This skill uses a split structure for HIGH-RISK requirements:
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontauriExecute the skills CLI command in your project's root directory to begin installation:
Fetches tauri from martinholovsky/claude-skills-generator and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate tauri. Access via /tauri in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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This skill uses a split structure for HIGH-RISK requirements:
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.
| 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 |
| 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 |
src-tauri/
├── Cargo.toml
├── tauri.conf.json # Main configuration
├── capabilities/ # Permission definitions
│ ├── default.json
│ └── admin.json
└── src/
└── main.rs
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')
})
})
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))
}
After tests pass, improve code structure without changing behavior:
# 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
// 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/*"]
}
]
}
// 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
}
}
}
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!(✓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
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share 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
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4.6★★★★★46 reviews- AAditi Robinson★★★★★Dec 28, 2024
tauri reduced setup friction for our internal harness; good balance of opinion and flexibility.
- WWilliam Rao★★★★★Dec 16, 2024
Registry listing for tauri matched our evaluation — installs cleanly and behaves as described in the markdown.
- SShikha Mishra★★★★★Dec 8, 2024
tauri fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- RRahul Santra★★★★★Nov 27, 2024
Registry listing for tauri matched our evaluation — installs cleanly and behaves as described in the markdown.
- AAmina Mehta★★★★★Nov 19, 2024
We added tauri from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- SSophia Sethi★★★★★Nov 7, 2024
tauri fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- MMichael Okafor★★★★★Nov 3, 2024
I recommend tauri for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- KKabir Rahman★★★★★Oct 26, 2024
We added tauri from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- RRen Khanna★★★★★Oct 22, 2024
Useful defaults in tauri — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- PPratham Ware★★★★★Oct 18, 2024
tauri reduced setup friction for our internal harness; good balance of opinion and flexibility.
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