lanshu-create-ai-presenter-video

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Turn a topic or script into a verified, publish-ready explainer video — presented by a digital human from one authorized portrait, or acted out on screen in one of twelve explainer styles — driven by the coding agent you already use.

lanshu-create-ai-presenter-video is an Agent Skill that takes an AI agent through the full production of an explainer video. On the presenter route that means script, narration, presenter generation, lip-sync, captions and keyword motion graphics, editing, rendering, and quality assurance. On the styled route the narration is performed scene by scene in one of twelve styles (nine visual, three narrative), from a word-timed story to a rendered film. Every stage is gated by evidence on disk, and nothing is delivered until the output passes decode and loudness checks.

Highlights

• Harness-neutral. A standard SKILL.md that loads in Claude Code, Codex, Gemini CLI, Cursor, OpenCode, GitHub Copilot, and other Agent Skills clients. Any agent that can read files and run shell commands can follow it directly.
• Provider-neutral. On the presenter route, voice, presenter video, lip-sync, and speech recognition are selected by capability at run time; each job records the provider, model, parameters, and task IDs it actually used. The styled route voices with MiniMax (word timestamps come from the voice engine) or takes narration you voiced or recorded elsewhere.
• Twelve performed explainer styles. Nine visual styles — editorial, tech HUD, notebook, paper pop-up, pop comic, 3D one-take, drafting sheet, chalkboard, and clay town — plus three narrative styles: derivation, casefile, and life-scene sitcom. Each has a reusable kit, starter, and example project.
• Audio-locked timeline. The approved narration is the master clock for presenter motion, captions, cuts, and final duration, which keeps lip-sync and scene boundaries aligned.
• Evidence-gated workflow. Eight production states, from intake to verified, computed from artifacts instead of declared by hand.
• Cost guardrails. Pilot-first generation, an explicit billing statement before the first paid call, retry ceilings, and task-ID recovery to avoid duplicate charges.
• Verified delivery. Master and share encodes are fully decoded and loudness-checked before any file is published.

Two routes

The agent infers the route from your request or asks once, with the style gallery. All styles share one contract — word-timed captions, chapter chips, a performed closing line, a one-frame recap. Nine are visual styles that differ in how ideas are acted out: editorial paper, tech HUD, notebook and pen, paper pop-up book, pop comic, 3D one-take, drafting sheet, chalkboard, and a clay town. Below is the same moment of the bundled KV cache explainer in all nine:

Three are narrative styles, where the look comes with a way of telling and the script can draw on its suggested arc: 推演 (derive it on one canvas, formulas and geometry bound by colour), 案卷 (investigate it on an evidence board, from the anomaly to the closed case) and 小剧场 (a life-scene sitcom: a misconception, a bill or a queue that shows the cause, one sentence that maps the analogy to the real term). Below is the same KV cache topic told the three ways:

A new topic becomes a playable draft in any style in a few commands — narration, word timing, captions, chapters, closing line, recap, and sound are generated; the agent then performs each line’s scene. See explainer/STYLES.md to choose a style and references/styled-explainer.md for the workflow — including an optional method for bringing your digital human into a styled film.

What you provide

How it works

Presenter route:

Topic or script + authorized portrait
        │
        ▼
Lock script and full narration ──► narration becomes the master clock
        │
        ▼
Low-cost presenter pilot
        │
        ▼
Presenter generation (split at real pauses when a provider caps duration)
        │
        ▼
Audio-driven edit: captions, keyword graphics, cover, close
        │
        ▼
Technical and visual QA
        │
        ▼
Master, share copy, contact sheet, and delivery report

Styled route:

Topic or script ──► choose a style (menu + galleries, or drafts in the nine visual styles)
        │
        ▼
Script written for the style ──► voice with word timestamps (or your own narration)
        │
        ▼
Draft film from the style's starter: captions, chapters, closing line, recap, sound
        │
        ▼
Storyboard ──► every line performed on screen, on its words
        │
        ▼
QA (check, stillness, cover) ──► render ──► master, share copy, subtitles, delivery report

Each state requires evidence before a job may advance. check_state.py computes the state from the job’s artifacts:

On the styled route, audio_locked also needs a voiced story.json (a --dry draft never counts), and presenter_generated is reached by the performed film and its visual review instead of a presenter video.

Requirements

• An Agent Skills harness, or any coding agent that can read files and run shell commands.
• Python 3.9+, FFmpeg with ffprobe, Bash, jq, awk, and sed.
• For the presenter route: access to at least one voice synthesis, presenter video generation, and lip-sync capability — a cloud CLI, an API, or a local model — and optionally a deterministic timeline compositor such as HyperFrames for captions, motion graphics, and final rendering.
• For the styled explainer: Node.js (HyperFrames runs with npx), rsync, Python with numpy (sound bed; point PYTHON at it), and a MiniMax API key (MINIMAX_API_KEY) for the voice and its word timestamps — or your own recorded narration.

Installation

The repository directory is the skill. Clone it into your harness’s skills directory and keep the folder name lanshu-create-ai-presenter-video, which the Agent Skills specification requires to match the skill name.

For example, with Claude Code:

git clone https://github.com/cclank/lanshu-create-ai-presenter-video.git \
  ~/.claude/skills/lanshu-create-ai-presenter-video

Run git pull in the installed directory to update. If you use several harnesses, clone once into each skills directory.

Tagged versions and a ready-to-extract skill ZIP are available in Releases. See CHANGELOG.md for version history.

Agents without Agent Skills support. Clone the repository anywhere, then start the session with:

Read /path/to/lanshu-create-ai-presenter-video/SKILL.md and follow its workflow exactly.

Quick start

Most harnesses select the skill automatically from its description, so you can describe the video you want:

Turn this script and portrait into a 30-second 16:9 presenter video with live captions.

For a styled explainer you don’t need to know the styles yet — ask, and the agent shows the twelve with pictures and recommends a few for your topic:

Make a 40-second explainer about RAG, no presenter. Which styles are there?

Use the explicit invocation from the table above to guarantee selection. The skill works in the language of your request.

The agent runs the bundled scripts itself. To set up a job manually, point SKILL_DIR at your installation:

SKILL_DIR=~/.claude/skills/lanshu-create-ai-presenter-video

python3 "$SKILL_DIR/scripts/init_job.py" \
  --job-dir ~/Videos/my-presenter-video \
  --presenter-image ~/Pictures/presenter.png \
  --topic "Context engineering in one minute" \
  --duration 60 \
  --aspect 9:16 \
  --rights-confirmed \
  --adult-presenter-confirmed

Complete the manual review and upload approvals in job.json, then run preflight:

python3 "$SKILL_DIR/scripts/preflight.py" ~/Videos/my-presenter-video/job.json

After each stage, record its artifacts in job.json and let the checker compute the state:

python3 "$SKILL_DIR/scripts/check_state.py" ~/Videos/my-presenter-video/job.json --write

A styled explainer by hand, from a job directory (X=$SKILL_DIR/explainer):

python3 "$SKILL_DIR/scripts/init_job.py" --job-dir . --route styled --explainer-style v8-chalkboard --topic "RAG in 40 seconds"
python3 "$X/tools/story.py" story              # story/script.md → voice + word times → story/story.json
bash "$X/tools/new_film.sh" v8-chalkboard story film   # a playable draft; perform each line in film/beats.js
python3 "$X/tools/qa.py" film qa/film          # check, stillness, snapshots, cover
bash "$X/tools/render.sh" film rag outputs     # master, share copy, subtitles

Scripts

Explainer tools

Under explainer/tools/, used on the styled route:

Reference guides

The agent loads each guide only when it reaches the matching stage, which keeps context usage low.

Repository layout

lanshu-create-ai-presenter-video/
├── SKILL.md                 # Skill entry point: metadata and workflow
├── README.md
├── README.zh-CN.md
├── agents/
│   └── openai.yaml          # Codex display metadata; other harnesses ignore it
├── assets/
│   └── job.template.json    # Job manifest template
├── references/              # Stage guides loaded on demand
├── scripts/                 # Job setup, gates, segment planning, delivery
├── explainer/               # Styled explainer engine: core runtime, twelve style kits + starters, tools, examples
│   ├── STYLES.md            # Style chooser with the gallery
│   ├── KITS.md              # The kit contract and tools
│   ├── kits//        # One look per style, and starter/ = the film minus the topic
│   ├── stories/             # Example scripts (KV cache) and a draft test story
│   ├── examples/kv-cache/   # The finished KV cache film in each style
│   └── tools/               # story.py, new_film.sh, qa.py, render.sh, srt.py, …
└── tests/
    ├── smoke.sh             # End-to-end test with synthetic media
    └── explainer_smoke.sh   # Styled route: intake, story, starter sync, state

Defaults

Safety and cost controls

• Image rights and adult-presenter status are confirmed before any upload.
• Voice cloning requires explicit authorization, and a voice is never inferred from an image.
• Before the first paid call, the agent states the uploads, requested seconds, known price, pilot size, and retry ceiling.
• When a provider caps duration, the quote uses the total requested seconds from the segment plan.
• Interrupted remote tasks are polled by their saved task ID before any resubmission.
• Work stops after three rejected paid candidates, with a summary of the recurring failure and remaining options.
• On the styled route the only paid call is the voice — one call per line, cached, so an edit re-voices only that line; drafts in the nine visual styles cost nothing more.

Privacy

• The repository contains no API keys, access tokens, signed URLs, or user media.
• Provider request records are stored without credentials or expiring URLs.
• Preflight and delivery reports store file names or job-relative paths only, never absolute machine paths.
• The styled explainer downloads font subsets from Google Fonts and jsDelivr (only the characters used); word timing comes from the voice provider’s own timestamps, so no speech model is installed; snapshots are taken with --describe false, so frames stay local.

Testing

The smoke test drives a synthetic job from intake to verified without calling any remote service. It requires FFmpeg, jq, and Python 3.9+:

bash tests/smoke.sh
bash tests/explainer_smoke.sh   # styled explainer; also needs Node.js and rsync

CI runs metadata validation, a portability check, syntax checks for the explainer engine, and both smoke tests on every push and pull request.

Contributing

Issues and pull requests are welcome. Reports from different harnesses and providers are especially valuable, as are improvements to the workflow, compatibility, and quality checks. To add another explainer style, follow the kit contract in explainer/KITS.md and the starter brief in explainer/STARTER-BRIEF.md.

Author

Created and maintained by 岚叔 (Lanshu). Follow on X for updates and new AI video workflows:

License

Released under the MIT License. Fonts, libraries, and services the explainer engine uses are listed in explainer/THIRD_PARTY.md.