Backend To Frontend Handoff Docs logo

Backend To Frontend Handoff Docs

OrganizationPopular
softaworks
backend-to-frontend-handoff-docs

Create API handoff documentation for frontend developers. Use when backend work is complete and needs to be documented for frontend integration, or user says 'create handoff', 'document API', 'frontend handoff', or 'API documentation'.

Overview

Publishersoftaworks
Repositoryagent-toolkit
Skill namebackend-to-frontend-handoff-docs
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 Backend To Frontend Handoff Docs 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/backend-to-frontend-handoff-docs .claude/skills/backend-to-frontend-handoff-docs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Backend To Frontend Handoff Docs 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 Backend To Frontend Handoff Docs 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 Backend To Frontend Handoff Docs 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.

API Handoff Mode

No Chat Output: Produce the handoff document only. No discussion, no explanation—just the markdown block saved to the handoff file.

You are a backend developer completing API work. Your task is to produce a structured handoff document that gives frontend developers (or their AI) full business and technical context to build integration/UI without needing to ask backend questions.

When to use: After completing backend API work—endpoints, DTOs, validation, business logic—run this mode to generate handoff documentation.

Simple API shortcut: If the API is straightforward (CRUD, no complex business logic, obvious validation), skip the full template—just provide the endpoint, method, and example request/response JSON. Frontend can infer the rest.

Goal

Produce a copy-paste-ready handoff document with all context a frontend AI needs to build UI/integration correctly and confidently.

Inputs

  • Completed API code (endpoints, controllers, services, DTOs, validation).
  • Related business context from the task/user story.
  • Any constraints, edge cases, or gotchas discovered during implementation.

Workflow

  1. Collect context — confirm feature name, relevant endpoints, DTOs, auth rules, and edge cases.
  2. Create/update handoff file — write the document to .claude/docs/ai/<feature-name>/api-handoff.md. Increment the iteration suffix (-v2, -v3, …) if rerunning after feedback.
  3. Paste template — fill every section below with concrete data. Omit subsections only when truly not applicable (note why).
  4. Double-check — ensure payloads match actual API behavior, auth scopes are accurate, and enums/validation reflect backend logic.

Output Format

Produce a single markdown block structured as follows. Keep it dense—no fluff, no repetition.


markdown
# API Handoff: [Feature Name]

## Business Context
[2-4 sentences: What problem does this solve? Who uses it? Why does it matter? Include any domain terms the frontend needs to understand.]

## Endpoints

### [METHOD] /path/to/endpoint
- **Purpose**: [1 line: what it does]
- **Auth**: [required role/permission, or "public"]
- **Request**:
  ```json
  {
    "field": "type — description, constraints"
  }
  • Response (success):
    json
    {
      "field": "type — description"
    }
  • Response (error): [HTTP codes and shapes, e.g., 422 validation, 404 not found]
  • Notes: [edge cases, rate limits, pagination, sorting, anything non-obvious]

[Repeat for each endpoint]

Data Models / DTOs

[List key models/DTOs the frontend will receive or send. Include field types, nullability, enums, and business meaning.]

typescript
// Example shape for frontend typing
interface ExampleDto {
  id: number;
  status: 'pending' | 'approved' | 'rejected';
  createdAt: string; // ISO 8601
}

Enums & Constants

[List any enums, status codes, or magic values the frontend needs to know. Include display labels if relevant.]

ValueMeaningDisplay Label
pendingAwaiting reviewPending

Validation Rules

[Summarize key validation rules the frontend should mirror for UX—required fields, min/max, formats, conditional rules.]

Business Logic & Edge Cases

  • [Bullet each non-obvious behavior, constraint, or gotcha]
  • [e.g., "User can only submit once per day", "Soft-deleted items excluded by default"]

Integration Notes

  • Recommended flow: [e.g., "Fetch list → select item → submit form → poll for status"]
  • Optimistic UI: [safe or not, why]
  • Caching: [any cache headers, invalidation triggers]
  • Real-time: [websocket events, polling intervals if applicable]

Test Scenarios

[Key scenarios frontend should handle—happy path, errors, edge cases. Use as acceptance criteria or test cases.]

  1. Happy path: [brief description]
  2. Validation error: [what triggers it, expected response]
  3. Not found: [when 404 is returned]
  4. Permission denied: [when 403 is returned]

Open Questions / TODOs

[Anything unresolved, pending PM decision, or needs frontend input. If none, omit section.]


---

## Rules
- **NO CHAT OUTPUT**—produce only the handoff markdown block, nothing else.
- Be precise: types, constraints, examples—not vague prose.
- Include real example payloads where helpful.
- Surface non-obvious behaviors—don't assume frontend will "just know."
- If backend made trade-offs or assumptions, document them.
- Keep it scannable: headers, tables, bullets, code blocks.
- No backend implementation details (no file paths, class names, internal services) unless directly relevant to integration.
- If something is incomplete or TBD, say so explicitly.

## After Generating
Write the final markdown into the handoff file only—do not echo it in chat. (If the platform requires confirmation, reference the file path instead of pasting contents.)

Frequently asked questions

What does the Backend To Frontend Handoff Docs AI skill do?

Create API handoff documentation for frontend developers. Use when backend work is complete and needs to be documented for frontend integration, or user says 'create handoff', 'document API', 'frontend handoff', or 'API documentation'.

Why use Backend To Frontend Handoff Docs on TypingMind?

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

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

Which AI models can use Backend To Frontend Handoff Docs?

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 Backend To Frontend Handoff Docs?

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

Is the Backend To Frontend Handoff Docs 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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