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Talk Stage3 Concepts

CommunityPopular
FlorianBruniaux
talk-stage3-concepts

Builds a numbered, categorized concept catalogue from the talk summary and timeline, scoring each concept HIGH / MEDIUM / LOW for talk potential with optional repo enrichment. Use when you need a structured inventory of concepts before choosing a talk angle, or when assessing which ideas have the strongest presentation potential.

Overview

PublisherFlorianBruniaux
Repositoryclaude-code-ultimate-guide
Skill nametalk-stage3-concepts
Stars
6.1K
Forks
807
Bundled files
Instructions only
LicenseCC-BY-SA-4.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by FlorianBruniaux on GitHub. Read the source before you install it.

Installation

Install the Talk Stage3 Concepts AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/FlorianBruniaux/claude-code-ultimate-guide/tree/main/examples/skills/talk-pipeline/stage-3-concepts
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/FlorianBruniaux/claude-code-ultimate-guide.git /tmp/claude-code-ultimate-guide
mkdir -p .claude/skills
cp -r /tmp/claude-code-ultimate-guide/examples/skills/talk-pipeline/stage-3-concepts .claude/skills/talk-stage3-concepts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Talk Stage3 Concepts in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Talk Stage3 Concepts on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Talk Stage3 Concepts is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Talk stage 3: Concepts

Builds an exhaustive catalogue of all identifiable concepts in the source material. Each concept is numbered, categorized, and scored for its talk potential.

When to use this skill

  • After Stage 1 (and Stage 2 if REX mode)
  • Before Stage 4 (Position needs the concept catalogue)
  • When you want a structured inventory of what's available before choosing an angle

What this skill does

  1. Reads the summary: loads {slug}-summary.md
  2. Reads the timeline (if available): enriches scoring with verified dates
  3. Extracts concepts: full scan of the source material
  4. Categorizes: assigns each concept to a domain category
  5. Scores: HIGH / MEDIUM / LOW for talk potential
  6. Optional repo enrichment: if repo_path is provided, analyzes AI config concepts
  7. Writes output files

Input

  • talks/{YYYY}-{slug}-summary.md (required)
  • talks/{YYYY}-{slug}-timeline.md (optional, enriches REX concepts)
  • repo_path (optional, for config/infrastructure concept extraction)

Output

  • talks/{YYYY}-{slug}-concepts.md (main catalogue)
  • talks/{YYYY}-{slug}-concepts-enriched.md (if repo_path provided)

Scoring criteria

HIGH: Strong potential

  • Demonstrable live or with a screenshot
  • Counter-intuitive or surprising (triggers a reaction)
  • Associated with verifiable numbers
  • Concrete and actionable (explainable in 30 seconds)
  • Differentiator vs other talks on the same topic

MEDIUM: Moderate potential

  • Useful but expected (not surprising)
  • Missing concrete proof or numbers
  • Too specific to one particular context
  • Needs too much explanation for a 30-min talk

LOW: Weak potential

  • Too abstract or philosophical without concrete grounding
  • Already heavily covered by other speakers
  • Requires specific technical background
  • Hard to illustrate in a slide

Scoring discipline: Max 30% HIGH. If everything is HIGH, nothing is.

Standard categories

CategoryDescription
ArchitectureTechnical decisions, stack, structural patterns
ToolingTools, workflows, automations
PhilosophyPrinciples, mindsets, approaches
WorkflowWork processes, habits
Knowledge TransferOnboarding, team, knowledge sharing
ProblemsObstacles encountered, trade-offs
Open SourceContributions, sharing, community
AI ConfigAI configuration, profiles, knowledge feeding
AI InfrastructureAgents, skills, hooks, commands
AI QualityReview, tests, anti-patterns
AI SecuritySecurity hooks, guardrails
OptimizationPerformance, cost/token reduction

Adapt or create categories if the talk has domain-specific areas.

Output format

concepts.md

markdown
# Key Concepts: {provisional title}

**Date**: {date}
**Source**: {source path} x Summary x Timeline (if available)

---

## Concept table

| # | Concept | Category | Short description | Talk potential |
|---|---------|----------|------------------|----------------|
| 1 | **{Concept name}** | {Category} | {1-2 concrete sentences} | HIGH / MEDIUM / LOW |
...

---

## Category breakdown

| Category | Count | HIGH concepts | Examples |
|----------|-------|---------------|---------|
| {category} | {n} | {n} | {examples} |
...
| **TOTAL** | **{N}** | **{N HIGH}** | |

---

## Recommendations for positioning

{3-5 sentences on concept clusters that could form the talk's acts.
Which HIGH concepts reinforce each other? What narrative arc is emerging?}

concepts-enriched.md (if repo available)

Same structure but focused on what the repo analysis reveals:

  • Specialized agents (count, size, roles)
  • Invocable skills (catalogue, domains covered)
  • System hooks (events, logic)
  • Modular config (profiles, modules, pipeline)
  • Project-specific code patterns

For each enriched concept, include:

  • Exact source: file and approximate line
  • Demo-able: yes/no (can it be shown in a slide or live?)

Anti-patterns

  • Creating overly granular concepts (one feature = one concept max)
  • Scoring HIGH by default (be selective)
  • Omitting LOW concepts (they're useful in positioning as "angles to avoid")
  • Duplicating very similar concepts (merge them instead)
  • Analyzing repo code if the repo isn't accessible

Validation checklist

  • Minimum 15 concepts identified (20+ for REX with repo)
  • Each concept has a 1-2 sentence concrete description
  • Scores are calibrated (not all HIGH, not all LOW)
  • Categories cover the summary's themes
  • Positioning recommendations present
  • Files saved to correct paths

Tips

  • The concept catalogue is what Stage 4 (Position) draws from; the richer it is, the better the angle choices
  • LOW concepts are valuable: they define the boundaries of what NOT to put in the talk
  • If two concepts feel very similar, merge them (a smaller, sharper list beats a long diluted one)

Related

Frequently asked questions

What does the Talk Stage3 Concepts AI skill do?

Builds a numbered, categorized concept catalogue from the talk summary and timeline, scoring each concept HIGH / MEDIUM / LOW for talk potential with optional repo enrichment. Use when you need a structured inventory of concepts before choosing a talk angle, or when assessing which ideas have the strongest presentation potential.

Why use Talk Stage3 Concepts on TypingMind?

Because you install it once and use it with any model. Talk Stage3 Concepts is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Talk Stage3 Concepts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FlorianBruniaux/claude-code-ultimate-guide/tree/main/examples/skills/talk-pipeline/stage-3-concepts. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Talk Stage3 Concepts?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Talk Stage3 Concepts?

As many as you like. As long as a model supports skills, you can use Talk Stage3 Concepts with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Talk Stage3 Concepts AI skill free?

Yes. It is published on GitHub by FlorianBruniaux under the CC-BY-SA-4.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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