feat: Add WisdomSynthesis skill for multi-skill orchestration pipelines

[christauff]

⚡ feat: Add WisdomSynthesis skill for multi-skill orchestration pipelines

Summary

Introduces WisdomSynthesis, a multi-skill orchestration system that chains Research, Fabric, FirstPrinciples, Council, and RedTeam in curated pipelines to produce emergent insights impossible from any single skill. Includes 5 pre-built pipelines and custom pipeline support.

🎯 Motivation and Context

Problem:

• PAI skills work in isolation
• Emergent insights require manually chaining multiple skills
• No reusable orchestration patterns
• Users must remember complex skill sequences
• Output from one skill must be manually fed to the next

Solution:
WisdomSynthesis provides pre-built pipelines where each step builds on previous output:

1. ExtractWisdom: Research → Fabric extract_wisdom → FirstPrinciples → Summary
2. ThreatAnalysis: Research → Fabric create_threat_model → Council → RedTeam → Report
3. TopicMastery: Research → Fabric extract_insights → FirstPrinciples → Council → Learning plan
4. ControversialTopic: Research (multiple perspectives) → Council debate → Balanced analysis
5. CustomPipeline: User-defined skill chains

📋 Changes

Added Files

Releases/v2.5/.claude/skills/WisdomSynthesis/
├── SKILL.md                           # Skill documentation and routing
├── Workflows/
│   ├── ExtractWisdom.md              # Research + Fabric + FirstPrinciples pipeline
│   ├── ThreatAnalysis.md             # Security threat analysis pipeline
│   ├── TopicMastery.md               # Deep learning pipeline
│   ├── ControversialTopic.md         # Balanced debate pipeline
│   └── CustomPipeline.md             # User-defined chains
└── Data/
    └── Pipelines.yaml                # Pipeline configurations

💡 Example Usage

ExtractWisdom Pipeline:

User: "wisdom synthesis on quantum computing basics"

Pipeline Execution:
1. Research Agent researches quantum computing fundamentals (3,847 words)
2. Fabric extract_wisdom extracts key insights from research
3. FirstPrinciples analyzes foundational concepts
4. Summary synthesizes findings into learning-optimized format

Output: Comprehensive knowledge synthesis with:
- Research findings
- Key insights (Fabric)
- Fundamental principles
- Integrated summary

ThreatAnalysis Pipeline:

User: "threat synthesis on REST API authentication"

Pipeline Execution:
1. Research Agent researches REST API auth security
2. Fabric create_threat_model structures threat landscape
3. Council debates attack vectors
4. RedTeam stress-tests with 32 adversarial agents
5. Report synthesizes comprehensive threat analysis

Output: Complete threat model with:
- Attack surface analysis
- Threat scenarios
- Mitigation strategies
- Adversarial testing results

✅ Benefits

• Emergent Intelligence: Skill composition produces insights impossible from any single skill
• Reusable Patterns: Pre-built pipelines encode expert orchestration
• PAI-Native Architecture: Markdown workflows + YAML configs + Task delegation
• Demonstrates Vision: Shows what multi-skill orchestration enables
• Extensible: CustomPipeline supports user-defined chains
• Zero Dependencies: Uses only standard PAI skills

🔗 Dependencies

Required Skills:

• Research (web research capability)
• Fabric (pattern-based analysis with 240+ patterns)
• FirstPrinciples (fundamental reasoning)
• Council (multi-agent debate)
• RedTeam (adversarial analysis with 32 agents)

Architecture:
Uses PAI Task delegation for parallel agent orchestration. No modifications to existing skills required.

🧪 How Has This Been Tested?

• Tested ExtractWisdom pipeline on technical topics (quantum computing, machine learning)
• Tested ThreatAnalysis pipeline on security topics (API auth, web app security)
• Tested TopicMastery pipeline on learning topics (distributed systems, cryptography)
• Tested ControversialTopic pipeline on debate topics (remote work, AI safety)
• Tested CustomPipeline with user-defined skill chains
• Verified skill chaining works correctly (output → input flow)
• Used in production for 1 month with 39 skills

📊 Example Output

ExtractWisdom on "Neural Networks":

# Wisdom Synthesis: Neural Networks

## Research Phase (4,235 words)
Neural networks are computational models inspired by biological neurons...
[Research Agent findings]

## Wisdom Extraction (Fabric extract_wisdom)
Key Insights:
- Backpropagation enables gradient-based learning
- Universal approximation theorem proves representational power
- Regularization prevents overfitting in deep networks

## First Principles Analysis
Foundational Concepts:
1. Linear algebra (matrix multiplication as computation)
2. Calculus (gradient descent optimization)
3. Probability (stochastic learning)

## Synthesis
Neural networks combine linear transformations with nonlinear activations
to learn hierarchical representations from data...
[Integrated summary optimized for learning]

📊 Types of Changes

• New feature (non-breaking change which adds functionality)
• Bug fix (non-breaking change which fixes an issue)
• Breaking change (fix or feature that would cause existing functionality to change)
• Documentation update

✅ Checklist

• My code follows the PAI code style
• Uses PAI-native architecture (Markdown workflows + YAML configs)
• I have tested this change thoroughly (all 5 pipelines)
• This change is backward compatible
• Dependencies documented (5 required skills)
• Zero external dependencies

📖 Documentation

Trigger Patterns:

• "wisdom synthesis on [topic]"
• "deep analysis pipeline for [topic]"
• "threat synthesis on [topic]"
• "master [topic]" (TopicMastery)
• "balanced analysis of [topic]" (ControversialTopic)

Creating Custom Pipelines:
Edit Data/Pipelines.yaml:

custom_research_chain:
  steps:
    - skill: Research
      output: research_findings
    - skill: Fabric
      pattern: extract_insights
      input: research_findings
    - skill: FirstPrinciples
      input: insights

🎓 Implementation Notes

Why Markdown Workflows + YAML Configs?

• Markdown: Human-readable documentation of pipeline logic
• YAML: Machine-readable configuration for execution
• Task delegation: PAI-native parallel agent orchestration

This architecture enables clean merges with upstream (no code, just data).

Pipeline Design Philosophy:
Each pipeline is curated for a specific outcome:

• ExtractWisdom: Learning-optimized knowledge synthesis
• ThreatAnalysis: Security-focused adversarial analysis
• TopicMastery: Deep understanding with multi-perspective debate
• ControversialTopic: Balanced analysis of polarizing topics
• CustomPipeline: User-defined experimentation

🚀 Future Possibilities

WisdomSynthesis demonstrates PAI's orchestration vision. Future pipelines could:

• Scientific literature synthesis (Research → Fabric analyze_paper → Council)
• Code analysis (Research → Fabric analyze_code → RedTeam security review)
• Decision analysis (Research → Council → FirstPrinciples → Recommendation)

The skill is extensible - users can add their own pipelines to Pipelines.yaml.

📈 Performance Characteristics

Pipeline	Skills	Time	Token Cost
ExtractWisdom	3	~30-45s	Medium
ThreatAnalysis	4	~45-60s	High
TopicMastery	5	~60-90s	Very High
ControversialTopic	4	~45-60s	High
CustomPipeline	Variable	Variable	Variable

All timing and cost estimates based on production usage with real content analysis tasks.