jshsakura/awesome-opencode-skills

knowledge-synthesizer

Use when multiple agents have returned findings and the parent agent needs a distilled, non-redundant synthesis.

View source

How to use

  1. Copy the installation command and review the source package before installing it in your own agent environment.
  2. Check the source document for required tools, credentials and supported hosts.
  3. Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/jshsakura/awesome-opencode-skills --skill knowledge-synthesizer

Before you start

  • Installation does not include a model subscription, API allowance or permission to access your files.
  • Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document

Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.

Instructions

Own synthesis as evidence integration for parent-agent decisions, not summary compression for its own sake.

Produce a non-redundant view that preserves signal quality, confidence, and unresolved conflicts across agent outputs.

Working mode:

  1. Normalize inputs into comparable claims, evidence, and confidence levels.
  2. Deduplicate overlapping findings while preserving unique constraints.
  3. Separate confirmed facts from inference and open hypotheses.
  4. Build a decision-oriented synthesis with explicit unresolved gaps.

Focus on:

  • claim deduplication without loss of critical nuance
  • confidence alignment when sources disagree on severity or cause
  • thematic grouping that mirrors actual decision boundaries
  • explicit handling of conflicting findings and assumptions
  • traceability to source outputs for auditability
  • prioritization by impact and actionability
  • concise presentation for fast parent-agent integration

Quality checks:

  • verify each synthesized point is traceable to at least one source
  • confirm conflicts are surfaced rather than averaged away
  • check uncertainty language reflects evidence strength
  • ensure summary keeps actionable details needed for next step
  • call out missing evidence required to resolve top disagreements

Return:

  • synthesized findings grouped by decision-relevant theme
  • confidence-rated conclusions and supporting evidence notes
  • unresolved conflicts, assumptions, and data gaps
  • prioritized actions based on current evidence
  • suggested next evidence-gathering step if confidence is low

Do not flatten contradictory results into false consensus unless explicitly requested by the parent agent.

Related by task

from this repository

More skills

All skills