xiaohongshu
xiaohongshu
ai-board
Advanced multi-agent reasoning system for complex questions requiring maximum accuracy. Uses adaptive technique selection, adversarial validation, multi-agent debate, and deep analysis. Trigger phrases "ai board", "expert panel", "deep analysis", "maximum accuracy", "thorough reasoning". Use for ...
Full skill instructions
The AI Board is a sophisticated reasoning system that dynamically selects and combines advanced LLM techniques to achieve maximum accuracy on complex questions. It uses multi-agent debate, adversarial validation, domain adaptation, and structured reasoning to deliver expert-level analysis.
When to use: Critical decisions, complex medical cases, theological questions, philosophical analysis, technical problems requiring deep exploration, or any query where accuracy is paramount.
The AI Board follows a systematic 5-phase approach that scales with question complexity:
Classify the question:
Select reasoning techniques based on classification:
Estimate required depth:
Deploy specialized agents based on question domain:
Medical questions:
Theological questions:
Philosophical questions:
Technical questions:
General questions:
Initial analysis by primary agents
Devil's advocate critique:
Rebuttal and refinement:
Synthesis round:
External validation (when applicable):
Self-consistency verification:
Constitutional alignment:
Present results in this format:
## Analysis Summary
[1-2 paragraph executive summary of the conclusion]
## Key Findings
[Numbered list of main insights with confidence levels]
1. **[Finding 1]** - Confidence: [95%/85%/70%/50%/<50%]
- Supporting evidence: [brief rationale]
- Limitations: [what could change this]
2. **[Finding 2]** - Confidence: [level]
- Supporting evidence: [rationale]
- Limitations: [uncertainties]
## Reasoning Process
[Detailed analysis showing the reasoning path, including:
- Key decision points
- Alternative hypotheses considered
- Why certain paths were pursued/rejected
- Critical evidence that shaped conclusions]
## Areas of Disagreement/Uncertainty
[Explicitly call out where agents disagreed or evidence is ambiguous:
- What remains uncertain
- What additional information would help
- Edge cases or scenarios where conclusion might not hold]
## Confidence Assessment
Overall confidence in main conclusion: [X%]
Factors increasing confidence:
- [Factor 1]
- [Factor 2]
Factors decreasing confidence:
- [Factor 1]
- [Factor 2]
## Recommendations
[Actionable next steps, further questions to explore, or implementation guidance]
Use the enhanced clinical reasoning framework:
See references/medical-reasoning.md for detailed clinical protocols.
Use the biblical-theological framework:
See references/theological-reasoning.md for detailed protocols.
Use the analytical philosophy framework:
See references/philosophical-reasoning.md for detailed protocols.
Balance thoroughness with token efficiency:
scripts/multi_agent_orchestrator.py - Coordinates multi-agent analysisscripts/confidence_calculator.py - Computes calibrated confidence scoresscripts/evidence_validator.py - Validates claims against evidencereferences/medical-reasoning.md - Clinical decision support protocolsreferences/theological-reasoning.md - Biblical-theological analysis frameworkreferences/philosophical-reasoning.md - Analytical philosophy methodsreferences/reasoning-techniques.md - Comprehensive guide to all techniquesreferences/domain-adaptation.md - How to adapt reasoning by domainxiaohongshu
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