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Talk Pipeline

CommunityPopular
FlorianBruniaux
talk-pipeline

Orchestrates the complete talk preparation pipeline from raw material to revision sheets, running 6 stages in sequence with human-in-the-loop checkpoints for REX or Concept mode talks. Use when starting a new talk pipeline, resuming a pipeline from a specific stage, or running the full end-to-end preparation workflow.

Overview

PublisherFlorianBruniaux
Repositoryclaude-code-ultimate-guide
Skill nametalk-pipeline
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 Pipeline 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/orchestrator
  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/orchestrator .claude/skills/talk-pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Talk Pipeline 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 Pipeline 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 Pipeline 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 pipeline orchestrator

Orchestrates the complete talk preparation pipeline from raw material to revision sheets. Can run the full pipeline or a single isolated stage.

Modes

  • --rex: REX talk with git/code proof (changelog, commits, measured metrics)
  • --concept: Conceptual talk from article, ideas, notes (skips Stage 2)

Usage

/talk-pipeline                          # full pipeline, asks for context
/talk-pipeline --stage=extract          # run a single isolated stage
/talk-pipeline --rex                    # REX mode (git archaeology included)
/talk-pipeline --concept                # Concept mode (skip research)
/talk-pipeline --rex --slug=my-talk --event="Conf 2026" --date=2026-06-15 --duration=30

Context collection

Ask with AskUserQuestion if not provided:

- slug         : kebab-case identifier (e.g., my-talk-topic)
- event        : event name (e.g., Conf 2026, Tech Meetup)
- date         : talk date (YYYY-MM-DD)
- duration     : duration in minutes (e.g., 30)
- audience     : audience profile (e.g., senior devs, tech leads, non-tech)
- type         : --rex or --concept
- source_path  : path to source material (article .mdx, transcript .md, notes)
- repo_path    : (REX only) path to git repository for archaeology

Workflow

  1. Collect context using AskUserQuestion for required metadata
  2. Route by mode (--rex vs --concept, skip Stage 2 if concept)
  3. Run Stage 1 (/talk-stage1-extract, always first)
  4. Run Stages 2-4 in parallel (after Stage 1 confirmed)
    • REX: extract -> research + concepts + position (in parallel)
    • Concept: extract -> concepts + position (in parallel, skip research)
  5. CHECKPOINT: wait for angle + title choice (Stage 4 output)
  6. Run Stage 5 (/talk-stage5-script with validated choice)
  7. Run Stage 6 (/talk-stage6-revision)
  8. Final summary: list all generated files with their paths

Dependency graph

         extract (Stage 1)
               |
    +----------+-----------+
    v          v           v
research    concepts   position
(Stage 2)   (Stage 3)  (Stage 4)
[--rex only]            [CHECKPOINT]
    |          |           |
    +----------+-----------+
               v
          script (Stage 5)
               |
               v
         revision (Stage 6)

Stage Routing (--stage=X)

If --stage is provided, run only the corresponding skill:

StageSkill to invoke
extract/talk-stage1-extract
research/talk-stage2-research
concepts/talk-stage3-concepts
position/talk-stage4-position
script/talk-stage5-script
revision/talk-stage6-revision

Output naming convention

talks/{YYYY}-{slug}-summary.md           # extract
talks/{YYYY}-{slug}-git-archaeology.md   # research
talks/{YYYY}-{slug}-changelog-analysis.md
talks/{YYYY}-{slug}-timeline.md
talks/{YYYY}-{slug}-concepts.md          # concepts
talks/{YYYY}-{slug}-concepts-enriched.md
talks/{YYYY}-{slug}-angles.md            # position
talks/{YYYY}-{slug}-titre.md
talks/{YYYY}-{slug}-descriptions.md
talks/{YYYY}-{slug}-feedback-draft.md
talks/{YYYY}-{slug}-pitch.md             # script
talks/{YYYY}-{slug}-slides.md
talks/{YYYY}-{slug}-kimi-prompt.md
talks/{YYYY}-{slug}-revision-sheets.md   # revision

Final summary format

After Stage 6 completes, display:

Pipeline complete. Files generated:

Stage 1: Extract
  v talks/{YYYY}-{slug}-summary.md

Stage 2: Research (REX only)
  v talks/{YYYY}-{slug}-git-archaeology.md
  v talks/{YYYY}-{slug}-changelog-analysis.md
  v talks/{YYYY}-{slug}-timeline.md

Stage 3: Concepts
  v talks/{YYYY}-{slug}-concepts.md
  v talks/{YYYY}-{slug}-concepts-enriched.md (if repo available)

Stage 4: Position
  v talks/{YYYY}-{slug}-angles.md
  v talks/{YYYY}-{slug}-titre.md
  v talks/{YYYY}-{slug}-descriptions.md
  v talks/{YYYY}-{slug}-feedback-draft.md

Stage 5: Script
  v talks/{YYYY}-{slug}-pitch.md
  v talks/{YYYY}-{slug}-slides.md
  v talks/{YYYY}-{slug}-kimi-prompt.md   <- copy-paste into kimi.com

Stage 6: Revision
  v talks/{YYYY}-{slug}-revision-sheets.md

Next step: open kimi-prompt.md, verify no {PLACEHOLDER} remains, paste into kimi.com.

Anti-patterns

  • Do not run Stage 5 without an explicit angle + title choice from the user
  • Do not run Stage 2 (research) in --concept mode
  • Do not proceed to the next stage if an upstream stage failed
  • Do not invent metrics or dates not present in the source material

Validation

  • All upstream files exist before launching a downstream stage
  • Stage 4 CHECKPOINT respected before script
  • Outputs named per convention talks/{YYYY}-{slug}-{stage}.md
  • No empty placeholders in generated files

Tips

  • The orchestrator is the recommended entry point for first use
  • For repeat users who know the pipeline, running individual stage skills is faster
  • The --stage=X flag is useful for rerunning a single stage without redoing the full pipeline

Related

Frequently asked questions

What does the Talk Pipeline AI skill do?

Orchestrates the complete talk preparation pipeline from raw material to revision sheets, running 6 stages in sequence with human-in-the-loop checkpoints for REX or Concept mode talks. Use when starting a new talk pipeline, resuming a pipeline from a specific stage, or running the full end-to-end preparation workflow.

Why use Talk Pipeline on TypingMind?

Because you install it once and use it with any model. Talk Pipeline 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 Pipeline 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/orchestrator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Talk Pipeline?

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 Pipeline?

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

Is the Talk Pipeline 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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