. Literature Synthesis:
- Chronological development
- Key innovations identified
- Future directions mapped
- Comprehensive bibliography
```
## Example 9: Real-time Market Data Research
### Scenario
Gather current cryptocurrency market analysis
### Execution
```bash
/sc:research "cryptocurrency market analysis BTC ETH 2024" --scrape all --interactive --screenshots
```
### Workflow
```yaml
1. Market Discovery:
- Find: CoinMarketCap, CoinGecko, TradingView
- Identify real-time data sources
2. Dynamic Content Handling:
- Playwright loads live charts
- Capture price movements
- Extract volume data
3. Interactive Analysis:
- Interact with chart timeframes
- Toggle technical indicators
- Capture different views
4. Data Synthesis:
- Current market conditions
- Technical analysis
- Sentiment indicators
- Visual documentation
5. Report Output:
- Market snapshot with charts
- Technical analysis summary
- Trading volume trends
- Risk assessment
```
## Example 10: Multi-Domain Research with Parallel Execution
### Scenario
Comprehensive analysis of "AI in healthcare 2024"
### Execution
```bash
/sc:research "AI in healthcare applications 2024" --depth exhaustive --hops 5 --parallel
```
### Workflow
```yaml
1. Domain Decomposition:
Parallel Searches:
- Medical AI applications
- Regulatory landscape
- Market analysis
- Technical implementations
- Ethical considerations
2. Multi-Hop Exploration:
Each Domain:
- Hop 1: Broad landscape
- Hop 2: Key players
- Hop 3: Case studies
- Hop 4: Challenges
- Hop 5: Future trends
3. Cross-Domain Synthesis:
- Medical ↔ Technical connections
- Regulatory ↔ Market impacts
- Ethical ↔ Implementation constraints
4. Quality Assessment:
- Coverage: All domains addressed
- Depth: Sufficient detail per domain
- Integration: Cross-domain insights
- Confidence: 0.87 achieved
5. Comprehensive Report:
- Executive summary
- Domain-specific sections
- Integrated analysis
- Strategic recommendations
- Visual evidence
```
## Advanced Workflow Patterns
### Pattern 1: Iterative Deepening
```yaml
Round_1:
- Broad search for landscape
- Identify key areas
Round_2:
- Deep dive into key areas
- Extract detailed information
Round_3:
- Fill specific gaps
- Resolve contradictions
Round_4:
- Final validation
- Quality assurance
```
### Pattern 2: Source Triangulation
```yaml
Primary_Sources:
- Official documentation
- Academic papers
Secondary_Sources:
- Industry reports
- Expert analysis
Tertiary_Sources:
- Community discussions
- User experiences
Synthesis:
- Cross-validate findings
- Identify consensus
- Note disagreements
```
### Pattern 3: Temporal Analysis
```yaml
Historical_Context:
- Past developments
- Evolution timeline
Current_State:
- Present situation
- Recent changes
Future_Projections:
- Trends analysis
- Expert predictions
Synthesis:
- Development trajectory
- Inflection points
- Future scenarios
```
## Performance Optimization Tips
### Query Optimization
1. Start with specific terms
2. Use domain filters early
3. Batch similar searches
4. Cache intermediate results
5. Reuse successful patterns
### Extraction Efficiency
1. Assess complexity first
2. Use appropriate tool per source
3. Parallelize when possible
4. Set reasonable timeouts
5. Handle errors gracefully
### Synthesis Strategy
1. Organize findings early
2. Identify patterns quickly
3. Resolve conflicts systematically
4. Build narrative progressively
5. Maintain evidence chains
## Quality Validation Checklist
### Planning Phase
- [ ] Clear objectives defined
- [ ] Appropriate strategy selected
- [ ] Resources estimated correctly
- [ ] Success criteria established
### Execution Phase
- [ ] All planned searches completed
- [ ] Extraction methods appropriate
- [ ] Multi-hop chains logical
- [ ] Confidence scores calculated
### Synthesis Phase
- [ ] All findings integrated
- [ ] Contradictions resolved
- [ ] Evidence chains complete
- [ ] Narrative coherent
### Delivery Phase
- [ ] Format appropriate for audience
- [ ] Citations complete and accurate
- [ ] Visual evidence included
- [ ] Confidence levels transparent