Pre Production logo

Pre Production

Community
matyasstoch
pre-production

YouTube video pre-production. Use when David is preparing a video that involves a tool, repo, product, build, or use-case demo. Two phases — (1) VET the tool actually works before committing recording time, then (2) BUILD the simplest reproducible steps David records from. Triggers on pre-production, pre-prod, test before filming, vet this tool, try it out, sandbox test, viability check, does this actually work, use case testing, figure out simplest steps, reproducible steps, demo prep, build the use case.

Overview

Publishermatyasstoch
Repositorydavid-skills
Skill namepre-production
Stars
61
Forks
11
Bundled files
Instructions only
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 matyasstoch on GitHub. Read the source before you install it.

Installation

Install the Pre Production 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/matyasstoch/david-skills/tree/main/skills/pre-production
  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/matyasstoch/david-skills.git /tmp/david-skills
mkdir -p .claude/skills
cp -r /tmp/david-skills/skills/pre-production .claude/skills/pre-production
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pre Production 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 Pre Production 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 Pre Production 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.

YouTube Pre-Production

Two phases of getting a video ready to record:

  1. VET — does the tool/repo/product actually work as advertised? Find the demo-worthy moments. Make a go/no-go decision before investing recording time.
  2. BUILD — once it's a go, figure out the simplest reproducible path to the end outcome and write it up as a clean step-by-step file David records from.

Phase 1 is for tool/repo/product videos. Phase 2 is for any build/use-case video. Many videos need both; some need only one.

Video Outline

A "Video Outline" is what the actual video will look like, the structure of the video.

The outline is a beat-by-beat skeleton, written into the video's .md file. It is high-level beats with a few talking points under each — not a full script. David fills the exact intro and wording at record time; the outline locks the order and the points he must hit.

David's proven outline structure (from his recent uploads)

This is the structure that works on his channel. Use it as the default scaffold; adapt per video.

  1. Hook (first ~15 seconds). Open with the payoff, not the setup. Pattern: "[Tool] is insane" + a stacked list of the wild outcomes it unlocks (e.g. "outperform models 100x bigger, cut API costs to near zero, build X for your business"). If a credibility proof exists (a famous dev endorsing it, a real result), drop it here.
  2. Problem + promise. Name the old pain ("until now, X had two big problems..."), then state the agenda plainly: "in this video I'm going to show you A, B, and C."
  3. Retention hook + objection crushing. "If you're serious about AI, watch until the end." Pre-empt the viewer's excuses fast: "isn't this complicated? No — one install. Too expensive? It's cheap. Not technical? If you can copy-paste, you can do this."
  4. Setup / install. Step-by-step from scratch, plain English, zero assumed knowledge. Plug the free resource bundle ("first link below the video").
  5. Main demo (the meat). The walkthrough. Every step explained with its WHY. This is where the pre-production phases above pay off — the vetted money-shots and the stripped reproducible steps go here.
  6. VPS / sponsor beat. Weave the sponsor in natively, not bolted on — e.g. "do it on a VPS so it runs while you sleep." The sponsor's value must be a real beat in the workflow.
  7. Soft mid-video CTA. Slip in "subscribe" naturally (don't break flow). No hard CTA in the first minute.
  8. Outro CTA. Recap the transformation -> push action ("watching is good, taking action changes your life") -> sponsor link + free bundle link.

Retention rules (baked into the outline)

  • Open on the outcome, never the backstory.
  • State "what you get by watching to the end" early, and re-tease it mid-video with a different reason.
  • Crush objections before the viewer can raise them.
  • Every section must add new value; no repeated examples or points.

What makes an outline bad

  • A weak/slow hook that buries the payoff.
  • "A video about everything" — no single hero use-case as the spine (covering 5 things = covering nothing).
  • A sponsor segment that feels bolted on instead of native to the workflow.
  • No retention hook / no clear reason to keep watching.
  • Steps with no WHY behind them.

How to Communicate with David (CRITICAL)

David needs to understand every step deeply enough to explain it to viewers on camera. This is non-negotiable.

Before each step: tell David what you're about to do (one sentence) and WHY it's needed.

After each step:

  • Report whether it worked as expected
  • "What just happened:" — explain in plain language what the step actually did under the hood. No jargon without explanation. Audience ranges from beginners to advanced
  • "Why we need it:" — connect the step to the overall goal. Why can't we skip it?
  • "Next step:" — preview what's next, ask before proceeding

Pacing rules:

  • ONE step at a time. Never batch or rush ahead
  • Wait for David's go-ahead before each step
  • If something fails, explain what went wrong and why before fixing
  • Keep explanations concise but thorough — short sentences, plain language
  • NEVER assume David already understands a concept. Nothing is "obvious"

The goal: after this process, David can sit in front of a camera and walk viewers through every step from memory — not just WHAT to do but WHY each step matters.

Where Testing Happens

Test on the exact same setup David will record on. Recording on a Hostinger VPS → test on a Hostinger VPS. Recording on Mac localhost → test on localhost. Never test in one environment and expect steps to work in a different one on camera.

Never install into David's main project dirs — always use sandbox/.


Phase 1 — VET (tool/repo/product viability)

1. Understand the tool

  • Read the video doc in work/youtube/videos/ if one exists
  • Identify what the tool claims to do and the hook for the video
  • Ask David what specific claims/features he wants to verify

2. Set up in sandbox

  • Clone/install into sandbox/, step by step, explaining each step
  • Document setup friction (viewers will hit the same issues)

3. Run the four-test viability check

Escalating difficulty. Each test must pass before the next:

TestPurposeWhat it proves
SanityTrivial taskThe tool runs at all
StressComplex/form-heavy taskHandles real-world complexity
Self-healingTask requiring adaptationThe key differentiator works
Real-workflowDavid's actual use caseIt's useful, not just a demo toy
  • Run one at a time. After each: what happened, did it match expectations, any surprises
  • Diff any files the tool modified

4. Document results

Summarize: which tests passed/failed, where the "money shot" demo moments are, gotchas viewers will hit, go/no-go recommendation. Update the video doc in work/youtube/videos/.

5. Go/no-go decision

Present findings. David decides whether to commit.


Phase 2 — BUILD (simplest reproducible steps)

Once vetted, figure out the cleanest path to the end outcome so David records cleanly.

Core principle: Start with the end outcome. Then find the simplest reproducible path to it. Trial-and-error is fine during exploration — NOT in the final output. Strip everything that wasn't actually needed.

1. Define the end outcome

Write exactly what the use case looks like when it works. What does David demonstrate? What's the money shot? If unclear, ask — don't guess.

2. Explore freely (trial and error allowed)

Figure out how to achieve the outcome. Try, fail, iterate. Messy on purpose — none of it goes in the final output yet.

3. Strip to the simplest reproducible path

Remove every unneeded step, dead end, abandoned attempt, debugging detour. Keep only what a fresh user needs to reproduce the result.

4. Write the clean step-by-step markdown

One markdown file with:

  • The end outcome (what we're building/showing)
  • Step-by-step instructions, in order
  • For EACH step: a short WHY — what it does, what breaks without it
  • Non-obvious gotchas (things that look broken but aren't)
  • Known issues: what they look like, how to fix

Plain English. No advanced terminology — the pre-prod person may not be a developer. David must read this on camera and explain every step without guessing.

Output location: one file per use case inside the video's folder, work/youtube/videos/<video-slug>/ alongside MAIN.md. E.g. work/youtube/videos/8-hermes-use-cases/use-case-01.md.

5. Verify the steps work — multiple times

Not optional. Run the stripped-down steps from scratch on a fresh setup, more than once (catch flakiness/hidden state). If anything fails, fix the file and run again. Only hand to David once it reproduces cleanly every time. David should hit zero surprises while recording.


Pitfalls

  • Don't rush tests. David needs to understand every step for the video.
  • Don't skip the self-healing test (phase 1) — usually the key differentiator. If it fails, rethink the angle.
  • Don't install into main project dirs — always sandbox/.
  • Don't assume marketing claims are true — the whole point is to verify.
  • Don't include trial-and-error in the final steps (phase 2) — minimal reproducible path only.
  • Don't skip the WHY on any step — no reasoning = unusable for recording.
  • Don't guess the end outcome — ask David.

Verification

  • (Phase 1) Four tests run and documented; video doc updated with go/no-go
  • (Phase 2) Clean step-by-step file reproduces the outcome cleanly, every time, on a fresh setup
  • David has enough understanding to explain the whole thing on camera

Frequently asked questions

What does the Pre Production AI skill do?

YouTube video pre-production. Use when David is preparing a video that involves a tool, repo, product, build, or use-case demo. Two phases — (1) VET the tool actually works before committing recording time, then (2) BUILD the simplest reproducible steps David records from. Triggers on pre-production, pre-prod, test before filming, vet this tool, try it out, sandbox test, viability check, does this actually work, use case testing, figure out simplest steps, reproducible steps, demo prep, build the use case.

Why use Pre Production on TypingMind?

Because you install it once and use it with any model. Pre Production 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 Pre Production in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/matyasstoch/david-skills/tree/main/skills/pre-production. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pre Production?

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 Pre Production?

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

Is the Pre Production AI skill free?

It is published on GitHub by matyasstoch. Check the repository for licensing terms. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇