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Talking Head Video Pipeline

Community
naive-kun
talking-head-video-pipeline

End-to-end, stateful talking-head video production for complete beginners and experienced editors. Use when a user wants optional rough-cut guidance, transcription or caption revision, viewer-facing content logic groups, screenshot and demo placement, visual design, staged keyframe plus HyperFrames/GSAP preview, a synchronized final export, interrupted-work recovery, diagnosis, scoped revision, or reusable style learning from explicit feedback. Routes through internal workflow modules, optionally integrates video-use for raw footage, and preserves originals, evidence readability, privacy, and preview approval.

Overview

Publishernaive-kun
Repositorynaive-video-skill
Skill nametalking-head-video-pipeline
Stars
135
Forks
19
Bundled files
65
LicenseMIT
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.

  • 65 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by naive-kun on GitHub. Read the source before you install it.

Installation

Install the Talking Head Video 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/naive-kun/naive-video-skill/tree/main
  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/naive-kun/naive-video-skill.git \
  .claude/skills/talking-head-video-pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Talking Head Video 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 Talking Head Video 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 Talking Head Video 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.

Talking Head Video Pipeline

Turn raw or rough-cut talking-head footage into an approved working cut, editable captions, a motion-packaged preview, and a verified final export. Treat this file as the router and shared contract. Read only the routed internal workflow and references needed for the current stage.

Beginner Promise

Assume the user has never edited with Codex.

  • Ask one short question at a time only when an answer cannot be inferred safely.
  • Prefer a working default over presenting many technical choices.
  • Explain the next visible result, not the renderer internals.
  • Introduce rough cutting, transcription providers, or API setup only when raw footage actually needs that branch.
  • Explain paid and local choices plainly, recommend one when useful, and never force it.
  • End every stage with exact output paths and the next phrase the user can say.
  • Resume existing work instead of restarting it.

Non-Negotiable Rules

  1. Source is immutable. Never overwrite or destructively edit the supplied source media.
  2. Main audio is the clock. Do not retime, reorder, resample, or replace it unless the user explicitly approves a rough-cut strategy. After approval, the derived working_video becomes the downstream clock while the original remains immutable.
  3. Evidence stays readable. Never cover screenshot body text, product UI, faces, or user-marked safe zones. Do not blur evidence assets.
  4. Preview before final. Build an official preview and wait for approval unless the user explicitly requests direct export.
  5. Original-quality final. Do not present an upscaled low-resolution preview as a true high-resolution final. Use the original source as the final base whenever possible.
  6. Do not interrupt progress. If a render is advancing, wait and report progress. Change pipelines only after a real error or explicit approval.
  7. No private data in the skill. Project-local paths, media names, brand rules, screenshots, customer details, and API keys stay in the user's project.
  8. Learn only from explicit feedback. Do not infer permanent style rules from silence or one unconfirmed draft.
  9. Typography is part of correctness. Declare and verify font family, real weight, caption line policy, baseline alignment, and longest-label fit before approval.
  10. Public defaults stay brand-neutral. Store creator palettes, recurring card systems, and private visual rules only in the local profile.
  11. Public install exposes one Skill. Keep stage modules under references/workflows/; do not add nested SKILL.md files that package managers may register separately.
  12. Install operations are recoverable. Back up an existing installation before replacement and archive uninstall targets instead of performing recursive force deletion.
  13. Optional providers stay optional. The curated default shot pack is native HyperFrames + GSAP. Never clone, install, update, or require ShotCraft, Remotion, or another provider without explicit user consent. Native recipes must remain a complete fallback.

Read references/quality-gates.md before preview or export work.

Route Table

User intent or phraseRouteTypical output
初始化视频项目, 第一次用, start a video projectreferences/workflows/init.mdState, edit plan, design profile
原片还没剪, 删口误和停顿, 帮我粗剪, raw takes, rough cutreferences/workflows/rough-cut.mdApproved non-destructive rough cut and timing handoff
抽字幕轴, 只做字幕, transcribe, 改字幕references/workflows/captions.mdSRT, CSV, transcript JSON
帮我设计, 换颜色风格, 规划弹窗, designreferences/workflows/design.mdDESIGN and complete edit plan
参考这张截图设计, 按这张图的视觉语言, style referencereferences/workflows/design.md reference branchProject-local STYLE_REFERENCE
按语义匹配动效, GSAP 动效丰富一点, semantic motionreferences/workflows/design.md, then previewValidated MOTION_PLAN and preview
参考 ShotCraft, 推荐电影感镜头, cinematic shot referencereferences/workflows/design.md ShotCraft branchOptional mapped shot references in MOTION_PLAN
做预览, 加动效, build previewreferences/workflows/preview.mdOfficial preview URL
出成片, 导出 4K, export finalreferences/workflows/export.mdVerified final video
改这个, 恢复上一版, revisereferences/workflows/revise.mdScoped revision and new preview/final
进度, 到哪了, statusreferences/workflows/status.mdCurrent stage and next action
体检, 为什么失败, doctorreferences/workflows/doctor.mdRead-only diagnosis
以后都这样, 记住这个风格, learn thisreferences/workflows/learn.mdConfirmed project-local lessons
复盘成片, 下次怎么改进, retroreferences/workflows/retro.mdStructured delivery retrospective
升级状态, 迁移, migratereferences/workflows/migrate.mdState schema upgrade

Mode Detection

Before routing:

  1. Identify the user's video project directory, not this skill repository.
  2. Look for .naive-video-state.json.
  3. If absent and the task is more than a one-off caption text edit, load the init workflow.
  4. If present, read it plus EDIT_PLAN.md, DESIGN.md, and VIDEO_LESSONS.md only when relevant. Use working_video when present; otherwise use main_video.
  5. Run python3 tools/video_doctor.py --project <project_dir> when state and files disagree.

Use these stages:

text
initialized -> captions_ready -> design_ready -> preview_ready
-> approved -> rendering -> final_ready

Never mark a later stage until its quality gate passes.

Default Pipeline

When the user asks for the complete workflow:

  1. Inspect source media with ffprobe.
  2. Initialize project state and safe output directories.
  3. Ask whether the footage is already rough-cut only when that status is unclear. If it is raw, load the optional rough-cut workflow and require strategy approval.
  4. Produce or validate caption files from working_video when present, otherwise main_video.
  5. Ask whether screenshots, recordings, product images, charts, or demos must appear. Let the user choose semantic placement, exact seconds/spoken-sentence anchors, or a hybrid; explain that exact anchors are more precise.
  6. Normalize word-level timing when available and split the speech into viewer-facing logic groups. Do not fake word precision from whole-caption starts.
  7. Collect or infer a style profile and safe zones. Offer optional screenshot-reference matching to beginners.
  8. Write an edit plan and semantic motion plan with every requested insert, logic-group link, and time range.
  9. For new projects, automatically map a few safe semantic nodes to the curated ShotCraft-inspired native pack unless the user chooses skip. Preserve existing approved projects, the native recipe, and offline fallback.
  10. For first-use, new-style, or reasoning-heavy work, render representative static keyframes and resolve composition problems before a full dynamic preview.
  11. Build a HyperFrames + GSAP preview using a browser-friendly proxy if needed.
  12. Run preview quality gates and provide the official preview URL.
  13. Wait for approval unless explicitly told to skip.
  14. Render overlays or final composition using the approved working video and its audio clock.
  15. Verify file existence, duration, resolution, frame rate, and audio.
  16. Record only confirmed feedback as project-local lessons.
  17. After delivery, run a factual retrospective and promote only explicit, privacy-safe rules.

Project Files

The init workflow creates this minimal project memory without touching source media:

text
<project_dir>/
├── .naive-video-state.json
├── EDIT_PLAN.md
├── DESIGN.md
├── CONTENT_LOGIC.json       # viewer-facing reasoning groups and timed beats
├── STYLE_REFERENCE.md       # created only when a reference image is used
├── MOTION_PLAN.json         # created when semantic motion is planned
├── VIDEO_LESSONS.md
├── VIDEO_RETRO.md
├── edit/
│   ├── rough-cut.mp4            # optional; original media is never overwritten
│   ├── rough-cut-edl.json       # optional decision record
│   ├── script-aligned.srt
│   ├── caption-table.csv
│   └── transcripts/
├── preview/
├── final/
└── qa/
    └── KEYFRAME_REVIEW.md   # static-composition review before dynamic preview

State is operational metadata, not a media database. Keep it small and never store transcript bodies, private screenshots, or secrets in it. See references/state-management.md.

Style Defaults

If the user wants speed and gives no reference:

  • Preserve the source aspect ratio.
  • Use a clean, readable card system with one configurable accent color.
  • Use bold captions with restrained keyword emphasis.
  • Use GSAP transforms and opacity for motion.
  • Place cards in verified empty space.
  • Reduce motion density while screenshots or demos are visible.
  • Keep readable text level and baseline-aligned; animate the containing component instead of skewing labels.
  • Choose an explicit caption maximum line count and wrap policy.

Ask for an accent color only if brand consistency matters. Otherwise use the neutral preset and make it easy to change later. Beginners may optionally provide a screenshot and choose low, medium, or high reference strength; explain that the skill copies visual language, never the source brand or content. Motion density is restrained, balanced, or energetic. See references/style-onboarding.md.

Detailed GSAP recipes and semantic mappings live in references/motion-recipes.md. Runtime and plugin selection live in references/gsap-runtime.md. Screenshot extraction and anti-copy rules live in references/style-reference-workflow.md. Asset timing choices live in references/asset-onboarding.md. Load them only for the matching design branch.

ShotCraft discovery and adaptation rules live in references/shotcraft-integration.md; the native beginner pack lives in references/shotcraft-default-pack.md. Load them for new automatic projects, named cards, or cinematic shot requests. Never install a provider silently.

Word-level timing, viewer-facing logic groups, and accumulation/exit behavior live in references/content-logic-workflow.md. Load it after captions and before semantic design.

Typography, component geometry, glass notifications, and seek-safe focus/type/split adaptation live in references/visual-quality-rules.md. Load it for every design or preview task.

Self-Iteration Contract

Self-iteration means improving the current user's workflow without leaking it into public defaults.

Classify feedback into one scope:

  • project: applies only to this video.
  • profile: applies to this user's future videos; write it only after explicit confirmation.
  • product: a privacy-safe, general reliability improvement; propose it to the skill maintainer separately.

Record profile feedback in VIDEO_LESSONS.md with the user's words, the confirmed rule, and the affected stage. Never copy media paths or private evidence into this repository. See references/self-iteration.md.

Use the retro workflow after delivery to separate failures, environment issues, and taste feedback before promoting any rule. New projects import active private-profile rules into VIDEO_LESSONS.md so the editor can actually apply them.

Recovery Rules

  • If preview stops responding, check the process and port before rebuilding.
  • If a browser cannot decode HEVC/Main10 smoothly, make an H.264 proxy for preview only.
  • If a render session is running, poll it; do not terminate it merely because it is slow.
  • If partial frames exist, compare expected and actual frame counts before resuming or restarting.
  • Move damaged partial outputs to a project-local quarantine only after confirming they are not the sole valid result.
  • Keep logs outside command pipes that can terminate long jobs.

Privacy Before Publishing

Run:

bash
bash scripts/doctor.sh --privacy-scan .
python3 tools/validate_skill.py .

Block publication if either command reports personal absolute paths, secrets, private media names, invalid frontmatter, multiple skill manifests, missing internal workflows, unsafe install commands, or broken templates.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 5 more files.

Frequently asked questions

What does the Talking Head Video Pipeline AI skill do?

End-to-end, stateful talking-head video production for complete beginners and experienced editors. Use when a user wants optional rough-cut guidance, transcription or caption revision, viewer-facing content logic groups, screenshot and demo placement, visual design, staged keyframe plus HyperFrames/GSAP preview, a synchronized final export, interrupted-work recovery, diagnosis, scoped revision, or reusable style learning from explicit feedback. Routes through internal workflow modules, optionally integrates video-use for raw footage, and preserves originals, evidence readability, privacy, a...

Why use Talking Head Video Pipeline on TypingMind?

Because you install it once and use it with any model. Talking Head Video 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 Talking Head Video Pipeline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/naive-kun/naive-video-skill/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Talking Head Video 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 Talking Head Video Pipeline?

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

Is the Talking Head Video Pipeline AI skill free?

Yes. It is published on GitHub by naive-kun under the MIT 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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