knowledge-synthesis

# 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 | --- ## 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 ``` --- ## 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 --- ## 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 ```markdown ## [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] ``` --- ## 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 --- ## 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 --- ## 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%" --- ## 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