frontend-to-backend-requirements

softaworks/agent-toolkit · updated Apr 27, 2026

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$npx skills add https://github.com/softaworks/agent-toolkit --skill frontend-to-backend-requirements
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summary

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

skill.md

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 Owns Backend Owns
What data is needed How data is structured
What actions exist Endpoint design
UI states to handle Field names, types
User-facing validation API conventions
Display requirements Performance/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:

# 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.

how to use frontend-to-backend-requirements

How to use frontend-to-backend-requirements 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 frontend-to-backend-requirements
2

Execute installation command

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

$npx skills add https://github.com/softaworks/agent-toolkit --skill frontend-to-backend-requirements

The skills CLI fetches frontend-to-backend-requirements from GitHub repository softaworks/agent-toolkit 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/frontend-to-backend-requirements

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

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.725 reviews
  • Noor Shah· Dec 24, 2024

    frontend-to-backend-requirements reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Daniel Ghosh· Dec 8, 2024

    frontend-to-backend-requirements is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Pratham Ware· Dec 4, 2024

    We added frontend-to-backend-requirements from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Carlos Diallo· Nov 27, 2024

    Solid pick for teams standardizing on skills: frontend-to-backend-requirements is focused, and the summary matches what you get after install.

  • Sakshi Patil· Nov 23, 2024

    frontend-to-backend-requirements fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Meera Jackson· Nov 15, 2024

    Registry listing for frontend-to-backend-requirements matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Carlos Rahman· Nov 7, 2024

    frontend-to-backend-requirements has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Carlos Ghosh· Oct 26, 2024

    Useful defaults in frontend-to-backend-requirements — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Amelia Diallo· Oct 18, 2024

    We added frontend-to-backend-requirements from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Chaitanya Patil· Oct 14, 2024

    frontend-to-backend-requirements is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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