tauri▌
martinholovsky/claude-skills-generator · updated Apr 8, 2026
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This skill uses a split structure for HIGH-RISK requirements:
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
- TDD First: Write tests before implementation - verify behavior works correctly
- Performance Aware: Async commands, efficient IPC serialization, resource management
- Least Privilege: Grant only necessary capabilities and permissions
- Defense in Depth: Multiple security layers (CSP, capabilities, validation)
- Secure Defaults: Start with restrictive config, enable features explicitly
- Input Validation: Validate all IPC messages from frontend
- Origin Verification: Check origins for all sensitive operations
- 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 tauriHow to use tauri on Cursor
AI-first code editor with Composer
1Prerequisites
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
2Execute 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 tauriThe skills CLI fetches tauri from GitHub repository martinholovsky/claude-skills-generator and configures it for Cursor.
3Select 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│ • Windsurf4Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/tauriReload 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.
Additional Resources
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
general reviewsRatings
4.6★★★★★46 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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