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concept-synthesis

Deduplicate concept stubs and synthesize tiered intellectual maps from note repositories.

Updated Jun 10, 2026
Search Tags:#note-taking#synthesis#deduplication#knowledge-management#intellectual-mapping#brain-runtime
One-click install
npx skills add https://github.com/starlink-awaken/omostation-gbrain --skill concept-synthesis-starlink-awaken
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: concept-synthesis
Source: https://github.com/starlink-awaken/omostation-gbrain/tree/main/skills/concept-synthesis
Command: npx skills add https://github.com/starlink-awaken/omostation-gbrain --skill concept-synthesis-starlink-awaken

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the problem of information fragmentation by deduplicating thousands of raw concept stubs and synthesizing them into a structured, tiered intellectual map.

Core Features & Use Cases

  • Automated Deduplication: Uses Jaccard, substring, and semantic analysis to merge duplicate notes and consolidate timelines.
  • Tiered Intellectual Mapping: Automatically classifies ideas into T1 (Canon) through T4 (Riff) based on frequency, timespan, and engagement.
  • Synthesis & Clustering: Generates rich evolution narratives for core concepts and organizes them into thematic domains.

Quick Start

Trigger the concept synthesis process to deduplicate your notes and build your intellectual map.

Frequently Asked Questions about concept-synthesis

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

FAQPage Schema
How do I deduplicate and organize a large repository of raw notes?▼

To deduplicate raw notes, this Skill uses Jaccard, substring, and semantic analysis to merge duplicate stubs and consolidate timelines. It transforms fragmented notes into a structured, tiered intellectual map.

What is tiered intellectual mapping for knowledge management?▼

Tiered intellectual mapping classifies ideas into T1 (Canon) through T4 (Riff) based on frequency, timespan, and engagement. It organizes raw concept stubs into structured thematic domains.

Can I use semantic analysis to trace the evolution of concepts across time?▼

Yes, semantic analysis traces idea evolution across time to generate rich evolution narratives for core concepts. It combines LLM-based analysis with deterministic heuristic scoring to maintain fidelity.

What's the best way to synthesize thousands of concept stubs into thematic clusters?▼

Synthesizing concept stubs requires identifying canonical frameworks and clustering them into thematic domains. The process uses LLM-based semantic analysis to generate high-fidelity intellectual fingerprints.

Does concept synthesis work on large-scale note repositories?▼

Concept synthesis operates on large-scale note repositories to deduplicate stubs and build intellectual maps. It requires LLM-based semantic analysis and deterministic heuristic scoring to process high volumes.

Why are my deduplicated notes still lacking a structured intellectual map?▼

Deduplicated notes lack structure without automated tiered classification and thematic clustering. Applying heuristic scoring to trace idea evolution generates the needed canonical frameworks and narratives.

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