What is feature-data-flow?

A set of instructions for building a complete Flutter feature that loads and displays data. Flutter is a toolkit for making mobile, web, and desktop applications from one codebase.

Use it when a feature needs API response models, conversion into app data, Riverpod state, use cases, and a screen that renders the loaded information.

It provides a consistent path from an API description to usable screen data, avoiding a UI that only looks complete but is not connected to storage or a backend service.

feature-data-flow — Skill by strvcom

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/strvcom/flutter-template/feature-data-flow

Any agent

npx skills add strvcom/flutter-template --skill feature-data-flow

Clone the repo

git clone --depth 1 https://github.com/strvcom/flutter-template

Made for: Claude Code, Codex.

Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,227 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.

Model	Per session	Once invoked
Fable 5	$0.00063	$0.01227
Opus 5	$0.00032	$0.00613
Sonnet 5	$0.00013	$0.00245
Haiku 4.5	$0.00006	$0.00123

Measured 2d ago against content hash cbc26d20627f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

feature-data-flow 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 2d ago.

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.

.agents/skills/feature-data-flow/SKILL.md · 128 lines

How it starts

The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Flutter Template Feature Data Flow

Use this skill when implementing a complete feature with data flow, not just a route or UI shell.

Use This For

a screen backed by API data
a feature that needs request or response models
a feature that needs DTO to entity mapping
a feature that should load data into Riverpod state
a feature that should read Swagger or backend schema before implementation

If the task is only a route and UI shell, prefer feature-screen.

Read First

AGENTS.md
docs/PROJECT_OVERVIEW.md
docs/PROJECT_GUIDELINES.md
build.yaml
lib/core/network/dio_provider.dart
lib/common/usecase/
lib/common/data/dto/
lib/common/data/entity/

Useful current examples:

lib/common/data/dto/user_response_dto.dart
lib/common/data/entity/user_entity.dart
lib/common/usecase/user/get_current_user_use_case.dart
lib/features/profile/profile_state.dart

Backend reference when relevant:

consult the adopting project's API docs or OpenAPI/Swagger spec
check the configured API base URL in Configuration.instance.apiHostUrl
check the environment value behind API_HOST_URL when verifying which backend you are integrating with

Target Architecture

Aim for this flow:

Feature UI
  -> feature state notifier
  -> use case
  -> dioProvider or shared storage layer
  -> DTO
  -> Entity
  -> feature state
  -> UI

Workflow

Identify the user-facing behavior and the data needed by the screen or flow.
Read the backend contract first when the task depends on network payload shape.
Add request or response DTOs in lib/common/data/dto/.
Use freezed plus JSON serialization for DTOs that come from or go to the backend.
Name DTO files with the *_dto.dart pattern so build.yaml codegen picks them up.
Add app-facing entity models in lib/common/data/entity/ when the UI should not consume the transport model directly.
Add conversion logic close to the DTO using an extension such as SomeResponseDTOExtension.
Add a focused Riverpod use case in lib/common/usecase/ to perform the IO.
Keep the use case narrow: fetch, parse, map, and return the result.
Update the feature *_state.dart notifier to call the use case and store UI-ready values.
Emit one-off events through *_event.dart only for navigation, snackbars, dialogs, or similar side effects.
Render the result in *_page_content.dart, using mapState or mapContentState where appropriate.
Run make gen, then analyze and tests.

Read the full file on GitHub · 128 lines

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.

2d ago First seen · 128 lines · 63 tokens per session scan A cbc26d20627f

Subscribe to this mod's changes

feature-data-flow is a skill published in the GitHub repository strvcom/flutter-template (24 stars, last pushed 2mo ago), licensed MIT. It adds 63 tokens to every session and 1,227 once invoked, about $0.0003 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 2026-08-30.

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