mirror of
https://github.com/SuperClaude-Org/SuperClaude_Framework.git
synced 2025-12-29 16:16:08 +00:00
* fix(orchestration): add WebFetch auto-trigger for infrastructure configuration Problem: Infrastructure configuration changes (e.g., Traefik port settings) were being made based on assumptions without consulting official documentation, violating the 'Evidence > assumptions' principle in PRINCIPLES.md. Solution: - Added Infrastructure Configuration Validation section to MODE_Orchestration.md - Auto-triggers WebFetch for infrastructure tools (Traefik, nginx, Docker, etc.) - Enforces MODE_DeepResearch activation for investigation - BLOCKS assumption-based configuration changes Testing: Verified WebFetch successfully retrieves Traefik official docs (port 80 default) This prevents production outages from infrastructure misconfiguration by ensuring all technical recommendations are backed by official documentation. * feat: Add PM Agent (Project Manager Agent) for seamless orchestration Introduces PM Agent as the default orchestration layer that coordinates all sub-agents and manages workflows automatically. Key Features: - Default orchestration: All user interactions handled by PM Agent - Auto-delegation: Intelligent sub-agent selection based on task analysis - Docker Gateway integration: Zero-token baseline with dynamic MCP loading - Self-improvement loop: Automatic documentation of patterns and mistakes - Optional override: Users can specify sub-agents explicitly if desired Architecture: - Agent spec: SuperClaude/Agents/pm-agent.md - Command: SuperClaude/Commands/pm.md - Updated docs: README.md (15→16 agents), agents.md (new Orchestration category) User Experience: - Default: PM Agent handles everything (seamless, no manual routing) - Optional: Explicit --agent flag for direct sub-agent access - Both modes available simultaneously (no user downside) Implementation Status: - ✅ Specification complete - ✅ Documentation complete - ⏳ Prototype implementation needed - ⏳ Docker Gateway integration needed - ⏳ Testing and validation needed Refs: kazukinakai/docker-mcp-gateway (IRIS MCP Gateway integration) * feat: Add Agent Orchestration rules for PM Agent default activation Implements PM Agent as the default orchestration layer in RULES.md. Key Changes: - New 'Agent Orchestration' section (CRITICAL priority) - PM Agent receives ALL user requests by default - Manual override with @agent-[name] bypasses PM Agent - Agent Selection Priority clearly defined: 1. Manual override → Direct routing 2. Default → PM Agent → Auto-delegation 3. Delegation based on keywords, file types, complexity, context User Experience: - Default: PM Agent handles everything (seamless) - Override: @agent-[name] for direct specialist access - Transparent: PM Agent reports delegation decisions This establishes PM Agent as the orchestration layer while respecting existing auto-activation patterns and manual overrides. Next Steps: - Local testing in agiletec project - Iteration based on actual behavior - Documentation updates as needed * refactor(pm-agent): redesign as self-improvement meta-layer Problem Resolution: PM Agent's initial design competed with existing auto-activation for task routing, creating confusion about orchestration responsibilities and adding unnecessary complexity. Design Change: Redefined PM Agent as a meta-layer agent that operates AFTER specialist agents complete tasks, focusing on: - Post-implementation documentation and pattern recording - Immediate mistake analysis with prevention checklists - Monthly documentation maintenance and noise reduction - Pattern extraction and knowledge synthesis Two-Layer Orchestration System: 1. Task Execution Layer: Existing auto-activation handles task routing (unchanged) 2. Self-Improvement Layer: PM Agent meta-layer handles documentation (new) Files Modified: - SuperClaude/Agents/pm-agent.md: Complete rewrite with meta-layer design - Category: orchestration → meta - Triggers: All user interactions → Post-implementation, mistakes, monthly - Behavioral Mindset: Continuous learning system - Self-Improvement Workflow: BEFORE/DURING/AFTER/MISTAKE RECOVERY/MAINTENANCE - SuperClaude/Core/RULES.md: Agent Orchestration section updated - Split into Task Execution Layer + Self-Improvement Layer - Added orchestration flow diagram - Clarified PM Agent activates AFTER task completion - README.md: Updated PM Agent description - "orchestrates all interactions" → "ensures continuous learning" - Docs/User-Guide/agents.md: PM Agent section rewritten - Section: Orchestration Agent → Meta-Layer Agent - Expertise: Project orchestration → Self-improvement workflow executor - Examples: Task coordination → Post-implementation documentation - PR_DOCUMENTATION.md: Comprehensive PR documentation added - Summary, motivation, changes, testing, breaking changes - Two-layer orchestration system diagram - Verification checklist Integration Validated: Tested with agiletec project's self-improvement-workflow.md: ✅ PM Agent aligns with existing BEFORE/DURING/AFTER/MISTAKE RECOVERY phases ✅ Complements (not competes with) existing workflow ✅ agiletec workflow defines WHAT, PM Agent defines WHO executes it Breaking Changes: None - Existing auto-activation continues unchanged - Specialist agents unaffected - User workflows remain the same - New capability: Automatic documentation and knowledge maintenance Value Proposition: Transforms SuperClaude into a continuously learning system that accumulates knowledge, prevents recurring mistakes, and maintains fresh documentation without manual intervention. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * docs: add Claude Code conversation history management research Research covering .jsonl file structure, performance impact, and retention policies. Content: - Claude Code .jsonl file format and message types - Performance issues from GitHub (memory leaks, conversation compaction) - Retention policies (consumer vs enterprise) - Rotation recommendations based on actual data - File history snapshot tracking mechanics Source: Moved from agiletec project (research applicable to all Claude Code projects) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: add Development documentation structure Phase 1: Documentation Structure complete - Add Docs/Development/ directory for development documentation - Add ARCHITECTURE.md - System architecture with PM Agent meta-layer - Add ROADMAP.md - 5-phase development plan with checkboxes - Add TASKS.md - Daily task tracking with progress indicators - Add PROJECT_STATUS.md - Current status dashboard and metrics - Add pm-agent-integration.md - Implementation guide for PM Agent mode This establishes comprehensive documentation foundation for: - System architecture understanding - Development planning and tracking - Implementation guidance - Progress visibility Related: #pm-agent-mode #documentation #phase-1 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: PM Agent session lifecycle and PDCA implementation Phase 2: PM Agent Mode Integration (Design Phase) Commands/pm.md updates: - Add "Always-Active Foundation Layer" concept - Add Session Lifecycle (Session Start/During Work/Session End) - Add PDCA Cycle (Plan/Do/Check/Act) automation - Add Serena MCP Memory Integration (list/read/write_memory) - Document auto-activation triggers Agents/pm-agent.md updates: - Add Session Start Protocol (MANDATORY auto-activation) - Add During Work PDCA Cycle with example workflows - Add Session End Protocol with state preservation - Add PDCA Self-Evaluation Pattern - Add Documentation Strategy (temp → patterns/mistakes) - Add Memory Operations Reference Key Features: - Session start auto-activation for context restoration - 30-minute checkpoint saves during work - Self-evaluation with think_about_* operations - Systematic documentation lifecycle - Knowledge evolution to CLAUDE.md Implementation Status: - ✅ Design complete (Commands/pm.md, Agents/pm-agent.md) - ⏳ Implementation pending (Core components) - ⏳ Serena MCP integration pending Salvaged from mistaken development in ~/.claude directory Related: #pm-agent-mode #session-lifecycle #pdca-cycle #phase-2 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix: disable Serena MCP auto-browser launch Disable web dashboard and GUI log window auto-launch in Serena MCP server to prevent intrusive browser popups on startup. Users can still manually access the dashboard at http://localhost:24282/dashboard/ if needed. Changes: - Add CLI flags to Serena run command: - --enable-web-dashboard false - --enable-gui-log-window false - Ensures Git-tracked configuration (no reliance on ~/.serena/serena_config.yml) - Aligns with AIRIS MCP Gateway integration approach 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * refactor: rename directories to lowercase for PEP8 compliance - Rename superclaude/Agents -> superclaude/agents - Rename superclaude/Commands -> superclaude/commands - Rename superclaude/Core -> superclaude/core - Rename superclaude/Examples -> superclaude/examples - Rename superclaude/MCP -> superclaude/mcp - Rename superclaude/Modes -> superclaude/modes This change follows Python PEP8 naming conventions for package directories. * style: fix PEP8 violations and update package name to lowercase Changes: - Format all Python files with black (43 files reformatted) - Update package name from 'SuperClaude' to 'superclaude' in pyproject.toml - Fix import statements to use lowercase package name - Add missing imports (timedelta, __version__) - Remove old SuperClaude.egg-info directory PEP8 violations reduced from 2672 to 701 (mostly E501 line length due to black's 88 char vs flake8's 79 char limit). * docs: add PM Agent development documentation Add comprehensive PM Agent development documentation: - PM Agent ideal workflow (7-phase autonomous cycle) - Project structure understanding (Git vs installed environment) - Installation flow understanding (CommandsComponent behavior) - Task management system (current-tasks.md) Purpose: Eliminate repeated explanations and enable autonomous PDCA cycles 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat(pm-agent): add self-correcting execution and warning investigation culture ## Changes ### superclaude/commands/pm.md - Add "Self-Correcting Execution" section with root cause analysis protocol - Add "Warning/Error Investigation Culture" section enforcing zero-tolerance for dismissal - Define error detection protocol: STOP → Investigate → Hypothesis → Different Solution → Execute - Document anti-patterns (retry without understanding) and correct patterns (research-first) ### docs/Development/hypothesis-pm-autonomous-enhancement-2025-10-14.md - Add PDCA workflow hypothesis document for PM Agent autonomous enhancement ## Rationale PM Agent must never retry failed operations without understanding root causes. All warnings and errors require investigation via context7/WebFetch/documentation to ensure production-quality code and prevent technical debt accumulation. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat(installer): add airis-mcp-gateway MCP server option ## Changes - Add airis-mcp-gateway to MCP server options in installer - Configuration: GitHub-based installation via uvx - Repository: https://github.com/oraios/airis-mcp-gateway - Purpose: Dynamic MCP Gateway for zero-token baseline and on-demand tool loading ## Implementation Added to setup/components/mcp.py self.mcp_servers dictionary with: - install_method: github - install_command: uvx test installation - run_command: uvx runtime execution - required: False (optional server) 🤖 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
|
|
- contradiction_rate
|
|
|
|
efficiency:
|
|
- cache_hit_rate
|
|
- parallel_execution_rate
|
|
- memory_usage
|
|
- api_cost
|
|
|
|
learning:
|
|
- pattern_reuse_rate
|
|
- strategy_success_rate
|
|
- improvement_trajectory
|
|
``` |