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happyflow-generator Skill

description: Automatically generate and execute Python test scripts from OpenAPI specifications and GraphQL schemas with enhanced features

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Stars
259
Forks
20
Updated
April 22, 2026
Quality score
45

Why use this skill

happyflow-generator is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around security skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is Claude Code Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.

That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?

How to evaluate and use it

Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in aiskillstore/marketplace, which gives you a concrete repo context, update history, and direct ownership trail.

Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.

SKILL.md preview

Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.

---
name: happyflow-generator
description: Automatically generate and execute Python test scripts from OpenAPI specifications and GraphQL schemas with enhanced features
---

# HappyFlow Generator Skill

## Metadata
- **Skill Name**: HappyFlow Generator
- **Version**: 2.0.0
- **Category**: API Testing & Automation
- **Required Capabilities**: Code execution, web requests, file operations
- **Estimated Duration**: 2-5 minutes per API spec
- **Difficulty**: Intermediate

## Description

Automatically generate and execute Python test scripts from OpenAPI specifications and GraphQL schemas that successfully call all API endpoints in dependency-correct order, ensuring all requests return 2xx status codes.

**Input**: OpenAPI/GraphQL spec (URL/file) + authentication credentials  
**Output**: Working Python script that executes complete API happy path flow

**Key Features**:
- **Multi-format support**: OpenAPI 3.0+ and GraphQL schemas
- **Enhanced execution**: Parallel execution, detailed reporting, connection pooling
- **Advanced testing**: File upload support, response schema validation, rate limiting handling
- **Modular architecture**: Well-organized codebase with proper error handling

## Complete Workflow

### Phase 1: Authentication Setup

Execute this code to prepare authentication headers:

```python
import base64
import requests
from typing import Dict, Any

def setup_authentication(auth_type: str, credentials: Dict[str, Any]) -> Dict[str, str]:
    """Prepare authentication headers based on auth type"""

    if auth_type == "bearer":
        return {"Authorization": f"Be

...