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