kata-discovery-synthesis

Synthesize heterogeneous research sources into structured discovery insight markdown files.

Updated Sep 3, 2025
One-click install
npx skills add https://github.com/guardiatechnology/design-system --skill kata-discovery-synthesis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kata-discovery-synthesis
Source: https://github.com/guardiatechnology/design-system/tree/main/.claude/skills/kata-discovery-synthesis
Command: npx skills add https://github.com/guardiatechnology/design-system --skill kata-discovery-synthesis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps teams replace manual reading and note-taking across heterogeneous sources with consistent, structured “discovery insights” that are ready for review.

Core Features & Use Cases

  • Guided source ingestion: Reads heterogeneous inputs (e.g., Notion, Figma, GitHub, or local transcripts) and captures evidence.
  • Insight candidate extraction: Breaks observations into indivisible, traceable units and avoids mixing multiple pains into one insight.
  • Canonical insight generation: Produces an insight markdown file with required front-matter and a standardized body (Observation, Source, Initial implication, Open questions) suitable for docs/discovery/{topic}/insights/.

Quick Start

Ask the AI to synthesize discovery insights for topic scheduled-payments-research from the provided source_refs, creating the next available numbered insight files in docs/discovery/scheduled-payments-research/insights/.

Frequently Asked Questions about kata-discovery-synthesis

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

FAQPage Schema
How do I synthesize product discovery insights from heterogeneous research sources?▼

Product discovery insight synthesis works by reading heterogeneous inputs and transforming each observation into a single traceable insight markdown file under docs/discovery/{topic}/insights/. It enforces traceability via source_refs and uses a canonical front-matter schema to separate Observation, Source, Initial implication, and Open questions without proposing solutions.

What is the best way to structure messy research notes into traceable insights?▼

The best way to structure messy research notes is to break observations into indivisible units, avoiding mixing multiple pains into one insight. This Skill generates a standardized markdown body with required front-matter, capturing a single observation with source_refs for full traceability across heterogeneous inputs.

Can I ingest source references from tools like Notion, Figma, or GitHub for insight generation?▼

Yes, guided source ingestion supports reading heterogeneous inputs from Notion, Figma, GitHub, or local transcripts. It captures evidence from these source_refs to generate structured discovery insights ready for team review.

How do I generate markdown files with canonical front-matter for product discovery?▼

To generate markdown files with canonical front-matter, ask the Skill to synthesize insights for a specific topic from provided source_refs. It automatically creates the next available numbered insight files in the docs/discovery/{topic}/insights/ directory using the required schema.

Does discovery insight synthesis propose solutions for the open questions it identifies?▼

No, discovery insight synthesis does not propose solutions. It strictly separates Observation, Source, Initial implication, and Open questions to ensure the output remains an objective, traceable insight ready for manual review.