workspace-audit
Skill1 starsChanged 59 days agoMITView on GitHub
Audit a user-authorized local workspace to identify file types, installed commands, project dependencies, and possible AI workflow needs without reading sensitive file contents or changing the system.
3 other projects use a version of this.
Adopted by
Most recently taken up by
- scribemail/claude-plugintheir own version, first seen Oct 13, 2026
- gerivdb/SKILLStheir own version, first seen Aug 7, 2026
- dylanmoo/workos-plugintheir own version, first seen May 28, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- gerivdb/SKILLS★ 1same name
- dylanmoo/workos-plugin★ 0same name
- scribemail/claude-plugin★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository3 other files it gives its agents.
- ai-workflow-advisorSkill
- setup-guideSkill
- tool-recommendationSkill
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈6
Distinctive · estimate
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
- Distinctive: line 8
Use this skill when a user asks what AI tools, CLI programs, MCP servers, or Agents fit their current work environment.
- Distinctive: line 13
Prefer cheap, read-only checks: current working directory, bounded file-name inventory, package manifests, Git metadata, and version/help …
- Distinctive: line 14
Never print secrets. Do not read API keys, cookies, tokens, OAuth files, private message contents, or full document bodies.
- Distinctive: line 16
If access is unavailable, say so and ask the user about their workflow instead of pretending to have historical usage data.
- Distinctive: line 32
follow-up questions that would materially change recommendations.
- Distinctive: line 34
Do not install, authenticate, modify PATH, edit config files, or call third-party services in this mode.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Workspace Audit
Use this skill when a user asks what AI tools, CLI programs, MCP servers, or Agents fit their current work environment.
Operating rules
- Explain the exact scan scope and ask for authorization before inspecting local paths or command availability.
- Prefer cheap, read-only checks: current working directory, bounded file-name inventory, package manifests, Git metadata, and version/help commands for explicitly requested binaries.
- Never print secrets. Do not read API keys, cookies, tokens, OAuth files, private message contents, or full document bodies. Redact environment variables and configuration values.
- Distinguish facts from inferences. A
.xlsxfile proves that a spreadsheet exists; it does not prove the user's recurring job. - If access is unavailable, say so and ask the user about their workflow instead of pretending to have historical usage data.
Scan scope
Unless the user sets a different boundary, inspect only the current workspace at a bounded depth and a small allowlist of manifest names. You may check whether a .git directory or remote exists, but do not print credentials or remote URLs containing tokens. Check command existence and versions only for a short relevant list; do not enumerate every installed program.
Output
Return a compact audit with:
- scan scope and timestamp;
- observed facts, such as file-extension counts, manifests, Git presence, and command/version evidence;
- user-provided context, kept separate from machine observations;
- likely tasks, clearly marked as hypotheses;
- capabilities not observed in this scope, never phrased as proof of absence;
- files, secrets, or permissions intentionally not inspected;
- follow-up questions that would materially change recommendations.
Do not install, authenticate, modify PATH, edit config files, or call third-party services in this mode.
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
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- It didn't
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.

