Higgsfield Faceless Video — Agent Skill by Higgsfield AI

From the author

Create a finished faceless explainer, documentary, illustrated story or children's music video, or edit a previously created faceless video. Use when: "make a faceless video", "create a narrated explainer", "make a children's song video", "edit this faceless video". NOT for: a single silent clip, ads or UGC, unrelated footage edits, or script-only requests.

— the description by Higgsfield AI

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The higgsfield-faceless-video skill provides a structured process for creating finished faceless video content. It supports various formats such as illustrated explainers, documentaries, illustrated stories, and children's music videos. The skill manages the end-to-end production workflow, including script validation, voice selection, asset management, and final assembly of video and audio blocks. It also provides capabilities for editing previously generated videos and adding subtitles.

What it does

Generates finished faceless videos based on user-provided topics and scripts.
Supports multiple production modes including illustrated explainers, channel-style motion, and children's songs.
Validates scripts against duration requirements and timing gates before generation.
Manages voice selection and dialogue generation using specified presets.
Assembles video and audio blocks into a final output file.
Adds subtitles to videos using local transcription models.

When to use it

When you need to create a narrated explainer or documentary video.
When you want to produce a children's song video with consistent visuals.
When you need to edit or update a previously generated faceless video.
When you require a video with a specific visual style and structured narration.

Advantages

Ensures consistent visual identity across video blocks.
Provides tools to verify script timing and narration duration before incurring costs.
Supports specific production modes for different content types like diagrams or stories.
Enables precise assembly of video and audio pairs without altering speech speed.

Usage examples

“make a faceless video about the history of the steam engine”: The agent will initiate the production workflow to create a documentary-style video, including script validation and asset generation.
“make a children's song video about counting apples”: The agent will use the song production mode to generate a synchronized music video based on the provided topic.
“edit this faceless video to change the narration in the second block”: The agent will use the editing workflow to update the specified block while preserving the rest of the video's production state.
“create a narrated explainer about photosynthesis”: The agent will generate an illustrated explainer video using diagram-led scenes and narration.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/higgsfield-ai/higgsfield-faceless-video/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip higgsfield-faceless-video.zip -d .agents/skills

Documentation by Higgsfield AI

Faceless video

Create one finished video with a consistent visual style and narration. For an illustrated explainer use deliberate, diagram-led scenes; for a story or documentary adapt the pacing and visuals to the brief. Do not treat an explainer as a request for rapid cuts. Preserve choices and assets supplied by the user.

Choose the requested mode before generating: illustrated explainers use references/production.md; fast-cut channel motion uses references/channel-motion.md; stills and picture stories use references/picture-story.md; a sung children's music video uses references/song.md; explicitly requested talking characters use references/talking-characters.md; a requested presenter overlay uses references/presenter.md alongside the motion workflow; edits to a previously generated video use references/editing.md. A request for background music alone is not a song request. Never infer a presenter from an incidental photo.

When the brief supplies animation_mode, treat fully_animated as motion and scene_based as stills without asking again. Resolve song intent from the complete brief, including topic and script; an explicitly sung children's video uses song mode even when a voice is supplied. Preserve the production mode when continuing or editing an existing video.

Before spending credits

Resolve missing topic, approximate duration, aspect ratio, language, visual style, voice, music and subtitle choices. Never invent a preset or voice ID. Use higgsfield preset list video-explainer --json and higgsfield voices list --json for current choices, and resolve a selected preset with higgsfield preset resolve video-explainer <id> --json. Confirm photo roles before using reference images. Honor the requested duration; never silently round it up.

Find this skill's installed directory and retain its absolute path as SKILL_DIR. Keep all work in a persistent session directory, not the skill directory. Before generation verify higgsfield account status, ffmpeg, ffprobe, python3, curl, and awk, and inspect the live schemas using higgsfield model get for each planned model. Estimate generation costs with higgsfield generate cost where supported; if an estimate is unavailable, disclose that before generation. If any required stage, model, dependency, or requested feature is unavailable, explain and stop before paid work. Do not call explainer_video or video_explainer.

Read references/production.md for the generation, assembly, QC, recovery and delivery procedure. For subtitles, read references/captions.md and check its dependencies before paid generation. Read references/creative.md for style, assets, long-form planning and review, references/styles.md for style guidance, and references/prompts.md when writing the scene plan. The executable helper scripts/assemble_blocks.sh assembles ordered video/audio pairs without changing speech speed; read its usage before running it. The previous skill name is supported for existing installations.

Files

ATTRIBUTION.mdAdded by AutoGineer890 B
LICENSELicence file added by AutoGineer1.0 KiB
SKILL.md3.5 KiB
references/captions.md1.3 KiB
references/channel-motion.md3.8 KiB
references/creative.md2.6 KiB
references/editing.md1.3 KiB
references/picture-story.md3.3 KiB
references/presenter.md2.1 KiB
references/production.md4.0 KiB
references/prompts.md903 B
references/song.md2.7 KiB
references/styles.md1.9 KiB
references/talking-characters.md1.7 KiB
scripts/assemble_blocks.shScript15.1 KiB
scripts/assemble_final.shScript24.6 KiB
scripts/assemble_partial_edit.shScript10.7 KiB
scripts/assemble_slides.shScript26.1 KiB
scripts/audio_to_captions.pyScript26.2 KiB
scripts/bind_scene_frame_results.pyScript6.3 KiB
scripts/build_scene_timeline.pyScript9.3 KiB
scripts/burn_captions.shScript3.8 KiB
scripts/check_voice.shScript1.0 KiB
scripts/measure_narration_takes.pyScript5.4 KiB
scripts/presenter_composite.shScript8.4 KiB
scripts/speech_metrics.shScript3.1 KiB
scripts/tests/test_assembly.pyScript2.5 KiB
scripts/tests/test_captions.pyScript953 B
scripts/tests/test_channel.pyScript3.0 KiB
scripts/tests/test_modes.pyScript7.8 KiB
scripts/tests/test_song.pyScript1.1 KiB
scripts/tests/test_timeline.pyScript2.5 KiB
scripts/trim_static_head.shScript2.2 KiB
scripts/validate_motion_script.pyScript33.5 KiB
scripts/validate_song_prompt.pyScript8.6 KiB

references/picture-story.md

Narrated picture story

Use this path when the user explicitly requests still scenes or a picture story. Preserve the requested topic, character identity, style, aspect ratio, duration, narration language and voice. Use a persistent working directory and save every job ID and returned URL. Before paid work verify the prerequisites in references/captions.md, including an available local transcription model; a timed narration transcript is required even when captions are off.

Write one continuous narration and a sequence of numbered beats in story.json: {"beats":[{"n":1,"phrase":"..."},{"n":2,"phrase":"..."}]}. Research factual topics and retain sources. Generate one continuous seed_audio take with the selected voice pair, with no speed adjustment. Measure it. If it differs from the requested duration by more than the larger of three seconds and ten percent, rewrite the text and retry at most twice; never change the requested duration. For long narration, join a few completed same-voice paragraph takes in order without changing their speed.

Before generating scene images, finish the narration take, run the local transcript and build a frame timeline. A reusable style or character reference may be prepared earlier; scene frames must follow the completed timeline:

python3 "$SKILL_DIR/scripts/audio_to_captions.py" narration.wav --script story.json \
  --language en --model "$LOCAL_MODEL_DIR" --json words.json
python3 "$SKILL_DIR/scripts/build_scene_timeline.py" --script story.json \
  --timestamps words.json --audio-duration "$MEASURED_SECONDS" \
  --requested-duration "$REQUESTED_SECONDS" --out timeline.json

Follow the timeline order, not completion order. Create style and character references; generate a distinct numbered image for every frame in timeline.json. For each declared variation, pass the preceding image's actual job ID with --image-references, not a downloaded file or newly uploaded copy. Describe only the intended change. Store each completed result as frameNNN.png and retain the full completed job receipt including params.medias. Do not claim a variation used a reference that was not supplied to its generation. Inspect the pictures against the narration and repair only failed or incorrect frames. Never repeat one picture to satisfy the count.

Make results.json with {"results":[...]}, containing each actual completed job object with its matching output_slot added. Keep its id, status, result_url and params unchanged so the binder can verify variation references from the receipt. Bind it and assemble:

python3 "$SKILL_DIR/scripts/bind_scene_frame_results.py" --manifest timeline.json \
  --results results.json --out bound.json
bash "$SKILL_DIR/scripts/assemble_slides.sh" --audio narration.wav --timeline bound.json \
  --frames-dir frames --blocks "$FRAME_COUNT" --requested-seconds "$REQUESTED_SECONDS" --out final.mp4

The assembler verifies ordering, references, aspect, frame density, duration and decode. Stop on a failed check. If captions were requested, time them against the continuous narration with audio_to_captions.py --script story.json, then use the caption burn and final review in references/captions.md. Verify three points in the result against the script, then deliver and optionally upload the actual final file.

Requirements and compatibility

Written for: Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Claude Code frontmatter, Needs Python

Security

What our scanner noticed· Low risk

Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

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