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evidence-synthesis

Synthesizes multiple evidence sources into a confidence-weighted conclusion using GRADE-adapted quality grading and convergence analysis.

7|2|Updated Mar 5, 2026
Search Tags:#literature review#decision support#meta-analysis#confidence assessment#evidence synthesis#GRADE quality#conflicting data
One-click install
npx skills add https://github.com/AndurilCode/craftwork --skill evidence-synthesis-andurilcode
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evidence-synthesis
Source: https://github.com/AndurilCode/craftwork/tree/main/skills/evidence-synthesis
Command: npx skills add https://github.com/AndurilCode/craftwork --skill evidence-synthesis-andurilcode

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you synthesize multiple pieces of evidence into a single, decision-ready conclusion when sources conflict, vary in quality, or come from different methodologies.

Core Features & Use Cases

  • Evidence framing: Defines a precise synthesis question with decision context and scope boundaries to prevent scope creep.
  • Quality-weighted inventory: Collects each evidence source with provenance, methodology, and relevance, then grades quality using a GRADE-adapted approach.
  • Convergence and divergence analysis: Identifies where findings agree across independent sources, diagnoses why results differ, and flags remaining uncertainty.
  • Gap detection and decision-ready output: Highlights what evidence is missing and produces a confidence-weighted conclusion with caveats and revision triggers.

Quick Start

Ask the AI: "Synthesize these findings into a decision-ready conclusion, weighting sources by quality, and explain where they converge, diverge, and remain uncertain."

Frequently Asked Questions about evidence-synthesis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I synthesize conflicting evidence from multiple studies into one decision-ready conclusion?▼

Synthesize conflicting evidence by inventorying sources with provenance, grading quality using a GRADE-adapted approach, diagnosing convergence and divergence, and producing a confidence-weighted conclusion with caveats.

What is the best way to weigh conflicting data from different experimental and observational methodologies?▼

Weigh conflicting data by collecting each source's provenance and methodology, assigning GRADE-adapted quality grades, and analyzing where independent findings converge, diverge, or remain uncertain to guide decision support.

How does a GRADE-adapted quality assessment work for literature reviews with varying evidence quality?▼

GRADE-adapted quality assessment grades each evidence source based on methodology and relevance, enabling quality-weighted synthesis to resolve conflicts and identify remaining uncertainty and evidence gaps.

Can I integrate causal-inference outputs directly into a meta-analytic style evidence synthesis?▼

Integrate causal-inference outputs by collecting them as provenance-inventoried sources, grading their quality, and synthesizing them with observational data to produce a confidence-rated decision-ready conclusion.

When should I use evidence synthesis for decision support instead of a standard literature review?▼

Use evidence synthesis when findings conflict, vary in quality, or span multiple methodologies, requiring convergence and divergence diagnosis, gap detection, and confidence-rated conclusions beyond a standard review.

How do I identify evidence gaps when findings from independent sources diverge?▼

Identify evidence gaps by diagnosing why results differ across independent sources, flagging remaining uncertainty, and highlighting missing evidence to produce confidence-weighted conclusions with revision triggers.

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