
Controlnet Pose
Transfer motion from a reference video onto a target character (Kling motion control),Generate images conditioned on OpenPose, DWPose, depth, or canny references,Pick video vs still and photoreal vs s
Install
npx skills add https://github.com/agentspace-so/runcomfy-agent-skills --skill controlnet-poseWhat is this skill?
- Multi-route router: Kling 2.6 Motion Control, Wan 2.2 Animate, Z-Image Turbo ControlNet LoRA
- Supports OpenPose, DWPose, depth, canny, and motion-transfer intents
- Distinguishes video motion control from still-image pose conditioning
Adoption & trust: 151k installs on skills.sh; 15 GitHub stars; 2/3 security scanners passed (skills.sh audits).
Recommended Skills
Journey fit
Pose and ControlNet generation is wired for agents building media pipelines through RunComfy during product development. Routing across Kling, Wan Animate, and Z-Image ControlNet via runcomfy CLI is agent-facing tooling, with optional ComfyUI workflow pointers.
Common Questions / FAQ
Is Controlnet Pose safe to install?
skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.
SKILL.md
READMESKILL.md - Controlnet Pose
# ControlNet & Pose Condition image or video generation on a pose, skeleton, or motion reference. This skill routes across the pose-driven Model API endpoints reachable today and points the agent at ComfyUI workflows for richer ControlNet rigs. [runcomfy.com](https://www.runcomfy.com/?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose) · [Kling motion control](https://www.runcomfy.com/models/kling/kling-2-6/motion-control-pro?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose) · [CLI docs](https://docs.runcomfy.com/cli/introduction?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose) ## Powered by the RunComfy CLI ```bash # 1. Install (see runcomfy-cli skill for details) npm i -g @runcomfy/cli # or: npx -y @runcomfy/cli --version # 2. Sign in runcomfy login # or in CI: export RUNCOMFY_TOKEN=<token> # 3. Pose-conditioned generate runcomfy run <vendor>/<model> \ --input '{"reference_video_url": "...", "character_image_url": "..."}' \ --output-dir ./out ``` CLI deep dive: [`runcomfy-cli`](https://www.skills.sh/agentspace-so/runcomfy-agent-skills/runcomfy-cli) skill. --- ## Pick the right model Routes split by video pose-transfer vs image pose-conditioned generation. ### Video — motion / pose transfer **Kling 2-6 Motion Control Pro** — `kling/kling-2-6/motion-control-pro` *(default for video pose transfer)* > Takes a reference performance video + a target character image, produces video of the target performing the reference motion / pose. > Pick for: transferring a source video's motion / blocking onto a new character; dance choreography re-shot; sports motion onto a stylized character. > Avoid for: still-image pose conditioning — use Z-Image ControlNet LoRA. **Kling 2-6 Motion Control Standard** — [`kling/kling-2-6/motion-control-standard`](https://www.runcomfy.com/models/kling/kling-2-6/motion-control-standard?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose) > Cheaper Kling Motion Control tier. > Pick for: drafts, iteration on motion-control compositions. > Avoid for: final delivery — use Pro. **Wan 2-2 Animate (video-to-video)** — [`community/wan-2-2-animate/video-to-video`](https://www.runcomfy.com/models/community/wan-2-2-animate/video-to-video?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose) > Community-published variant on Wan 2-2. Audio-driven character animation that also accepts pose-style conditioning. > Pick for: stylized character animation, mascot work. > Avoid for: photoreal subjects — use Kling Motion Control. ### Image — pose-conditioned generation **Z-Image Turbo ControlNet LoRA** — [`tongyi-mai/z-image/turbo/controlnet/lora`](https://www.runcomfy.com/models/tongyi-mai/z-image/turbo/controlnet/lora?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose) > Z-Image Turbo with a ControlNet LoRA — feed a control image (pose skeleton, depth map, canny) and a prompt, get a generation conditioned on that control. > Pick for: pose-locked image