What does this skill do, and when should you use it?
Vox Director is an agent skill that turns a one-line topic into a finished Vox-style torn-paper collage explainer or ad video, end to end on the Atlas Cloud API plus local ffmpeg. The pipeline is driven by one beats. per project: pick a narrative arc, run a style bake-off, generate collage posters, animate them, add narration and music, and assemble with ffmpeg. Beyond B-roll (topic to video), it supports A-roll (re-styling an existing talking-head video into the collage look) and C-roll (anchoring a single photo of a person or product as a photographic sticker inside the collage). Two human decision gates keep you in control: approving the beat map and picking the visual style by eye.
Each stage is one script: 1) writes beats. using narrative arcs from beat-layer.md (timeline, pas, how_it_works, etc.) and shows it for approval; 2) style_bakeoff.py renders the same beat in 3–4 theme presets for a visual pick; 3) keyframes.py calls google/nano-banana-2/text-to-image to produce a collage poster with baked-in headline per shot (about $0.08 each, cheap to re-roll); 4) clips.py animates each poster with gemini-omni-flash image-to-video, applying per-shot camera moves and rich element_motion (real people/brands are routed to kling-video-o3-pro); 5) audio.py generates narration via xai/tts-v1 (optional seed-audio voice cloning) and instrumental BGM via minimax/music-2.6; 6) assemble.py uses ffmpeg to concat, duck music under VO, and burn captions plus a watermark into final.mp4. All calls go through scripts/provider.py, which auto-resubmits stalled or failed jobs.
- A creator who already has a talking-head video and wants it re-styled into the collage look while keeping the real face, lip-sync and gestures (A-roll)
- A brand or indie marketer turning one product photo into a collage ad, with the label typography kept pixel-faithful (C-roll)
- A knowledge/explainer channel needing dense 9:16 vertical social videos with a cut every 4–6 seconds
- Users not on Atlas Cloud who want a manual prompt-pack: beat map + per-beat image prompts + motion prompts + narration script to paste into any generator
- History/tech/food topics needing multilingual narration (xai/tts offers 5 multilingual plus ~66 native voices)
- When you need dramatic piece-by-piece assembly motion or filter-free animation of real people via the local keyframe engine
How do you install this skill?
- Core function depends entirely on the paid Atlas Cloud API; mainland-China reachability and account availability are undeclared — verify before use.
- User photos, talking-head video and voice-clone samples are uploaded to a third-party cloud; biometric/voice data implications should be assessed by privacy-sensitive users.
- Default output carries a 'Made with Atlas Cloud' watermark (promotional); confirm removal for commercial use.
- Model IDs drift; docs admit defaults work 'today' only, so manual model reselection may be needed at runtime.
- Voice cloning and the 'zero content-filter' local engine path for animating real people carry misuse risk (deepfake/impersonation); users must ensure compliance themselves.
- Shell / CLI
- Network access
- Local filesystem
Atlas Cloud API keyffmpeg + ffprobePython 3 with Pillow
Prerequisites: any coding agent that can follow a workflow and run scripts (Claude Code, Codex, etc.), an Atlas Cloud API key, ffmpeg + ffprobe, and Python 3 with Pillow. Option A: git clone https://github.com/Alisa0808/vox-director.git ~/.claude/skills/vox-director. Option B: download vox-director.skill and install it via your Claude skills UI. Then export your key: export ATLASCLOUD_API_KEY="sk-..." (get one at atlascloud.ai/console/api-keys). Non-Claude agents read AGENTS.md → SKILL.md.
git clone --depth 1 https://github.com/Alisa0808/vox-director.git ~/.claude/skills/vox-directorGenerated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Make me a Vox-style collage video introducing Mexican street food — English, 16:9, 15 seconds.
With the skill installed, just ask your coding agent, e.g.: "Make me a Vox-style collage video introducing Mexican street food — English, 16:9, 15 seconds." The agent drafts a beat map for your approval (gate 1), runs a style bake-off for you to pick by eye (gate 2), then generates keyframes → motion → voice → music and outputs out/<project>/final.mp4. To verify the result, extract frames to jpg with ffmpeg — you cannot read an mp4 directly.
What are this skill's strengths and limitations?
- Fully automated end to end — one topic in, a finished mp4 out — while keeping two human checkpoints (beat map, style pick)
- One script per stage driven by a single beats.; after approval, re-runs only generate what's missing (keyframes.py skips shots that already have a keyframe_url)
- Exceptionally thorough docs: prompt structures, 14 narrative arcs, a voice roster, and a catalog of every API/ffmpeg gotcha
- Pluggable media backend (provider.py) — switching backends requires no changes to stage scripts; stalled jobs auto-resubmit
- Cost-aware design: re-roll weak keyframes cheaply ($0.08) instead of paying to animate a bad image
- Hard dependency on Atlas Cloud — the only backend today; without its API key you're limited to the manual prompt-pack mode
- SKILL.md gives no overall cost estimate for a full film and mentions no automated test suite
- Content restrictions exist: Omni and Seedance refuse real people and brand logos (use Kling instead); in A-roll you must never ask the model to redraw the face
- Model IDs drift, so the live model list must be fetched before each run
- Some behaviors are known traps (e.g. seed-audio cloning requires the pinned-speaker template) and require reading the gotchas doc to avoid
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Vox Director: One-Topic Vox-Style Collage Videos this page | 56 · Use with care | ★ 2.7k | 1mo ago | MIT |
| 9Router Video Generation (xAI Grok Imagine) | 49 · Use with care | ★ 32k | 9d ago | MIT |
| Video ShotCraft: Cinematic Product Video Skill | 61 · Recommended | ★ 17k | 8d ago | Apache-2.0 |
| Narrator AI Skill | 56 · Use with care | ★ 3.2k | 4mo ago | MIT |
| Nano Banana Pro Prompts Recommender | 50 · Use with care | ★ 1.9k | today | — |
The README explicitly positions the look and workflow as inspired by Vox's explainer visual language and the collage-ad workflows of Stav Zilber, rom1trs and Higgsfield — the skill automates those workflows rather than competing with a named alternative. The source lists no other comparable tools.
How did FollowSkills review this skill?
SKILL.md stops when ATLASCLOUD_API_KEY is missing, enforces two human approval gates (beat map, aspect approximation), minimal local deps (ffmpeg/Pillow), no credential exfiltration signs; but it uploads user photos, talking-head video and voice-clone samples to the third-party Atlas Cloud API, and the default watermark is 'Made with Atlas Cloud' (promotional), with incomplete data-flow disclosure — deducted.
Extensive documented gotchas, auto-resubmit, model fallbacks, resumable generation (skips existing keyframe_url) show serious engineering; but no committed tests or CI were seen, key paths are unverifiable statically, and error handling exists only as prose — capped at 10 for static review.
Triggers, three input modalities (B/A/C-roll) and boundaries (Kling for real people, content-filter workarounds) are very clearly written, bilingual; but the core function depends entirely on the single overseas Atlas Cloud API (paid, quota-bound) with no mainland-China reachability statement — deducted.
Clear layered architecture (SKILL.md + references), MIT license, package. version, bilingual docs; but no changelog, maintenance is by one individual, update path unstated, and hidden assumptions (Atlas account, drifting model IDs) are delegated to external docs — deducted.
If executed as documented, it produces a finished video end to end with clear marginal value (manual collage video production is far costlier); but no independent reproduction statically, and cost/quality evidence is author-supplied showcase only — capped at 6.
Example beats., showcase videos and concrete recorded failures (e.g. 'PARFUM'→'PAREUM') exceed pure marketing; but everything is author-attested, with no third-party tests, CI, or independently checkable model verification — capped at 4.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →