agentleFS
Gaurav890Gaurav890/everything-agentic-engineering/.claude/skills/token-audit/SKILL.md

token-audit

Skill1 starsChanged 2 months agoMITView on GitHub

Audit implementation and token sources for raw visual values, arbitrary utilities, duplicate semantics, invalid aliases, missing theme coverage, unused tokens, and undocumented exceptions. Use after tokenized UI changes or before system-scale release.

14 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 12 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Do not force geometric/canvas values into reusable tokens when they are truly one-off.

    line 19

“Common” is measured: kept by at least 6 in 10 of the 12 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Token audit

Inspect token JSON and product code for:

  • raw colors, spacing, radii, shadows, borders, z-index, and durations;
  • direct primitive consumption where semantic intent exists;
  • duplicate or ambiguous semantics;
  • invalid aliases and missing light/dark mappings;
  • component tokens that should be semantic—or needless one-off tokens;
  • unused/dead tokens and inconsistent naming;
  • undocumented TOKEN_EXCEPTION values.

Classify findings as defect, migration, system decision, or valid exception. Do not force geometric/canvas values into reusable tokens when they are truly one-off. Report file, value, recommended token/action, and risk.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.