codable-patterns

dpearson2699/swift-ios-skills · updated Apr 8, 2026

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$npx skills add https://github.com/dpearson2699/swift-ios-skills --skill codable-patterns
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summary

Encode and decode Swift types using Codable (Encodable & Decodable) with

  • JSONEncoder, JSONDecoder, and related APIs. Targets Swift 6.2 / iOS 26+.
skill.md

Codable Patterns

Encode and decode Swift types using Codable (Encodable & Decodable) with JSONEncoder, JSONDecoder, and related APIs. Targets Swift 6.2 / iOS 26+.

Contents

Basic Conformance

When all stored properties are themselves Codable, the compiler synthesizes conformance automatically:

struct User: Codable {
    let id: Int
    let name: String
    let email: String
    let isVerified: Bool
}

let user = try JSONDecoder().decode(User.self, from: jsonData)
let encoded = try JSONEncoder().encode(user)

Prefer Decodable for read-only API responses and Encodable for write-only. Use Codable only when both directions are required.

Custom CodingKeys

Rename JSON keys without writing a custom decoder by declaring a CodingKeys enum:

struct Product: Codable {
    let id: Int
    let displayName: String
    let imageURL: URL
    let priceInCents: Int

    enum CodingKeys: String, CodingKey {
        case id
        case displayName = "display_name"
        case imageURL = "image_url"
        case priceInCents = "price_in_cents"
    }
}

Every stored property must appear in the enum. Omitting a property from CodingKeys excludes it from encoding/decoding -- provide a default value or compute it separately.

Custom Decoding and Encoding

Override init(from:) and encode(to:) for transformations the synthesized conformance cannot handle:

struct Event: Codable {
    let name: String
    let timestamp: Date
    let tags: [String]

    enum CodingKeys: String, CodingKey {
        case name, timestamp, tags
    }

    init(from decoder: Decoder) throws {
        let container = try decoder.container(keyedBy: CodingKeys.self)
        name = try container.decode(String.self, forKey: .name)
        // Decode Unix timestamp as Double, convert to Date
        let epoch = try container.decode(Double.self, forKey: .timestamp)
        timestamp = Date(timeIntervalSince1970: epoch)
        // Default to empty array when key is missing
        tags = try container.decodeIfPresent([String].self, forKey: .tags) ?? []
    }

    func encode(to encoder: Encoder) throws {
        var container = encoder.container(keyedBy: CodingKeys.self)
        try container.encode(name, forKey: .name)
        try container.encode(timestamp.timeIntervalSince1970, forKey: .timestamp)
        try container.encode(tags, forKey: .tags)
    }
}

Nested and Flattened Containers

Use nestedContainer(keyedBy:forKey:) to navigate and flatten nested JSON:

// JSON: { "id": 1, "location": { "lat": 37.7749, "lng": -122.4194 } }
struct Place: Decodable {
    let id: Int
    let latitude: Double
    let longitude: Double

    enum CodingKeys: String, CodingKey { case id, location }
    enum LocationKeys: String, CodingKey { case lat, lng }

    init(from decoder: Decoder) throws {
        let container = try decoder.container(keyedBy: CodingKeys.self)
        id = try container.decode(Int.self, forKey: .id)
        let location = try container.nestedContainer(
            keyedBy: LocationKeys.self, forKey: .location)
        latitude = try location.decode(Double.self, forKey: .lat)
        longitude = try location.decode(Double.self, forKey: .lng)
    }
}

Chain multiple nestedContainer calls to flatten deeply nested structures. Also use nestedUnkeyedContainer(forKey:) for nested arrays.

Heterogeneous Arrays

Decode arrays of mixed types using a discriminator field:

// JSON: [{"type":"text","content":"Hello"},{"type":"image","url":"pic.jpg"}]
enum ContentBlock: Decodable {
    case text(String)
    case image(URL)

    enum CodingKeys: String, CodingKey { case type, content, url }

    init(from decoder: Decoder) throws {
        let container = try decoder.container(keyedBy: CodingKeys.self)
        let type = try container.decode(String.self, forKey: .type)
        switch type {
        case "text":
            let content = try container.decode(String
how to use codable-patterns

How to use codable-patterns 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 codable-patterns
2

Execute installation command

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

$npx skills add https://github.com/dpearson2699/swift-ios-skills --skill codable-patterns

The skills CLI fetches codable-patterns from GitHub repository dpearson2699/swift-ios-skills 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/codable-patterns

Reload or restart Cursor to activate codable-patterns. Access the skill through slash commands (e.g., /codable-patterns) 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

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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

4.643 reviews
  • Shikha Mishra· Dec 28, 2024

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

  • James Liu· Dec 20, 2024

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

  • Aanya Malhotra· Dec 16, 2024

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

  • Nikhil Rahman· Dec 12, 2024

    Solid pick for teams standardizing on skills: codable-patterns is focused, and the summary matches what you get after install.

  • Yash Thakker· Nov 19, 2024

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

  • Aanya Mehta· Nov 19, 2024

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

  • Sakshi Patil· Nov 15, 2024

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

  • James Farah· Nov 11, 2024

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

  • Nikhil Torres· Nov 3, 2024

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

  • Omar Verma· Oct 22, 2024

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

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