agentleFS
Sign inStart free
p10ns11yp10ns11y/skills/tauri-agentic/SKILL.md

tauri-agentic

Skill2 starsChanged 40 days agoMITView on GitHub

Patterns for building agentic, MCP-exposed, self-guarded desktop apps in Tauri (Rust backend + React/TS frontend). Expose finder functions as MCP tools, implement guards/pauses in UI and backend, command palette as agent interface, secure key storage, integration with X resources and CV guard. Use when implementing the Tauri shell, reactor UI, or MCP server. Fission for Rust/TS code; fusion for desktop as agent body.

The file

We split this skill into the rules it gives an agent

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

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    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
  1. Distinctive:

    All platform/LLM calls go through reactor core.

    line 24

≈ “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.

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

Tauri Agentic — Desktop as Agentic Platform

Core Mission: Turn a Tauri app into a first-class agentic environment where a reactor runs with autonomy, but self-guards and pauses are exposed in the UI and via MCP.

Key Patterns

  1. MCP Server in Tauri: Rust MCP (stdio or local HTTP). Tools: search, analyze, prep, promote, get_state. Guards on every call; ask_user for pauses.
  2. UI as Agent Interface: Command palette with guard status; dashboard for spend/rate limits; diff viewer for CV promote (cv-promote-guard).
  3. Backend Guards in Rust: Central reactor wrapping platform client + LLM calls; structured decisions; secure keyring/file storage for API tokens.
  4. React Frontend: react-client-expert — minimal state, deliberate invoke calls, XState or reducer for reactor FSM.
  5. Integration: Load X skill.md at startup (x-agent-resources); optional xurl shell.

Guardrails

  • All platform/LLM calls go through reactor core.
  • MCP tools read-heavy by default; writes guarded.
  • Keys never in frontend.
  • Tauri capabilities: fs/dialog limited to app data + approved external CV path.

Related

finder-reactor, x-agent-resources, cv-promote-guard, react-client-expert, fusion-sage.

Example provenance: collab-finder.

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.

Check your setup, free