Clipcannon

ClipCannon -- AI Video Editor via MCP

Turns Claude into a professional video editor. Ingest video, run a 22-stage AI analysis DAG, then use 51 MCP tools across 12 categories to find moments, create edits, render platform-ready clips, generate music, clone voices, produce lip-synced talking-head videos, and converse via a real-time voice agent. Everything runs locally on GPU with a tamper-evident SHA-256 provenance chain.

Full catalog (51 tools, 14 ML models, 5 embedding spaces, credit costs, architecture diagram, Voice Agent lifecycle, integrations) lives in references/tools-and-models.md. Read it when you need an exact tool name, a model's VRAM budget, or the credit cost of an operation.

When to Use This Skill

• Video editing: "edit this video", "cut the boring parts", "create a highlight reel"
• Content discovery: "find the most emotional moments", "find where they talk about X"
• Platform rendering: "render for TikTok", "create Instagram Reels version"
• Voice: "clone this speaker's voice", "generate narration", "lip sync"
• Audio: "add background music", "generate sound effects", "compose a score"
• Analysis: "transcribe this video", "who are the speakers?", "scene breakdown"
• Text-to-video: "generate a video from this script" (end-to-end voice + lip-sync)
• Voice Agent: "talk to Jarvis", real-time conversational AI with wake-word activation

When Not to Use

• For simple video format conversion -- use ffmpeg-processing
• For AI image generation -- use comfyui or art
• For agentic video production from scratch -- use open-montage
• For meeting transcription -- use echoloop
• For audio-only processing -- use ffmpeg-processing

Quick Path

Not baked into this image. command -v clipcannon returns nothing in a stock container — install it (below) or run the separate Docker stack before any clipcannon_* MCP tool call will succeed.

# Install (requires Python 3.12+, CUDA GPU, 8+ GB VRAM min, 24+ GB recommended)
pip install clipcannon
clipcannon serve                       # start the MCP server

# Docker (Dashboard :3200, License server :3100) -- config/ lives in the upstream
# repo, not in this skill directory; clone it first (see "From Source" below)
cd jlma-clipcannon/config && docker compose up -d

Typical MCP flow once the server is up:

1. clipcannon_project_create -> clipcannon_ingest (runs the 22-stage pipeline; ~10 credits)
2. clipcannon_find_best_moments / clipcannon_search_content to locate material
3. clipcannon_create_edit (declarative EDL) -> clipcannon_preview_clip (free, 540p)
4. clipcannon_render with a platform profile (TikTok, Reels, Shorts, YouTube, YouTube 4K, Facebook, LinkedIn)

Voice / avatar extras: clipcannon_speak (cloned voice), clipcannon_lip_sync, clipcannon_generate_music, clipcannon_generate_video (text -> voice -> lip-sync).

Voice Agent ("Jarvis") -- human-operated, outside agent scope

Real-time, all-local conversational AI with "Hey Jarvis" wake word. It pauses other GPU workers on activation and resumes them on deactivation to share a single GPU's VRAM. This is a long-running, stateful, turn-by-turn audio loop a human runs and talks to directly -- neither Claude Code nor Codex has persistent audio I/O to drive it, so it is out of scope for an agent to invoke. The MCP-tool-driven editing capabilities elsewhere in this skill remain agent-usable; this section is not.

python -m voiceagent talk --voice boris        # Pipecat + Ollama, all local
python -m voiceagent serve --port 8765         # WebSocket server for remote clients

Lifecycle and component models are in references/tools-and-models.md.

From Source

cd /tmp && git clone https://github.com/JLMA-Agentic-Ai/jlma-clipcannon.git
cd jlma-clipcannon && pip install -e ".[ml]" ".[phase2]"

Set-up options and defaults for CLIPCANNON_DATA_DIR, CLIPCANNON_GPU_DEVICE, and CLIPCANNON_NVENC are documented in the reference file.

Attribution

ClipCannon by Chris Royse. BSL 1.1 License. Repo: https://github.com/JLMA-Agentic-Ai/jlma-clipcannon