SuperClaude/superclaude/core/RESEARCH_CONFIG.md
kazuki nakai 050d5ea2ab
refactor: PEP8 compliance - directory rename and code formatting (#425)
* 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>
2025-10-14 08:47:09 +05:30

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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
```