mirror of
https://github.com/SuperClaude-Org/SuperClaude_Framework.git
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* 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(15KB) → PLANNING.md * TASKS.md (3.7KB) → TASK.md * ROADMAP.md (11KB) → TASK.md * PROJECT_STATUS.md (4.2KB) → outdated * 13 PM Agent research files → archived in KNOWLEDGE.md - docs/PM_AGENT.md - Old implementation status - docs/pm-agent-implementation-status.md - Duplicate -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>
446 lines
9.4 KiB
Markdown
446 lines
9.4 KiB
Markdown
# Deep Research Configuration
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## Default Settings
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```yaml
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research_defaults:
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planning_strategy: unified
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max_hops: 5
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confidence_threshold: 0.7
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memory_enabled: true
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parallelization: true
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parallel_first: true # MANDATORY DEFAULT
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sequential_override_requires_justification: true # NEW
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parallel_execution_rules:
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DEFAULT_MODE: PARALLEL # EMPHASIZED
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mandatory_parallel:
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- "Multiple search queries"
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- "Batch URL extractions"
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- "Independent analyses"
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- "Non-dependent hops"
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- "Result processing"
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- "Information extraction"
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sequential_only_with_justification:
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- reason: "Explicit dependency"
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example: "Hop N requires Hop N-1 results"
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- reason: "Resource constraint"
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example: "API rate limit reached"
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- reason: "User requirement"
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example: "User requests sequential for debugging"
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parallel_optimization:
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batch_sizes:
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searches: 5
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extractions: 3
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analyses: 2
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intelligent_grouping:
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by_domain: true
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by_complexity: true
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by_resource: true
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planning_strategies:
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planning_only:
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clarification: false
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user_confirmation: false
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execution: immediate
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intent_planning:
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clarification: true
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max_questions: 3
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execution: after_clarification
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unified:
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clarification: optional
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plan_presentation: true
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user_feedback: true
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execution: after_confirmation
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hop_configuration:
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max_depth: 5
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timeout_per_hop: 60s
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parallel_hops: true
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loop_detection: true
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genealogy_tracking: true
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confidence_scoring:
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relevance_weight: 0.5
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completeness_weight: 0.5
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minimum_threshold: 0.6
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target_threshold: 0.8
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self_reflection:
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frequency: after_each_hop
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triggers:
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- confidence_below_threshold
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- contradictions_detected
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- time_elapsed_percentage: 80
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- user_intervention
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actions:
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- assess_quality
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- identify_gaps
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- consider_replanning
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- adjust_strategy
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memory_management:
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case_based_reasoning: true
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pattern_learning: true
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session_persistence: true
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cross_session_learning: true
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retention_days: 30
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tool_coordination:
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discovery_primary: tavily
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extraction_smart_routing: true
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reasoning_engine: sequential
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memory_backend: serena
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parallel_tool_calls: true
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quality_gates:
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planning_gate:
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required_elements: [objectives, strategy, success_criteria]
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execution_gate:
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min_confidence: 0.6
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synthesis_gate:
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coherence_required: true
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clarity_required: true
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extraction_settings:
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scraping_strategy: selective
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screenshot_capture: contextual
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authentication_handling: ethical
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javascript_rendering: auto_detect
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timeout_per_page: 15s
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```
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## Performance Optimizations
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```yaml
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optimization_strategies:
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caching:
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- Cache Tavily search results: 1 hour
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- Cache Playwright extractions: 24 hours
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- Cache Sequential analysis: 1 hour
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- Reuse case patterns: always
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parallelization:
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- Parallel searches: max 5
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- Parallel extractions: max 3
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- Parallel analysis: max 2
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- Tool call batching: true
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resource_limits:
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- Max time per research: 10 minutes
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- Max search iterations: 10
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- Max hops: 5
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- Max memory per session: 100MB
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```
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## Strategy Selection Rules
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```yaml
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strategy_selection:
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planning_only:
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indicators:
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- Clear, specific query
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- Technical documentation request
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- Well-defined scope
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- No ambiguity detected
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intent_planning:
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indicators:
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- Ambiguous terms present
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- Broad topic area
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- Multiple possible interpretations
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- User expertise unknown
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unified:
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indicators:
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- Complex multi-faceted query
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- User collaboration beneficial
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- Iterative refinement expected
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- High-stakes research
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```
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## Source Credibility Matrix
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```yaml
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source_credibility:
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tier_1_sources:
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score: 0.9-1.0
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types:
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- Academic journals
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- Government publications
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- Official documentation
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- Peer-reviewed papers
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tier_2_sources:
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score: 0.7-0.9
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types:
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- Established media
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- Industry reports
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- Expert blogs
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- Technical forums
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tier_3_sources:
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score: 0.5-0.7
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types:
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- Community resources
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- User documentation
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- Social media (verified)
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- Wikipedia
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tier_4_sources:
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score: 0.3-0.5
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types:
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- User forums
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- Social media (unverified)
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- Personal blogs
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- Comments sections
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```
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## Depth Configurations
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```yaml
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research_depth_profiles:
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quick:
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max_sources: 10
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max_hops: 1
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iterations: 1
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time_limit: 2 minutes
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confidence_target: 0.6
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extraction: tavily_only
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standard:
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max_sources: 20
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max_hops: 3
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iterations: 2
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time_limit: 5 minutes
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confidence_target: 0.7
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extraction: selective
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deep:
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max_sources: 40
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max_hops: 4
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iterations: 3
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time_limit: 8 minutes
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confidence_target: 0.8
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extraction: comprehensive
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exhaustive:
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max_sources: 50+
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max_hops: 5
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iterations: 5
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time_limit: 10 minutes
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confidence_target: 0.9
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extraction: all_sources
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```
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## Multi-Hop Patterns
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```yaml
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hop_patterns:
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entity_expansion:
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description: "Explore entities found in previous hop"
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example: "Paper → Authors → Other works → Collaborators"
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max_branches: 3
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concept_deepening:
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description: "Drill down into concepts"
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example: "Topic → Subtopics → Details → Examples"
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max_depth: 4
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temporal_progression:
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description: "Follow chronological development"
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example: "Current → Recent → Historical → Origins"
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direction: backward
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causal_chain:
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description: "Trace cause and effect"
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example: "Effect → Immediate cause → Root cause → Prevention"
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validation: required
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```
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## Extraction Routing Rules
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```yaml
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extraction_routing:
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use_tavily:
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conditions:
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- Static HTML content
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- Simple article structure
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- No JavaScript requirement
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- Public access
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use_playwright:
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conditions:
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- JavaScript rendering required
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- Dynamic content present
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- Authentication needed
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- Interactive elements
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- Screenshots required
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use_context7:
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conditions:
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- Technical documentation
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- API references
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- Framework guides
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- Library documentation
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use_native:
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conditions:
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- Local file access
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- Simple explanations
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- Code generation
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- General knowledge
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```
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## Case-Based Learning Schema
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```yaml
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case_schema:
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case_id:
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format: "research_[timestamp]_[topic_hash]"
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case_content:
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query: "original research question"
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strategy_used: "planning approach"
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successful_patterns:
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- query_formulations: []
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- extraction_methods: []
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- synthesis_approaches: []
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findings:
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key_discoveries: []
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source_credibility_scores: {}
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confidence_levels: {}
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lessons_learned:
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what_worked: []
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what_failed: []
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optimizations: []
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metrics:
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time_taken: seconds
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sources_processed: count
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hops_executed: count
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confidence_achieved: float
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```
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## Replanning Thresholds
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```yaml
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replanning_triggers:
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confidence_based:
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critical: < 0.4
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low: < 0.6
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acceptable: 0.6-0.7
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good: > 0.7
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time_based:
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warning: 70% of limit
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critical: 90% of limit
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quality_based:
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insufficient_sources: < 3
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contradictions: > 30%
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gaps_identified: > 50%
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user_based:
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explicit_request: immediate
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implicit_dissatisfaction: assess
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```
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## Output Format Templates
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```yaml
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output_formats:
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summary:
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max_length: 500 words
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sections: [key_finding, evidence, sources]
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confidence_display: simple
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report:
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sections: [executive_summary, methodology, findings, synthesis, conclusions]
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citations: inline
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confidence_display: detailed
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visuals: included
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academic:
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sections: [abstract, introduction, methodology, literature_review, findings, discussion, conclusions]
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citations: academic_format
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confidence_display: statistical
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appendices: true
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```
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## Error Handling
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```yaml
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error_handling:
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tavily_errors:
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api_key_missing: "Check TAVILY_API_KEY environment variable"
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rate_limit: "Wait and retry with exponential backoff"
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no_results: "Expand search terms or try alternatives"
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playwright_errors:
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timeout: "Skip source or increase timeout"
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navigation_failed: "Mark as inaccessible, continue"
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screenshot_failed: "Continue without visual"
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quality_errors:
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low_confidence: "Trigger replanning"
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contradictions: "Seek additional sources"
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insufficient_data: "Expand search scope"
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```
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## Integration Points
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```yaml
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mcp_integration:
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tavily:
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role: primary_search
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fallback: native_websearch
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playwright:
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role: complex_extraction
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fallback: tavily_extraction
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sequential:
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role: reasoning_engine
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fallback: native_reasoning
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context7:
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role: technical_docs
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fallback: tavily_search
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serena:
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role: memory_management
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fallback: session_only
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```
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## Monitoring Metrics
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```yaml
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metrics_tracking:
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performance:
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- search_latency
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- extraction_time
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- synthesis_duration
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- total_research_time
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quality:
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- confidence_scores
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- source_diversity
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- coverage_completeness
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- contradiction_rate
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efficiency:
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- cache_hit_rate
|
|
- parallel_execution_rate
|
|
- memory_usage
|
|
- api_cost
|
|
|
|
learning:
|
|
- pattern_reuse_rate
|
|
- strategy_success_rate
|
|
- improvement_trajectory
|
|
``` |