knowledge-synthesis

name	knowledge-synthesis
description	Extract insights from multi-agent interactions, identify patterns, and build collective intelligence through cross-agent learning and knowledge management. Use when synthesizing findings, building knowledge bases, or improving system-wide practices.
tags	["knowledge-management","insights","patterns","synthesis"]
triggers	["synthesize knowledge","extract patterns","build knowledge base","cross-agent learning","collective intelligence"]

Knowledge Synthesis

Extract, organize, and distribute insights across multi-agent systems. Turns raw interaction data, logs, and outcomes into actionable knowledge through pattern recognition, best practice codification, and structured retrieval.

When to Use This Skill

• Synthesizing findings from multiple agents or research sessions
• Building or updating a shared knowledge base
• Identifying recurring success or failure patterns in workflows
• Codifying best practices from empirical evidence
• Structuring data for optimal retrieval (RAG optimization)
• Cross-domain knowledge transfer between projects or teams

Quick Reference

Resource	Purpose	Load when
references/synthesis-workflow.md	Pattern recognition, RAG optimization, citation methods, knowledge graphs	Starting a synthesis cycle

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Workflow

Phase 1: Discovery     → Mine interactions, logs, and outcomes for patterns
Phase 2: Codification  → Document best practices, build knowledge graph
Phase 3: Dissemination → Surface insights to relevant agents/teams
Phase 4: Feedback      → Capture adoption feedback, refine the knowledge base

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Phase 1: Knowledge Discovery

Map the landscape before extracting insights:

1. Scope sources -- identify which interactions, logs, artifacts, and outcomes to mine
2. Classify signals -- tag each finding by value (high/medium/low), novelty, and confidence
3. Identify patterns -- look for recurring success patterns, failure modes, and decision trees
4. Document contradictions -- note where sources disagree or outcomes diverge

Discovery Checklist

• All relevant interaction logs identified
• Outcomes mapped to the workflows that produced them
• Recurring patterns tagged with confidence levels
• Contradictions and edge cases flagged

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Phase 2: Codification

Transform raw patterns into structured, retrievable knowledge:

1. Write Knowledge Nuggets -- concise, actionable summaries with context and evidence
2. Build decision trees -- for common choice points, document the decision logic
3. Create playbooks -- step-by-step guides for patterns that recur frequently
4. Update indices -- structure data for retrieval (embeddings, tags, graph links)

Knowledge Nugget Template

## [Pattern Name]

**Context**: When does this pattern apply?
**Evidence**: What interactions/outcomes support it? [cite sources]
**Action**: What should agents do when they encounter this situation?
**Confidence**: High | Medium | Low
**Tags**: [domain], [workflow-type], [agent-role]

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Phase 3: Dissemination

Surface the right insights to the right consumers:

• Route knowledge nuggets to agents whose workflows they affect
• Integrate high-confidence patterns into skill references and playbooks
• Flag low-confidence patterns for further validation
• Update retrieval indices so future queries find new knowledge

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Phase 4: Feedback Loop

Close the loop to keep the knowledge base accurate:

• Monitor adoption -- are agents applying the patterns?
• Capture corrections -- when a pattern proves wrong, update or retract it
• Track retrieval quality -- are the right nuggets surfacing for the right queries?
• Refine confidence scores based on real-world outcomes

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Grounded Responses and Citations

When answering questions based on the knowledge base, provide grounded responses:

1. Use numbered citation markers (e.g., [1], [2]) inline
2. Append a References section listing the source and relevant snippet
3. Cite the specific session, log, or artifact that provided evidence

Example:

The retry logic reduces failures by 40% in high-latency environments [1].

References: [1] "Session 2025-03-12" -- "After adding exponential backoff, error rate dropped from 12% to 7%"

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Anti-Patterns

• Do not synthesize from a single data point -- require multiple corroborating sources
• Do not codify patterns without confidence ratings
• Do not overwrite existing knowledge without citing the new evidence
• Do not skip the feedback loop -- unvalidated knowledge degrades over time