generating-video-clips

generating-video-clips is a skill for Claude Code from shaharsha/claude-skills. It costs 118 tokens per session (2,422 once invoked), scanned A, original, MIT.

A workflow for turning still images into short animated video clips, then matching a character's mouth to recorded dialogue. It covers choosing supported video services and checking results from extracted frames.

In plain words
What is it for?
Use it for image-to-video clips, animated character dialogue, model or provider selection, and troubleshooting API access.
Why use it?
It gives a repeatable way to turn artwork into motion and handle lip-sync without relying on video playback inside the workflow.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: positional $N argument.

Part of the video-production plugin — 2 skills shipped together , and of shaharsha-skills

Good fit Use it for image-to-video clips, animated character dialogue, model or provider selection, and troubleshooting API access.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/shaharsha/claude-skills/generating-video-clips
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add shaharsha/claude-skills --skill generating-video-clips
Clone the repo
git clone --depth 1 https://github.com/shaharsha/claude-skills

Made for: Claude Code.

Or install video-production, the plugin that ships this one along with the rest of its 2 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for generating-video-clips

README.md
[![agentmods](https://agentmods.dev/badge/skills/shaharsha/claude-skills/generating-video-clips/github.svg)](https://agentmods.dev/skills/shaharsha/claude-skills/generating-video-clips)
Your own site
<a href="https://agentmods.dev/skills/shaharsha/claude-skills/generating-video-clips"><img src="https://agentmods.dev/badge/skills/shaharsha/claude-skills/generating-video-clips/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for generating-video-clips

Your own site · 80×15
<a href="https://agentmods.dev/skills/shaharsha/claude-skills/generating-video-clips"><img src="https://agentmods.dev/badge/skills/shaharsha/claude-skills/generating-video-clips.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,422 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00118 $0.02422
Opus 5.5 $0.00047 $0.00969
Sonnet 5.5 $0.00024 $0.00484
Haiku 4.5 $0.00012 $0.00242

Measured 15d ago against content hash 3ff3c120126b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

generating-video-clips scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 15d ago.

The scan reads SKILL.md. This mod also ships 5 executable files (scripts/lipsync_fal.py, scripts/lipsync_parts.py, scripts/omni_generate.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/generating-video-clips/SKILL.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Generating video clips (still to clip, then lip-sync)

Overview

A finished still becomes a 4-9 s clip with an image-to-video model. Then, if a character talks on screen, a lip-sync model re-times their mouth to your own dialogue. You cannot watch the result: judge every clip from extracted frames (scripts/take_sheet.py, and the viewing-videos skill), and let the human have the final say on motion.

Requirements: Python 3 (stdlib only) and ffmpeg/ffprobe. Keys go in env vars: GEMINI_API_KEY (a Google AI Studio key; an image-scoped one works, and GEMINI_IMAGE_API_KEY is read as a fallback) for Omni, and FAL_KEY (the full key exactly as fal shows it) for lip-sync. Never print them.

Choose the model

Model Reached through Use for Notes
Gemini Omni Flash 1.1 (gemini-omni-1.1-flash) Google Interactions API, AI Studio key Default for illustrated, anime and cartoon stills; also worked on a photoreal still (one test) keeps art style and faces; any input aspect (a 2:3 still came back as clean 9:16, so no need to outpaint or crop first); 720p 9:16 or 16:9; synchronous, ~25-35 s per clip; adds its own audio (mute it)
Veo 3.1 / Veo 3.1 Fast Gemini API, image as the start frame photoreal stills not used in our runs; check current docs first
Seedance 2.5 ElevenLabs Flows, or ByteDance directly alternative look needed account approval when tried
Any model inside ElevenLabs Flows ElevenLabs API one key for everything API access needs the Pro plan (402 paid_plan_required) and the key's image_video_generation permission (401 without it); Omni there is app-only

For lip-sync, Sync 3 on fal (fal-ai/sync-lipsync/v3) is the default. It works video-to-video, so it keeps the clip's motion and re-times only the mouth. Comparison and prices: references/providers.md.

Animate a still

python3 <this-skill-dir>/scripts/omni_generate.py jobs.json clips/ [--takes 3] [--only S05]
{"base": "Bring this still to life as it is: same art style, palette, light and layout, and every character keeps the face, hair, build and clothes they have in it. Natural, fluid movement. Nothing written on screen, no speech bubbles, nobody new, one continuous shot. ",
 "jobs": {"S05": {"image": "panels/p05.png",
                  "prompt": "[0-3s] The woman in the red jacket lifts the phone and talks, excited. [3-6s] The friend in the green hoodie leans back on the sofa and laughs. Slow camera push-in."}}}

How to write the prompt (full guide with examples in references/gemini-omni.md):

  • A per-second timeline, one beat per bracket. The clip runs about as long as the timeline.
  • Name people by what they wear, never by name: the model doesn't know who "Dana" is.
  • Say "talks" for anyone who will be lip-synced, so the mouth already moves.
  • Pin what must not change: "the camera stays still", "one single window on the left wall", "exactly ONE hooded figure", "her hands stay flat on the table". Each pin in references/failure-modes.md came from a real failure.

Read the full file on GitHub · 112 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 15d ago First seen · 112 lines · 118 tokens per session scan A 3ff3c120126b

Subscribe to this mod's changes

generating-video-clips is a skill published in the GitHub repository shaharsha/claude-skills (5 stars, last pushed 7d ago), licensed MIT. It adds 118 tokens to every session and 2,422 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-04.

Related

Other skills, from other repositories

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…

vercel/next.js · 123 tokens