SuperAPI AI Directory
Partner中文AI newsFavorites
Skillslanshu-create-ai-presenter-video

lanshu-create-ai-presenter-video

Design & creation

View repository

Updated 10-15 17:33

About this skill

Lanshu Create AI Presenter Video

Produce a verified presenter-led video from minimal inputs. The final narration is the master clock for presenter motion, captions, graphics, cuts, and delivery duration.

Required outcome

  • Start from a topic or script and one authorized image containing one clear adult presenter.
  • Deliver a fully decoded master video, a smaller share copy, captions, production records, and separate machine and visual QA notes.
  • Keep the workflow portable across providers, models, aspect ratios, languages, and durations.

Operating rules

  • Confirm image rights, adult status, remote-upload approval, and voice-cloning authorization before the relevant remote action.
  • Treat remote generation as potentially billable. Before the first paid call, state the uploaded assets, requested seconds or units, known cost, pilot size, retry ceiling, and expected output. Reuse an approval already given for that exact plan.
  • Never infer a real voice from an image. Use an authorized sample or record a selected stock voice.
  • Lock the complete narration before presenter generation, caption timing, or final scene boundaries.
  • Prefer one presenter image, one voice identity, one visual treatment, and one continuous presenter source.
  • Mute video sources in the final composition. Route only the approved external narration and intentional mix tracks.
  • Preserve provider request bodies and task IDs without credentials or expiring URLs. Poll interrupted work before considering resubmission.
  • Use numeric checks for technical faults and normal-speed visual review for identity, mouth timing, blinking, gestures, hands, lighting, and continuity.
  • Stop after three rejected paid candidates, or before a change that materially affects cost, privacy, voice, appearance, or provider.
  • Do not claim completion until the final files fully decode, the contact sheet or full playback has been reviewed, and check_state.py reports verified.

Start or resume a job

For a new job, read generation.md. Set SKILL_DIR to the directory that contains this file; its location depends on the harness. Then run:

SKILL_DIR=/path/to/lanshu-create-ai-presenter-video

python3 "$SKILL_DIR/scripts/init_job.py" \
  --job-dir ~/Videos/my-presenter-video \
  --presenter-image ~/Pictures/presenter.png \
  --topic "用一分钟讲清楚上下文工程"

Use --script for an existing script file. Optional flags include --voice-sample, --supporting-media, --duration, --aspect, --width, --height, --fps, --watermark, and --cta. The initializer copies every input into the job and records job-relative paths, so the job directory is self-contained; keep later artifact paths job-relative too.

Inspect the actual source image and listen to any voice sample. Record the manual review and approvals in job.json, then run:

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

For an existing job, read job.json, current artifacts, task IDs, and QA reports, then run check_state.py (below) to find the earliest unfinished state. Resume there without regenerating accepted work.

Production state machine

Advance a job only when its evidence exists:

intake
→ content_locked       passing preflight report, script, beat sheet
→ audio_locked         decodable final audio, ASR report
→ visual_plan_locked   timeline, storyboard, plan.status "approved"
→ presenter_generated  recorded main_presenter capability, selected video, visual review
→ composition_checked  composition report
→ rendered             decodable render with video and audio
→ verified             master, share, delivery report with passing output loudness

Never edit state by hand. Record artifacts in job.json, then let the checker compute the state:

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

Without --write it only reports. It exits non-zero when the recorded state claims more than the evidence supports, and lists what the next state is missing.

1. Lock content and audio

Read generation.md.

  1. Turn a topic into one spoken content spine, or polish a supplied script without changing factual meaning silently.
  2. Save the production script, beat sheet, pronunciations, and narration sections.
  3. Generate the complete approved narration with one voice configuration.
  4. Normalize sections consistently, run ASR on the final audio, and correct material omissions, additions, numbers, names, or repeated speech.
  5. Record real durations. These durations now define the timeline.

2. Plan and generate the presenter

Choose a presenter-led, screen-demo, or mixed-explainer route. Use supporting media only when it proves or clarifies a spoken point.

When the provider caps request or reference-audio duration below the narration length, plan the split from the locked audio's ASR timings instead of guessing:

python3 "$SKILL_DIR/scripts/plan_segments.py" \
  --timings ~/Videos/my-presenter-video/qa/asr/sentences.json \
  --audio ~/Videos/my-presenter-video/assets/audio/final/narration.wav \
  --cap 15 --whole-seconds \
  --output ~/Videos/my-presenter-video/qa/requests/segment-plan.json

Save the plan path in plan.segment_plan. Generate every segment from the same image, seed, framing, light, and motion constraints.

Generate a short, low-cost pilot before a full run. Prioritize identity and mouth timing for the main track. Use one controlled gesture for an actionful opening or close. When body motion is accepted but mouth timing fails, preserve the motion plate and apply a dedicated lip-sync repair with the locked audio.

Archive the prompt, parameters, provider/model/version, region, task ID, requested seconds, and acceptance notes.

3. Edit the video

Read editing.md.

Build a deterministic timeline driven by the locked audio. Keep authored start, duration, and source offset independent. Add captions and keyword callouts only after audio and selected media are final.

When using HyperFrames, load the current hyperframes, general-video, and relevant domain instructions. Run the project check, inspect transition and emphasis frames, open the Studio preview, and wait for final visual approval before rendering.

4. Verify and deliver

Read qa-recovery.md.

Render at delivery quality, watch the complete video, and finalize it:

bash "$SKILL_DIR/scripts/finalize_delivery.sh" \
  ~/Videos/my-presenter-video/renders/rendered.mp4 \
  ~/Videos/my-presenter-video/outputs \
  my-video

The finalizer preserves the input aspect ratio, performs two-pass program loudness normalization, creates master/share encodes, fully decodes them, measures their delivered loudness, counts black and frozen-picture events, and produces a nine-frame contact sheet. It builds everything in a temporary directory and publishes nothing unless every check passes. The target defaults to -16 LUFS; set PROGRAM_LUFS (for example PROGRAM_LUFS=-14) when the destination requires another.

Inspect the contact sheet, review any freeze events against intentional still shots, record the outputs and delivery report in job.json, and run check_state.py --write to reach verified.

Default behavior for minimal input

  • Infer language from the request.
  • Use 9:16, 1080×1920, 30fps unless the intended platform suggests another format.
  • Preserve a supplied script's natural duration; for a topic, target 45–75 seconds.
  • Use a suitable stock voice when no authorized voice sample exists.
  • Use a presenter-led layout with a designed hook, 2–4 useful beats, and a concise close.
  • Omit music and promotional CTA unless requested or clearly justified.
  • Keep styling credible, contemporary, readable, and safe for the destination platform.

Required job artifacts

job.json
docs/{SCRIPT,BEAT_SHEET,TIMELINE,STORYBOARD}.md
assets/source/
assets/audio/{reference,raw,final}/
assets/video/{candidates,selected,render}/
assets/captions/
qa/{requests,asr,contacts,reports}/
renders/
outputs/

Reference routing

  • Read generation.md for intake, content, voice, tool selection, presenter prompts, paid generation, or provider changes.
  • Read editing.md for timeline construction, screen-demo layouts, openings, closes, captions, keyword callouts, previews, and exports.
  • Read qa-recovery.md before accepting media or delivery, and whenever lip sync, identity, hands, exposure, freezes, captions, audio, or remote jobs fail.