data

A guide for generating the data layer of a Flutter application from feature specifications or API documentation. This layer connects the app’s domain logic to remote services, local storage, or caches and converts data between formats.

In plain words
What is it for?
Use it to create data-transfer models, JSON serialization code, repository implementations, and remote, local, or cached data sources after the domain interfaces exist.
Why use it?
It keeps external communication and JSON conversion in one place instead of mixing them with the app’s core rules.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/launch52-ai/flutter-template/data
Any agent
npx skills add launch52-ai/flutter-template --skill data
Clone the repo
git clone --depth 1 https://github.com/launch52-ai/flutter-template

Made for: Claude Code, Codex.

Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,609 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00046 $0.01609
Opus 5 $0.00023 $0.00805
Sonnet 5 $0.00009 $0.00322
Haiku 4.5 $0.00005 $0.00161

Measured yesterday against content hash 839d533df519, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/data/SKILL.md · 189 lines

How it starts

The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data - Data Layer Generator

Generate data layer components that implement domain interfaces and handle external data sources.

Philosophy

The data layer is the only place for:

  • Freezed models with JSON serialization
  • API field mapping (@JsonKey)
  • Repository implementations
  • Data source abstractions (remote, local, cache)

This keeps the domain pure while handling all serialization and external communication here.

When to Use This Skill

  • After /domain has created entities and repository interfaces
  • When implementing API integration
  • When adding local storage/caching
  • When you have backend code or OpenAPI specs as reference
  • User asks to "implement repository", "create DTOs", or "wire up API"

Note: This skill replaces the former /api skill. All API integration functionality is now here.

What This Skill Creates

lib/features/{feature}/data/
├── models/
│   ├── {entity}_model.dart       # Freezed DTO with JSON
│   └── {entity}_model.g.dart     # Generated JSON code
├── repositories/
│   └── {feature}_repository_impl.dart  # Implements domain interface
└── data_sources/
    ├── {feature}_remote_data_source.dart   # API calls
    └── {feature}_local_data_source.dart    # Local storage (optional)

Input Sources

Source What Claude Extracts
Feature spec (.spec.md) Endpoints, DTOs, storage needs
Backend code (NestJS, Express) Controllers, DTOs, types
OpenAPI/Swagger spec Paths, schemas, request/response shapes
Endpoint descriptions GET /users → User[]

Workflow

Step 1: Locate Requirements

Check for:

lib/features/{feature}/.spec.md           # Feature specification
lib/features/{feature}/domain/            # Domain entities/repository interface

If domain layer doesn't exist, ask user to run /domain {feature} first.

Step 2: Analyze Data Sources

From spec:

  • API Endpoints - Section 3.1 (URLs, methods, request/response shapes)
  • Local Storage - Section 3.2 (what to persist, cache strategy)
  • Field Mappings - API field names vs domain field names

Read the full file on GitHub · 189 lines

Files

What ships with it

47 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 189 lines · 46 tokens per session scan A 839d533df519

Subscribe to this mod's changes

data is a skill published in the GitHub repository launch52-ai/flutter-template (2 stars, last pushed 6mo ago), licensed MIT. It adds 46 tokens to every session and 1,609 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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