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Talk Stage1 Extract

CommunityPopular
FlorianBruniaux
talk-stage1-extract

Extracts and structures source material (articles, transcripts, notes) into a talk summary with narrative arc, themes, metrics, and gaps. Auto-detects REX vs Concept type. Use when starting a new talk from any source material or auditing existing material before committing to a talk.

Overview

PublisherFlorianBruniaux
Repositoryclaude-code-ultimate-guide
Skill nametalk-stage1-extract
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 Stage1 Extract 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-1-extract
  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-1-extract .claude/skills/talk-stage1-extract
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Talk Stage1 Extract 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 Stage1 Extract 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 Stage1 Extract 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 1: Extract

Transforms raw material (article, transcript, notes, or a mix) into a structured summary ready for the pipeline's downstream stages. Auto-detects source type.

When to use this skill

  • Starting a new talk from any source material
  • First step of the talk pipeline (always run before other stages)
  • Auditing existing source material before committing to a talk

What this skill does

  1. Collects metadata by asking for slug, event, date, duration, audience, mode if not provided
  2. Reads the source and loads the source file or inline content
  3. Detects source type (REX with real-world proof vs Concept with ideas/thesis) based on content signals
  4. Extracts the narrative arc (chronological for REX, thematic for Concept)
  5. Extracts metrics with every measurable number and its source
  6. Identifies main themes (3-7 themes)
  7. Flags gaps: what's missing for a complete talk
  8. Writes {slug}-summary.md

Input

Required:

  • Source file path or inline content (article .mdx, transcript .md, notes)
  • Metadata: slug, event, date, duration, audience, type (--rex or --concept)

If metadata is missing -> AskUserQuestion before proceeding.

Output

talks/{YYYY}-{slug}-summary.md

Source type detection

REX signalsConcept signals
Specific datesTheses, arguments
Measured metricsGeneral observations
Project/tool namesTrend observations
Commits, releases, PRsAnalogies, metaphors
"I shipped", "We built""I think", "In my opinion"

If hybrid -> note both components in the summary.

Output format

markdown
# Talk Summary: {Provisional Title}

**Slug** : {slug}
**Event** : {event}
**Date** : {date}
**Duration** : {duration} min
**Audience** : {audience description}
**Type detected** : REX | Concept | Hybrid
**Source** : {source file path}

---

## Narrative Arc

{Arc description: 3-5 sentences. Chronological if REX, thematic if Concept.}

## Main Themes

| # | Theme | Short description | Weight |
|---|-------|------------------|--------|
| 1 | {theme} | {description} | High/Medium/Low |
...

## Key Metrics Extracted

{All measurable numbers found in the source}

Format: `{value}`: {context} (Source: {section/page/git})

Examples:
- `1,200 commits` over 7 months (Source: "acceleration" section)
- `-97% traffic` after SSE migration (Source: CHANGELOG v1.1.0)

If none -> "No verifiable metrics found (Concept mode)"

## Narrative Potential

{3-5 sentences on the strengths and possible narrative angles.
What makes this talk potentially strong. What might be missing.}

## Gaps Identified

- [ ] {gap 1}: {how to fill it}
- [ ] {gap 2}: {how to fill it}

If no obvious gaps -> "No major gaps identified."

## Recommendations for next stages

- **Research**: {recommended / not applicable (Concept mode)}: {why}
- **Concepts**: {priority themes to explore}
- **Position**: {angles already visible from the source material}

---

*Generated by talk-stage1-extract: {date}*
*Source: {source path}*

Metric extraction rules

  • Do not round without indicating it
  • Always include the metric's source
  • If two sources contradict, flag both without picking one
  • No invented metrics to fill gaps
  • Use {before} -> {after} format for evolutions

Anti-patterns

  • Vague summary ("This text is about AI...")
  • Omitting metrics, even approximate ones with their source
  • Hiding gaps (naming them is better than pretending they don't exist)
  • Changing the detected type without justification
  • Inventing a narrative arc not present in the source

Validation checklist

  • Source type detected and justified
  • Narrative arc in 3-5 clear sentences
  • All measurable metrics extracted with their source
  • Main themes listed (3-7 max)
  • Gaps explicitly identified
  • File saved to talks/{YYYY}-{slug}-summary.md

Tips

  • Run this before the orchestrator if you want to verify the source material is usable
  • The summary is the foundation: every downstream stage reads it
  • Hybrid sources (part REX, part Concept) are fine, name both components clearly

Related

Frequently asked questions

What does the Talk Stage1 Extract AI skill do?

Extracts and structures source material (articles, transcripts, notes) into a talk summary with narrative arc, themes, metrics, and gaps. Auto-detects REX vs Concept type. Use when starting a new talk from any source material or auditing existing material before committing to a talk.

Why use Talk Stage1 Extract on TypingMind?

Because you install it once and use it with any model. Talk Stage1 Extract 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 Stage1 Extract 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-1-extract. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Talk Stage1 Extract?

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 Stage1 Extract?

As many as you like. As long as a model supports skills, you can use Talk Stage1 Extract with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Talk Stage1 Extract 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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