jshsakura/awesome-opencode-skills

knowledge-synthesizer

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

Quelltext ansehen

Verwendung

  1. Installationsbefehl kopieren und das Quellpaket vor der Installation in der eigenen Agentenumgebung prüfen.
  2. Benötigte Werkzeuge, Zugangsdaten und Hosts im Original prüfen.
  3. Eingabe und gewünschtes Ergebnis beschreiben und die Ausgabe mit den Quellen abgleichen.
npx skills add https://github.com/jshsakura/awesome-opencode-skills --skill knowledge-synthesizer

Vor dem Start

  • Die Installation umfasst weder Modellabo noch API-Kontingent oder Dateizugriffsrechte.
  • Sterne und Installationen zeigen Aktivität, keine unabhängige Qualitäts- oder Sicherheitsprüfung.
Originales Skill-Dokument

Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.

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.

Verwandte Aufgaben

aus demselben Repository

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