swiftdata-pro

twostraws/swiftdata-agent-skill · updated Apr 8, 2026

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$npx skills add https://github.com/twostraws/swiftdata-agent-skill --skill swiftdata-pro
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summary

Write, review, and improve SwiftData code using modern APIs and best practices.

  • Validates code against core SwiftData rules including relationships, delete rules, autosaving, and FetchDescriptor patterns
  • Checks predicates for safety and runtime crashes, with special handling for CloudKit constraints and unsupported operations
  • Targets Swift 6.2+ with modern concurrency; recommends indexing strategies for iOS 18+ and class inheritance patterns for iOS 26+
  • Reports only genuine issues
skill.md

Write and review SwiftData code for correctness, modern API usage, and adherence to project conventions. Report only genuine problems - do not nitpick or invent issues.

Review process:

  1. Check for core SwiftData issues using references/core-rules.md.
  2. Check that predicates are safe and supported using references/predicates.md.
  3. If the project uses CloudKit, check for CloudKit-specific constraints using references/cloudkit.md.
  4. If the project targets iOS 18+, check for indexing opportunities using references/indexing.md.
  5. If the project targets iOS 26+, check for class inheritance patterns using references/class-inheritance.md.

If doing partial work, load only the relevant reference files.

Core Instructions

  • Target Swift 6.2 or later, using modern Swift concurrency.
  • The user strongly prefers to use SwiftData across the board. Do not suggest Core Data functionality unless it is a feature that cannot be solved with SwiftData.
  • Do not introduce third-party frameworks without asking first.
  • Use a consistent project structure, with folder layout determined by app features.

Output Format

If the user asks for a review, organize findings by file. For each issue:

  1. State the file and relevant line(s).
  2. Name the rule being violated.
  3. Show a brief before/after code fix.

Skip files with no issues. End with a prioritized summary of the most impactful changes to make first.

If the user asks you to write or improve code, follow the same rules above but make the changes directly instead of returning a findings report.

Example output:

Destination.swift

Line 8: Add an explicit delete rule for relationships.

// Before
var sights: [Sight]

// After
@Relationship(deleteRule: .cascade, inverse: \Sight.destination) var sights: [Sight]

Line 22: Do not use isEmpty == false in predicates – it crashes at runtime. Use ! instead.

// Before
#Predicate<Destination> { $0.sights.isEmpty == false }

// After
#Predicate<Destination> { !$0.sights.isEmpty }

DestinationListView.swift

Line 5: @Query must only be used inside SwiftUI views.

// Before
class DestinationStore {
    @Query var destinations: [Destination]
}

// After
class DestinationStore {
    var modelContext: ModelContext

    func fetchDestinations() throws -> [Destination] {
        try modelContext.fetch(FetchDescriptor<Destination>())
    }
}

Summary

  1. Data loss (high): Missing delete rule on line 8 of Destination.swift means sights will be orphaned when a destination is deleted.
  2. Crash (high): isEmpty == false on line 22 will crash at runtime – use !isEmpty instead.
  3. Incorrect behavior (high): @Query on line 5 of DestinationListView.swift only works inside SwiftUI views.

End of example.

References

  • references/core-rules.md - autosaving, relationships, delete rules, property restrictions, and FetchDescriptor optimization.
  • references/predicates.md - supported predicate operations, dangerous patterns that crash at runtime, and unsupported methods.
  • references/cloudkit.md - CloudKit-specific constraints including uniqueness, optionality, and eventual consistency.
  • references/indexing.md - database indexing for iOS 18+, including single and compound property indexes.
  • references/class-inheritance.md - model subclassing for iOS 26+, including @available requirements, schema setup, and predicate filtering.
how to use swiftdata-pro

How to use swiftdata-pro 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 swiftdata-pro
2

Execute installation command

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

$npx skills add https://github.com/twostraws/swiftdata-agent-skill --skill swiftdata-pro

The skills CLI fetches swiftdata-pro from GitHub repository twostraws/swiftdata-agent-skill 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/swiftdata-pro

Reload or restart Cursor to activate swiftdata-pro. Access the skill through slash commands (e.g., /swiftdata-pro) 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)
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general reviews

Ratings

4.736 reviews
  • Hiroshi Huang· Dec 24, 2024

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

  • Aarav Singh· Dec 16, 2024

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

  • Charlotte Ghosh· Nov 11, 2024

    swiftdata-pro is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Aarav Martinez· Nov 7, 2024

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

  • Rahul Santra· Nov 3, 2024

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

  • Kofi Thomas· Oct 26, 2024

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

  • Pratham Ware· Oct 22, 2024

    swiftdata-pro has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Luis Diallo· Oct 2, 2024

    swiftdata-pro reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Sakshi Patil· Sep 25, 2024

    Keeps context tight: swiftdata-pro is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Mei Thompson· Sep 13, 2024

    swiftdata-pro is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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