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Mediapipe Pose Detection

Checked
feniix·vlatest·other·MIT

Detects human body keypoints from images/video using Mediapipe, enabling applications like motion tracking and gesture recognition.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add kinemotioninc/kinemotion --skill mediapipe-pose-detection -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

feniix

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned feniix/kinemotion at bacca75476.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
bacca75476
Manifest
.claude/skills/mediapipe-pose-detection/SKILL.md
Scanned
2 files, 836 lines
Copies in repo
2 — all of them scanned
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill utilizes MediaPipe to detect human poses in images and videos. It identifies 33 key body landmarks, providing x and y coordinates for each point. The output can be used for various applications like fitness tracking, animation, or creating interactive experiences based on user movement.

When to use it

  • Fitness Tracking: Analyze a video of someone exercising and provide feedback on their form.
  • Animation Control: Use the pose data to control an animated character in real-time based on a person's movements.
  • Interactive Games: Create games where player actions are determined by their body poses.
  • Virtual Try-On: Overlay clothing or accessories onto a user’s image/video based on detected pose landmarks.

Key capabilities

  • 33 Landmark Pose Detection
  • Real-time processing of images and videos
  • Provides x, y coordinates for each landmark
  • Utilizes MediaPipe library

Example prompts

  • "Detect the pose in this video."
  • "Give me the coordinates of the left elbow in this image."
  • "Analyze my squat form from this video clip."

Tips & gotchas

  • Requires a clear view of the person being analyzed for accurate landmark detection.
  • Performance may vary depending on processing power and video resolution.