Stitch Loop

Name: Stitch Loop
Author: google-labs-code

Official

google-labs-code·vlatest·frontend·Apache-2.0

Stitch Loop automatically creates looping animations from sequential images or videos, streamlining your frontend development workflow and saving time on complex animation builds.

Repository

Install on your platform

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

Run this in your project

terminal

$ npx skills add google-labs-code/stitch-skills --skill stitch-loop -a claude-code

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

google-labs-code

Matched to the vendor's own GitHub organisation.

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 28 Sept 2026

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 google-labs-code/stitch-skills at 0337446dad.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: 0337446dad
Manifest: plugins/stitch-utilities/skills/stitch-loop/SKILL.md
Scanned: 6 files, 564 lines

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

The stitch-loop skill enables AI agents to perform iterative tasks in a structured and repeatable manner, such as processing sequences of data or executing commands in a loop. It is particularly useful for automating repetitive frontend workflows like rendering dynamic UI components, handling form inputs, or managing state transitions.

When to use it

Processing a list of items dynamically on a webpage, such as rendering user comments or product listings.
Managing animations or transitions that require repeated execution over time.
Handling multi-step forms where each step must be validated and processed sequentially.

Key capabilities

Iterative processing of data sequences
Dynamic UI component rendering
State management for repetitive tasks
Integration with frontend event loops

Example prompts

"Render a list of user comments dynamically using a loop."
"Animate the transition between multiple slides in a carousel."
"Validate and process each step of a multi-step form sequentially."

Tips & gotchas

Ensure that your loop has a clear exit condition to avoid infinite loops. Also, be mindful of performance when rendering large datasets iteratively—use virtualization or pagination if needed.