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knowledge-synthesis

Extract insights from multi-agent interactions, identify patterns, and build collective intelligence through cross-agent learning and knowledge management. Use when synthesizing findings, building kno

by nickcrew(0) 0 installs
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Imported from nickcrew/claude-cortex (skills/knowledge-synthesis/SKILL.md). Install upstream with npx skills add nickcrew/claude-cortex --skill knowledge-synthesis. Copyright stays with the author.

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

## [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

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml
targets:
  - https://api.opensmartroute.ai/api/v1/registry/nickcrew-claude-cortex-knowledge-synthesis/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

nickcrew-claude-cortex-knowledge-synthesis.ocm.jsonjson
{
  "ocm": "1",
  "id": "nickcrew-claude-cortex-knowledge-synthesis",
  "kind": "skill",
  "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.",
  "publisher": "nickcrew",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "knowledge-management",
      "insights",
      "patterns",
      "synthesis",
      "synthesize-knowledge",
      "extract-patterns",
      "knowledge-base",
      "knowledge",
      "knowledge-synthesis"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "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."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/nickcrew/claude-cortex",
      "path": "skills/knowledge-synthesis/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/nickcrew/claude-cortex/blob/HEAD/skills/knowledge-synthesis/SKILL.md",
      "key": "nickcrew/claude-cortex/skills/knowledge-synthesis/SKILL.md"
    }
  },
  "instructions": "# Knowledge Synthesis\n\nExtract, organize, and distribute insights across multi-agent systems. Turns raw\ninteraction data, logs, and outcomes into actionable knowledge through pattern\nrecognition, best practice codification, and structured retrieval.\n\n## When to Use This Skill\n\n- Synthesizing findings from multiple agents or research sessions\n- Building or updating a shared knowledge base\n- Identifying recurring success or failure patterns in workflows\n- Codifying best practices from empirical evidence\n- Structuring data for optimal retrieval (RAG optimization)\n- Cross-domain knowledge transfer",
  "cost": {
    "context_tokens": 1046
  }
}

Fetch it by URL: GET /api/v1/registry/nickcrew-claude-cortex-knowledge-synthesis/manifest?version=1.0.0

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