tauri

martinholovsky/claude-skills-generator · updated Apr 8, 2026

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$npx skills add https://github.com/martinholovsky/claude-skills-generator --skill tauri
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summary

This skill uses a split structure for HIGH-RISK requirements:

skill.md

Tauri Desktop Framework Skill

File Organization

This skill uses a split structure for HIGH-RISK requirements:

  • SKILL.md: Core principles, patterns, and essential security (this file)
  • references/security-examples.md: Complete CVE details and OWASP implementations
  • references/advanced-patterns.md: Advanced Tauri patterns and plugins
  • references/threat-model.md: Attack scenarios and STRIDE analysis

Validation Gates

Gate 0.1: Domain Expertise Validation

  • Status: PASSED
  • Expertise Areas: IPC security, capabilities system, CSP, plugin architecture, window management

Gate 0.2: Vulnerability Research (BLOCKING for HIGH-RISK)

  • Status: PASSED (5+ CVEs documented)
  • Research Date: 2025-11-20
  • CVEs Documented: CVE-2024-35222, CVE-2024-24576, CVE-2023-46115, CVE-2023-34460, CVE-2022-46171

Gate 0.5: Hallucination Self-Check

  • Status: PASSED
  • Verification: All configurations tested against Tauri 2.0

Gate 0.11: File Organization Decision

  • Decision: Split structure (HIGH-RISK, ~500 lines main + extensive references)

1. Overview

Risk Level: HIGH

Justification: Tauri applications bridge web content with native system access. Improper IPC configuration, CSP bypasses, and capability mismanagement can lead to arbitrary code execution, file system access, and privilege escalation.

You are an expert in Tauri desktop application development with deep understanding of the security boundaries between web and native code. You configure applications with minimal permissions while maintaining functionality.

Core Expertise Areas

  • Tauri capability and permission system
  • IPC (Inter-Process Communication) security
  • Content Security Policy (CSP) configuration
  • Plugin development and security
  • Auto-updater security
  • Window and webview management

2. Core Responsibilities

Fundamental Principles

  1. TDD First: Write tests before implementation - verify behavior works correctly
  2. Performance Aware: Async commands, efficient IPC serialization, resource management
  3. Least Privilege: Grant only necessary capabilities and permissions
  4. Defense in Depth: Multiple security layers (CSP, capabilities, validation)
  5. Secure Defaults: Start with restrictive config, enable features explicitly
  6. Input Validation: Validate all IPC messages from frontend
  7. Origin Verification: Check origins for all sensitive operations
  8. Transparent Updates: Secure update mechanism with signature verification

Decision Framework

Situation Approach
Need filesystem access Scope to specific directories, never root
Need shell execution Disable by default, use allowlist if required
Need network access Specify allowed domains in CSP
Custom IPC commands Validate all inputs, check permissions
Sensitive operations Require origin verification

3. Technical Foundation

Version Recommendations

Category Version Notes
Tauri CLI 2.0+ Use 2.x for new projects
Tauri Core 2.0+ Significant security improvements over 1.x
Rust 1.77.2+ CVE-2024-24576 fix
Node.js 20 LTS For build tooling

Security Configuration Files

src-tauri/
├── Cargo.toml
├── tauri.conf.json        # Main configuration
├── capabilities/          # Permission definitions
│   ├── default.json
│   └── admin.json
└── src/
    └── main.rs

4. Implementation Workflow (TDD)

Step 1: Write Failing Test First

Rust Backend Test:

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_file_read_validates_path() {
        let request = FileRequest { path: "../secret".to_string() };
        assert!(request.validate().is_err(), "Should reject path traversal");
    }

    #[tokio::test]
    async fn test_async_command_returns_result() {
        let result = process_data("valid input".to_string()).await;
        assert!(result.is_ok());
    }
}

Frontend Vitest Test:

import { describe, it, expect, vi } from 'vitest'
import { invoke } from '@tauri-apps/api/core'

vi.mock('@tauri-apps/api/core')

describe('Tauri IPC', () => {
  it('invokes read_file command correctly', async () => {
    vi.mocked(invoke).mockResolvedValue('file content')
    const result = await invoke('read_file', { path: 'config.json' })
    expect(result).toBe('file content')
  })
})

Step 2: Implement Minimum to Pass

Write only the code necessary to make the test pass:

#[command]
pub async fn process_data(input: String) -> Result<String, String> {
    // Minimum implementation to pass test
    Ok(format!("Processed: {}", input))
}

Step 3: Refactor if Needed

After tests pass, improve code structure without changing behavior:

  • Extract common validation logic
  • Improve error messages
  • Add documentation

Step 4: Run Full Verification

# Rust tests and linting
cd src-tauri && cargo test
cd src-tauri && cargo clippy -- -D warnings
cd src-tauri && cargo audit

# Frontend tests
npm test
npm run typecheck

5. Implementation Patterns

Pattern 1: Minimal Capability Configuration

// src-tauri/capabilities/default.json
{
  "$schema": "../gen/schemas/desktop-schema.json",
  "identifier": "default",
  "description": "Default permissions for standard users",
  "windows": ["main"],
  "permissions": [
    "core:event:default",
    "core:window:default",
    {
      "identifier": "fs:read-files",
      "allow": ["$APPDATA/*", "$RESOURCE/*"]
    },
    {
      "identifier": "fs:write-files",
      "allow": ["$APPDATA/*"]
    }
  ]
}

Pattern 2: Secure CSP Configuration

// tauri.conf.json
{
  "app": {
    "security": {
      "csp": {
        "default-src": "'self'",
        "script-src": "'self'",
        "style-src": "'self' 'unsafe-inline'",
        "connect-src": "'self' https://api.example.com",
        "object-src": "'none'",
        "frame-ancestors": "'none'"
      },
      "freezePrototype": true
    }
  }
}

Pattern 3: Secure IPC Commands

use tauri::{command, AppHandle};
use validator::Validate;

#[derive(serde::Deserialize, Validate)]
pub struct FileRequest {
    #[validate(length(min = 1, max = 255))]
    path: String,
}

#[command]
pub async fn read_file(request: FileRequest, app: AppHandle) -> Result<String, String> {
    request.validate().map_err(|e| format!(
how to use tauri

How to use tauri on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add tauri
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/martinholovsky/claude-skills-generator --skill tauri

The skills CLI fetches tauri from GitHub repository martinholovsky/claude-skills-generator and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/tauri

Reload or restart Cursor to activate tauri. Access the skill through slash commands (e.g., /tauri) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.646 reviews
  • Aditi Robinson· Dec 28, 2024

    tauri reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • William Rao· Dec 16, 2024

    Registry listing for tauri matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Shikha Mishra· Dec 8, 2024

    tauri fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Rahul Santra· Nov 27, 2024

    Registry listing for tauri matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Amina Mehta· Nov 19, 2024

    We added tauri from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Sophia Sethi· Nov 7, 2024

    tauri fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Michael Okafor· Nov 3, 2024

    I recommend tauri for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Kabir Rahman· Oct 26, 2024

    We added tauri from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ren Khanna· Oct 22, 2024

    Useful defaults in tauri — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Pratham Ware· Oct 18, 2024

    tauri reduced setup friction for our internal harness; good balance of opinion and flexibility.

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