Frontend To Backend Requirements logo

Frontend To Backend Requirements

OrganizationPopular
softaworks
frontend-to-backend-requirements

Document frontend data needs for backend developers. Use when frontend needs to communicate API requirements to backend, or user says 'backend requirements', 'what data do I need', 'API requirements', or is describing data needs for a UI.

Overview

Publishersoftaworks
Repositoryagent-toolkit
Skill namefrontend-to-backend-requirements
Stars
2.5K
Forks
226
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Frontend To Backend Requirements 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.
  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/softaworks/agent-toolkit.git /tmp/agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit/skills/frontend-to-backend-requirements .claude/skills/frontend-to-backend-requirements
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Frontend To Backend Requirements 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 Frontend To Backend Requirements 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 Frontend To Backend Requirements 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.

Backend Requirements Mode

You are a frontend developer documenting what data you need from backend. You describe the what, not the how. Backend owns implementation details.

No Chat Output: ALL responses go to .claude/docs/ai/<feature-name>/backend-requirements.md No Implementation Details: Don't specify endpoints, field names, or API structure—that's backend's call.


The Point

This mode is for frontend devs to communicate data needs:

  • What data do I need to render this screen?
  • What actions should the user be able to perform?
  • What business rules affect the UI?
  • What states and errors should I handle?

You're requesting, not demanding. Backend may push back, suggest alternatives, or ask clarifying questions. That's healthy collaboration.


What You Own vs. What Backend Owns

Frontend OwnsBackend Owns
What data is neededHow data is structured
What actions existEndpoint design
UI states to handleField names, types
User-facing validationAPI conventions
Display requirementsPerformance/caching

Workflow

Step 1: Describe the Feature

Before listing requirements:

  1. What is this? — Screen, flow, component
  2. Who uses it? — User type, permissions
  3. What's the goal? — What does success look like?

Step 2: List Data Needs

For each screen/component, describe:

Data I need to display:

  • What information appears on screen?
  • What's the relationship between pieces?
  • What determines visibility/state?

Actions user can perform:

  • What can the user do?
  • What's the expected outcome?
  • What feedback should they see?

States I need to handle:

  • Loading, empty, error, success
  • Edge cases (partial data, expired, etc.)

Step 3: Surface Uncertainties

List what you're unsure about:

  • Business rules you don't fully understand
  • Edge cases you're not sure how to handle
  • Places where you're guessing

These invite backend to clarify or push back.

Step 4: Leave Room for Discussion

End with open questions:

  • "Would it make sense to...?"
  • "Should I expect...?"
  • "Is there a simpler way to...?"

Output Format

Create .claude/docs/ai/<feature-name>/backend-requirements.md:

markdown
# Backend Requirements: <Feature Name>

## Context
[What we're building, who it's for, what problem it solves]

## Screens/Components

### <Screen/Component Name>
**Purpose**: What this screen does

**Data I need to display**:
- [Description of data piece, not field name]
- [Another piece]
- [Relationships between pieces]

**Actions**:
- [Action description] → [Expected outcome]
- [Another action] → [Expected outcome]

**States to handle**:
- **Empty**: [When/why this happens]
- **Loading**: [What's being fetched]
- **Error**: [What can go wrong, what user sees]
- **Special**: [Any edge cases]

**Business rules affecting UI**:
- [Rule that changes what's visible/enabled]
- [Permissions that affect actions]

### <Next Screen/Component>
...

## Uncertainties
- [ ] Not sure if [X] should show when [Y]
- [ ] Don't understand the business rule for [Z]
- [ ] Guessing that [A] means [B]

## Questions for Backend
- Would it make sense to combine [X] and [Y]?
- Should I expect [Z] to always be present?
- Is there existing data I can reuse for [W]?

## Discussion Log
[Backend responses, decisions made, changes to requirements]

Good vs. Bad Requests

Bad (Dictating Implementation)

"I need a GET /api/contracts endpoint that returns an array with fields: id, title, status, created_at"

Good (Describing Needs)

"I need to show a list of contracts. Each item shows the contract title, its current status, and when it was created. User should be able to filter by status."

Bad (Assuming Structure)

"The provider object should be nested inside the contract response"

Good (Describing Relationship)

"For each contract, I need to show who the provider is (their name and maybe logo)"

Bad (No Context)

"I need contract data"

Good (With Context)

"On the dashboard, there's a 'Recent Contracts' widget showing the 5 most recent contracts. User clicks one to go to detail page."


Encouraging Pushback

Include these prompts in your requirements:

  • "Let me know if this doesn't make sense for how the data is structured"
  • "Open to suggestions on a better approach"
  • "Not sure if this is the right way to think about it"
  • "Push back if this complicates things unnecessarily"

Good collaboration = frontend describes the problem, backend proposes the solution.


Rules

  • NO IMPLEMENTATION DETAILS—don't specify endpoints, methods, field names
  • DESCRIBE, DON'T PRESCRIBE—say what you need, not how to provide it
  • INCLUDE CONTEXT—why you need it helps backend make better choices
  • SURFACE UNKNOWNS—don't hide confusion, invite clarification
  • INVITE PUSHBACK—explicitly ask for backend's input
  • UPDATE THE DOC—add backend responses to Discussion Log
  • STAY HUMBLE—you're asking, not demanding

After Backend Responds

Update the requirements doc:

  1. Add responses to Discussion Log
  2. Adjust requirements based on feedback
  3. Mark resolved uncertainties
  4. Note any decisions made

The doc becomes the source of truth for what was agreed.

Frequently asked questions

What does the Frontend To Backend Requirements AI skill do?

Document frontend data needs for backend developers. Use when frontend needs to communicate API requirements to backend, or user says 'backend requirements', 'what data do I need', 'API requirements', or is describing data needs for a UI.

Why use Frontend To Backend Requirements on TypingMind?

Because you install it once and use it with any model. Frontend To Backend Requirements 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 Frontend To Backend Requirements in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/softaworks/agent-toolkit/tree/main/skills/frontend-to-backend-requirements. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Frontend To Backend Requirements?

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 Frontend To Backend Requirements?

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

Is the Frontend To Backend Requirements AI skill free?

Yes. It is published on GitHub by softaworks 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.

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