short-video-agent-kit
Agent-first short-form AI video generation CLI and MCP server for Sora, Veo, xAI and Seedance
Install / Use
claude mcp add davidmosiah -- npx -y github:davidmosiah/short-video-agent-kitIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
AutomationSupported Platforms
Tags
Our assessment of short-video-agent-kit
short-video-agent-kit scores 78/100 on our quality scale, 2566th of 2,870 Automation skills we index.
Its MCP Server is 8.4 KB long, well organised into 14 sections with 15 code examples: a thorough specification that gives an agent plenty to work with.
It has 3 GitHub stars, so there is little community track record yet; judge it on its content.
Maintenance, license and trust
- The repository was last updated 8 days ago, so short-video-agent-kit is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 87/100, with 2 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
short-video-agent-kit compared with similar skills
All 4 of these similar skills score higher than short-video-agent-kit; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| short-video-agent-kit (this skill)by davidmosiah | 78 | 3 | 8d ago | MCP Server |
| Agent-Reachby Panniantong | 100 | 106.6k | 5d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 78.3k | today | CLAUDE.md |
| CowAgentby zhayujie | 100 | 47.7k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 90.4k | 1d ago | MCP Server |
Frequently asked questions
- How do I install short-video-agent-kit?
- Run
claude mcp add davidmosiah -- npx -y github:davidmosiah/short-video-agent-kit. The install tabs above show the steps for each supported agent. - Which AI agents does short-video-agent-kit work with?
- It is written for Claude Code, Claude Desktop, Gemini CLI and OpenAI Codex, as a MCP Server file. Other agents that read the same format can often use it too.
- Is short-video-agent-kit safe to use?
- It is MIT-licensed and scores 87/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is short-video-agent-kit still maintained?
- The repository was last updated 8 days ago, so short-video-agent-kit is actively maintained.
Skill content
View source on GitHub⭐ If this agent-first tool helps your workflow, please star the repo. Stars make this tooling easier for other builders to discover and help Delx keep shipping open infrastructure.<br> 🧱 Part of the Delx agent stack — 15 open-source MCP servers across body, reach and coordination.
HTTP (v2 stateless)
Default is stdio. Optional Streamable HTTP — no session id, JSON responses, loopback only:
npx -y -p short-video-agent-kit short-video-mcp --http
# GET http://127.0.0.1:3033/health
# POST http://127.0.0.1:3033/mcp (sessionless)
Env: SHORT_VIDEO_MCP_HOST, SHORT_VIDEO_MCP_PORT, SHORT_VIDEO_MCP_TRANSPORT=http.
Provider-neutral short-form AI video toolkit for agents. It gives Codex, Claude, Cursor, Hermes, OpenClaw and other MCP clients one interface for building dry-run payloads and, when explicitly enabled, generating vertical video through Sora/OpenAI, Gemini Veo, xAI/Grok and Seedance/PiAPI-style providers.
Use it when an agent needs one safe interface for prompt-to-video payload validation and optional paid generation across multiple providers.
Why It Is Agent-First
Video generation can be expensive and prompt-sensitive. This package makes agents start with safe steps:
- inspect provider readiness
- return privacy boundaries
- build payloads without spending credits
- require
--liveorSHORT_VIDEO_DRY_RUN=falsebefore provider calls - keep prompts and outputs in local user-controlled paths
Install
npm install -g short-video-agent-kit
Or run directly:
npm exec --yes --package=short-video-agent-kit -- short-video-agent-kit doctor
Quickstart
No API key needed to try it — generation is dry-run by default, so the kit returns the exact provider-neutral plan it would send without spending a credit.
Build a Sora plan for an 8-second vertical teaser:
short-video-agent-kit generate \
--provider openai_sora \
--prompt "Vertical 8-second product teaser for a minimalist water bottle, soft studio light, slow dolly-in" \
--output ./output/teaser.mp4
Real output (no provider call, no credits spent):
{
"ok": true,
"dry_run": true,
"next_step": "Pass --live or set SHORT_VIDEO_DRY_RUN=false to call the provider API.",
"provider": "openai_sora",
"endpoint": "POST /v1/videos",
"payload": {
"model": "sora-2",
"prompt": "Vertical 8-second product teaser for a minimalist water bottle, soft studio light, slow dolly-in",
"seconds": "8",
"size": "720x1280"
}
}
Same prompt, different provider — the plan re-targets the endpoint and parameter shape for you. payload returns just the plan (no dry_run wrapper):
short-video-agent-kit payload --provider gemini_veo --prompt "Same teaser, 9:16, cinematic"
{
"provider": "gemini_veo",
"endpoint": "POST /models/{model}:predictLongRunning",
"payload": {
"instances": [
{
"prompt": "Same teaser, 9:16, cinematic"
}
],
"parameters": {
"aspectRatio": "9:16",
"durationSeconds": 8
}
}
}
Check which providers are wired up (keys are detected, never printed):
short-video-agent-kit doctor
{
"ok": false,
"dry_run": true,
"providers": {
"openai_sora": { "configured": false, "env_keys": ["OPENAI_API_KEY"], "models": ["sora-2"] },
"gemini_veo": { "configured": false, "env_keys": ["GEMINI_API_KEY", "GOOGLE_API_KEY"], "models": ["veo-3.1-fast-generate-preview"] },
"xai_grok": { "configured": false, "env_keys": ["XAI_API_KEY"], "models": ["grok-imagine-video", "grok-imagine-image"] },
"seedance_piapi": { "configured": false, "env_keys": ["PIAPI_KEY", "SEEDANCE_API_KEY"], "models": ["seedance-2-fast-preview"] }
},
"output_dir": "./output",
"next_steps": [
"Set one provider key: OPENAI_API_KEY, GEMINI_API_KEY, XAI_API_KEY or PIAPI_KEY."
]
}
When you are ready to actually render, set a provider key and re-run generate with --live (or SHORT_VIDEO_DRY_RUN=false).
CLI
short-video-agent-kit manifest --client codex
short-video-agent-kit doctor
short-video-agent-kit privacy-audit
short-video-agent-kit payload --provider gemini_veo --prompt-file prompt.txt
short-video-agent-kit generate --provider openai_sora --prompt "Vertical product teaser" --output ./output/teaser.mp4
short-video-agent-kit generate --provider openai_sora --prompt-file prompt.txt --output ./output/teaser.mp4 --live
Supported providers:
openai_soragemini_veoxai_grokseedance_piapi
MCP
short-video-mcp
HTTP transport:
SHORT_VIDEO_MCP_TRANSPORT=http short-video-mcp
Hermes-style config:
mcp_servers:
short_video:
command: npx
args: ["-y", "short-video-agent-kit"]
sampling:
enabled: false
Recommended first calls:
short_video_connection_statusshort_video_privacy_auditshort_video_build_payloadshort_video_generate
Agent Surfaces
| Tool | Purpose |
|---|---|
| short_video_agent_manifest | Install/runtime guidance for Codex, Claude, Cursor, Hermes and OpenClaw |
| short_video_connection_status | Provider readiness without API keys |
| short_video_privacy_audit | Prompt, output and reference-asset boundaries |
| short_video_build_payload | Provider-specific payload without paid generation |
| short_video_generate | Dry-run by default, live only when explicitly requested |
Copy-Paste Agent Prompt
Use short-video-agent-kit. First call short_video_connection_status and short_video_privacy_audit.
Build the payload before generation. Only set live=true if I explicitly confirm a paid provider call.
Configuration
Copy .env.example to .env and fill only the provider keys you plan to use. .env, output/ and .agent-data/ are ignored by Git.
Safety Model
- Dry-run is the default.
- API keys are never returned by tools.
- Paid generation requires
--live, MCPlive=true, orSHORT_VIDEO_DRY_RUN=false. - Reference images must be user-owned or licensed.
- Outputs are written to local paths controlled by the user.
Development
npm install
npm test
npm run check
📧 Contact & Support
- 📨 support@delx.ai — general questions, integration help, partnerships
- 🐛 Bug reports / feature requests — GitHub Issues
- 🐦 Updates — @delx369 on X
- 🌐 Site — wellness.delx.ai
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
