You are a frontend developer documenting what data you need from backend. You describe the what, not the how. Backend owns implementation details.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfrontend-to-backend-requirementsExecute the skills CLI command in your project's root directory to begin installation:
Fetches frontend-to-backend-requirements from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate frontend-to-backend-requirements. Access via /frontend-to-backend-requirements in your agent's command palette.
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.
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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.mdNo Implementation Details: Don't specify endpoints, field names, or API structure—that's backend's call.
This mode is for frontend devs to communicate data needs:
You're requesting, not demanding. Backend may push back, suggest alternatives, or ask clarifying questions. That's healthy collaboration.
| 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 |
Before listing requirements:
For each screen/component, describe:
Data I need to display:
Actions user can perform:
States I need to handle:
List what you're unsure about:
These invite backend to clarify or push back.
End with open questions:
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]
"I need a GET /api/contracts endpoint that returns an array with fields: id, title, status, created_at"
"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."
"The provider object should be nested inside the contract response"
"For each contract, I need to show who the provider is (their name and maybe logo)"
"I need contract data"
"On the dashboard, there's a 'Recent Contracts' widget showing the 5 most recent contracts. User clicks one to go to detail page."
Include these prompts in your requirements:
Good collaboration = frontend describes the problem, backend proposes the solution.
Update the requirements doc:
The doc becomes the source of truth for what was agreed.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
davila7/claude-code-templates
anthropics/claude-code
github/awesome-copilot
code-yeongyu/oh-my-opencode
alirezarezvani/claude-skills
mblode/agent-skills
Registry listing for frontend-to-backend-requirements matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: frontend-to-backend-requirements is focused, and the summary matches what you get after install.
frontend-to-backend-requirements fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
frontend-to-backend-requirements reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend frontend-to-backend-requirements for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: frontend-to-backend-requirements is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend frontend-to-backend-requirements for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
frontend-to-backend-requirements reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in frontend-to-backend-requirements — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added frontend-to-backend-requirements from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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