Narrow React Prop Types logo

Narrow React Prop Types

OrganizationPopular
humanlayer
narrow-react-prop-types

narrow React component prop types to match live code paths

Overview

Publisherhumanlayer
Repositoryskills
Skill namenarrow-react-prop-types
Stars
4.7K
Forks
151
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by humanlayer on GitHub. Read the source before you install it.

Installation

Install the Narrow React Prop Types AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/humanlayer/skills/tree/main/plugins/narrow-react-prop-types/skills/narrow-react-prop-types
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/humanlayer/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/narrow-react-prop-types/skills/narrow-react-prop-types .claude/skills/narrow-react-prop-types
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Narrow React Prop Types in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Narrow React Prop Types on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Narrow React Prop Types is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Narrow React Prop Types

Use this skill when a React component's props have been widened for stories, mocks, tests, or demos and now express states the live application does not enter.

The goal is to make component types describe the real live-code-path contract, then require stories/tests/mocks to adapt to that contract instead of weakening it.

Core Requirements

  • Find the actual non-test, non-Storybook call sites before changing types.
  • Treat live code paths as the source of truth for the prop contract.
  • Do not preserve optional props only because they make Storybook, tests, or mock data easier.
  • Keep props optional only when there are non-Storybook, non-test call sites which do not provide them and which have a good reason for not doing so.
  • Types should not enable expressing states which are not observed in non-test, non-Storybook call sites.
  • Types should be as strict as possible so code can be as simple as possible.
  • Prefer deriving and extracting types from existing live-code-path values and APIs where possible.

Workflow

For an example recurring GitHub Actions workflow that runs this skill through CodeLayer, see references/agent-narrow-component-props.yml. Its example agent memory file is references/narrow-component-props-memory.md. For CI agent response formatting, see references/response-template.md.

1. Identify the suspect component

Look for components with these signals:

  • Large props interfaces with many optional fields.
  • Optional callback calls such as onSelect?.(...) or onArchive?.(...).
  • Fallback state handling such as items ?? [], count ?? 0, or handler && ... around values live code likely always supplies.
  • UI affordances that always render even though their callbacks are optional.
  • Props that look demo-oriented, such as defaultFoo, alternate handler shapes, or display toggles not used by live code.

Do not pick a target from a story or test alone. Use stories/tests only as supporting evidence that the type has been widened, not as evidence that a state is real.

2. Find every live usage

Search for all imports/usages of the component, exported prop type, and shared child primitives.

Classify call sites by whether they are live code paths or support code:

  • Live code paths: app routes, wired components, providers, hooks, production package exports, and shared components used by those paths.
  • Support code: Storybook stories, test files, fixtures, mocks, demo harnesses, and visual-only examples.

Only live code paths should determine what the component API supports.

3. Derive the real types from the live code paths

Read the live call sites and classify each prop:

  • Required: every non-test, non-Storybook call site supplies it.
  • Optional: at least one non-test, non-Storybook call site omits it and that omission is a meaningful runtime state.
  • Removed: no non-test, non-Storybook call site uses it.

Nullability and optionality are different. If live code always passes a prop but the value can be empty, prefer a required nullable prop such as focusedItem: FocusedItem | null over focusedItem?: FocusedItem | null.

4. Tighten the public prop type

Update exported prop types to match only the states observed in live code paths.

The looser and more optional a type is, the more possible states the component has to reason about. Every optional prop creates another branch the component must handle, test, and keep correct. Prefer strict types that prevent impossible states instead of broad types that require defensive render logic.

If the component always renders an interactive affordance, require the handler that makes it work. Do not allow inert states like a visible menu item that calls onRename?.(...).

5. Derive and extract types where possible

Prefer deriving types from the live APIs instead of restating them manually:

  • Parameters<typeof fn>[0] for function argument types.
  • ReturnType<typeof fn> for return types.
  • Extract<Union, Shape> for narrowing a union to a real variant.
  • React.Dispatch<React.SetStateAction<T>> for React state setters instead of approximating them as (value: T) => void.

Prefer explicit state type parameters when inference would widen or obscure the intended state shape:

ts
const [dialogState, setDialogState] = useState<DialogState>({
  id: null,
  isOpen: false,
})

Avoid relying on implicit useState(...) inference when it produces broad nullable object shapes, string literal widening, or callback types that later need hand-written approximations.

6. Tighten internal child props too

Do not stop at the exported component if it passes broad props into child primitives.

If row/menu/button child components receive optional handlers only because the parent props were broad, tighten those internal props too. Replace optional calls like this:

ts
onRename?.(id, name)

with required calls:

ts
onRename(id, name)

7. Remove fallback logic for unsupported states

Once props are required, remove defensive fallbacks that only existed for widened types.

Examples:

ts
new Set(expandedIds ?? defaultExpandedIds ?? [])

should become:

ts
new Set(expandedIds)
ts
items && items.length > 0

should become:

ts
items.length > 0

8. Update all variants that share the prop type

If multiple components share the broad prop type, update them together so they all enforce the same live-code-path contract.

9. Let tests and stories adapt to live code

If a story or test breaks after narrowing props, fix it by providing realistic handlers and state. Do not make live-code-path props optional again to reduce test setup.

If the story/test setup feels verbose, create a test helper or fixture that satisfies the strict live-code-path contract. Keep the helper in support code; do not weaken the component API.

10. Validate the change

Run package-level typechecks for the changed package and each live app/package that consumes the changed component.

Use repository-specific validation commands when available. In this monorepo, prefer:

bash
bun --bun run typecheck --filter <package>

11. Format the response

When running as a CI agent, format your final response according to references/response-template.md. This response becomes the PR body.

Include:

  • Summary of how many components were narrowed
  • Table of changes with rationale
  • Live call sites that justify each narrowing
  • Support code (stories/tests) that needed updating
  • Validation results
  • Risk assessment

Review Checklist

  • The changed prop type was derived from non-test, non-Storybook call sites.
  • Optional callbacks are removed for always-rendered interactions.
  • Rendered menu items and buttons cannot be inert because of missing handlers.
  • Removed props are not used by live code paths.
  • Nullability is preserved only for real states, such as no current focus.
  • Types are derived or extracted where possible rather than manually duplicated.
  • useState<T>(...) is used where inference would otherwise widen or obscure the intended state.
  • Shared variants compile against the same narrowed contract.
  • Typecheck passes for the shared package and consuming live app/package.

Anti-Patterns to Avoid

  • Making callbacks optional so stories can omit them.
  • Rendering a menu item that calls onAction?.(...).
  • Adding default* props for Storybook when live code is controlled.
  • Using ?? [] or ?? 0 to hide missing required live state.
  • Accepting multiple API shapes when live code only uses one.
  • Treating pure components as mock components with relaxed contracts.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Narrow React Prop Types AI skill do?

narrow React component prop types to match live code paths

Why use Narrow React Prop Types on TypingMind?

Because you install it once and use it with any model. Narrow React Prop Types is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Narrow React Prop Types in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/humanlayer/skills/tree/main/plugins/narrow-react-prop-types/skills/narrow-react-prop-types. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Narrow React Prop Types?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Narrow React Prop Types?

As many as you like. As long as a model supports skills, you can use Narrow React Prop Types with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Narrow React Prop Types AI skill free?

Yes. It is published on GitHub by humanlayer under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇