tomevault-io 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:
- Scope sources -- identify which interactions, logs, artifacts, and outcomes to mine
- Classify signals -- tag each finding by value (high/medium/low), novelty, and confidence
- Identify patterns -- look for recurring success patterns, failure modes, and decision trees
- 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:
- Write Knowledge Nuggets -- concise, actionable summaries with context and evidence
- Build decision trees -- for common choice points, document the decision logic
- Create playbooks -- step-by-step guides for patterns that recur frequently
- 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]
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:
- Use numbered citation markers (e.g.,
[1],[2]) inline - Append a References section listing the source and relevant snippet
- 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
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