Publisher: Anthropic · Editorial profile by AI Tools Ratings · Review method
Not runtime tested; source-document review only. Referenced scripts were not executed or comprehensively audited. No security certification.
What task is this skill designed for?
Build an original flow-field viewer with a saved seed and density controls.
What input and output should you expect?
Input: A visual brief, permitted palette, canvas dimensions, seed and required controls.
Output: A design rationale and HTML/JavaScript viewer; expected, not independently tested.
What does it need to run?
A compatible Claude skill environment, p5.js, a browser preview and the referenced viewer template. Inspect external library loading before use.
Publisher README describes Claude Skills use; independent Codex or Cursor runtime compatibility is not established.
How should you approach installation?
- Inspect the pinned source, license and task-specific reference files before installing anything.
- Follow the publisher README guidance for your actual client; no installation command or cross-client compatibility was independently tested here.
- Try the illustrative prompt with synthetic input in a separate folder, inspect the output against the checklist and approve external actions separately.
Installation guide: discovery, dependencies and runtime checks
What files, accounts or data can it access?
Reads the brief and writes executable browser code. A viewer may load libraries over the network; inspect the generated HTML before sharing it with sensitive data.
What does the skill cost?
The inspected source license is Apache-2.0. Agent subscriptions, model usage, dependencies and external services have separate costs and terms. No paid runtime was tested.
What are its limits and alternatives?
A seed reproduces a composition only when code and parameters are unchanged. Large particle counts can hurt performance; a preview does not establish mobile usability or originality.
Choose Canvas Design for a static poster. Choose this workflow when adjustable behavior and repeatable variations are required.
What could you ask in a small trial?
Try this with a small sample:
Build an original flow-field viewer using seed 17, density controls and two approved palettes. Record all parameters and explain how to reproduce the composition.
How would you verify the result?
- Save code, seed and parameter settings together.
- Reload the same configuration and compare composition.
- Review narrow-screen performance and reduced-motion behavior.
Use a baseline and record acceptance evidence before treating a successful task as a reliable workflow.
Which source and license were checked?
Read the reviewed SKILL.md at its pinned commit · Complete reviewed folder
Repository and current publisher guidance
Checked:
Apache-2.0 — identified in skills/algorithmic-art/LICENSE.txt at the reviewed commit. Dependencies and third-party assets may have separate terms. License evidence
Version details and review scope
Source: anthropics/skills/skills/algorithmic-art
Reviewed commit: dbd4588f9e1033efb41dad4bef2f7947c8993d44
SKILL.md SHA-256: 3bc4092c09804853186524c826bc0621b940bb6122c05b84496dff95388e6eef
Source-document review. Source text and stated dependencies were inspected for this profile. Referenced code was not comprehensively audited, no runtime test was performed, and source files are not mirrored here.