skills.rest
Alamator
AlamatorCOMMUNITY
Explore all skills by this author →
GitHub

talk-stage3-concepts

Extract and organize talk concepts into a ranked, categorized catalogue.

Updated Apr 12, 2026
Search Tags:#pipeline#presentation#categorization#concepts#catalogue#stage-3#talk
One-click install
npx skills add https://github.com/Alamator/code-ultimate --skill talk-stage3-concepts-alamator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: talk-stage3-concepts
Source: https://github.com/Alamator/code-ultimate/tree/main/examples/skills/talk-pipeline/stage-3-concepts
Command: npx skills add https://github.com/Alamator/code-ultimate --skill talk-stage3-concepts-alamator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates the extraction and organization of identifiable concepts from talk summaries and timelines into a numbered, categorized catalogue with talk-potential scores.

Core Features & Use Cases

  • Reads the summary file (talks/{YYYY}-{slug}-summary.md) and optional timeline (talks/{YYYY}-{slug}-timeline.md), forming a comprehensive concept set.
  • Categorizes concepts into domain areas and assigns HIGH/MEDIUM/LOW scores for talk potential.
  • Optional repo enrichment using repo_path to enrich AI-config concepts.

Quick Start

Load a talks summary and optional timeline, then run stage-3 concept extraction to generate the concepts catalogue.

Frequently Asked Questions about talk-stage3-concepts

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

FAQPage Schema
How do I extract and categorize concepts from talk summaries?▼

To extract concepts from talk summaries, you can use a stage-3 concept extraction process that reads summary files and optional timelines to generate a structured, categorized concept catalogue with domain assignments and talk-potential scores.

What is a concept catalogue and how does it help with talk preparation?▼

A concept catalogue is a structured inventory of identifiable concepts extracted from talk summaries, categorized into domain areas and scored by talk potential, providing a ranked overview to prioritize topics during talk preparation.

Can I enrich extracted talk concepts using my own repository?▼

Yes, you can enrich extracted talk concepts by providing a repository path during the concept extraction stage, allowing the process to pull additional context from your codebase to enrich AI-config concepts.

Does concept extraction require a timeline file to score talk potential?▼

No, concept extraction does not require a timeline file to score talk potential. The process reads the main summary file to build the concept inventory, while an optional timeline file can be analyzed to form a more comprehensive concept set.

What is the best way to rank topics for a presentation from a summary file?▼

The best way to rank topics for a presentation from a summary file is to use an automated concept extraction process that assigns HIGH, MEDIUM, or LOW talk-potential scores to each identified concept within a structured markdown table.

Related Skills

Explore More Content & Communication Skills→
aeonbridgeaeonbridge

langextract

Extract structured data from unstructured text with source grounding.

Official
Advanced
alexica00alexica00

talk-stage3-concepts

Extract and categorize talk concepts into a numbered catalogue with potential scores.

Community
Advanced
AIMFllysAIMFllys
3

project-context

Map the StudySolo repository structure, tech stack, and workflow node taxonomy.

Community
Intermediate
ArogyaReddyArogyaReddy
1

talk-stage3-concepts

Scan talk summaries and timelines to produce a scored Markdown concepts catalogue.

Community
Advanced