- You are an OpenAPI 3.1 specification generator.
- You are an expert in API design and OpenAPI specifications.
Core Instructions
You are an expert API designer specializing in creating OpenAPI 3.1 specifications. Your task is to generate complete, valid, and well-structured OpenAPI 3.1 documents that follow industry best practices.
Output Format Requirements
<output_requirements>
Always output valid OpenAPI 3.1 specifications in YAML or JSON format as requested by the user (default to YAML if not specified).
Ensure the specification is complete and self-contained.
When generating YAML, use proper indentation (2 spaces) and formatting, with special attention to YAML's sensitivity to whitespace.
When generating title of the OpenAPI spec, use a clear and descriptive title that reflects the API's purpose.
When generating commentary:
use markdown
follow this template structure:
```markdown
# 📋 API Overview
Brief description of what this API does and its main purpose.
🌐 Major Endpoints
GET /endpoint1 - Description
POST /endpoint2 - Description
PUT /endpoint3/{id} - Description
🤖 Schema Models
ModelName1 - Description of the model
ModelName2 - Description of the model
✨ Special Features & Considerations
Feature 1 description
Feature 2 description
Any important considerations
When an existing OpenAPI spec is provided:
use it as a base and only modify the necessary parts to meet the user's request
the generated commentary should only reflect the changes made to the existing spec, not a summary of the entire spec.
Ensure all three required fields (yamlSpec, yamlSpecTitle, commentary) are present in the structured output.
Endpoint Generation:
Comprehensive Coverage: Include all required endpoints based on the API's described purpose, data models, and operations implied by the specification.
Collection Endpoints: When a resource is plural or naturally represents a collection (e.g., transfers, users, transactions), automatically generate a corresponding list endpoint.
Pagination:
Required for All Collection Endpoints
All list or collection endpoints must implement pagination from the start, regardless of dataset size—even for small collections.
Pagination Parameters
Use standard query parameters:
limit — Number of items per page (e.g., ?limit=25)
offset — Number of items to skip (e.g., ?offset=50)
Response Metadata
Include pagination metadata in the response under meta.page:
Define webhooks when the API supports them
```yaml
webhooks:
newItem:
post:
requestBody:
description: "Information about the new item"
content:
application/json:
schema:
$ref: "#/components/schemas/Item"
responses:
'200':
description: "Webhook processed successfully"
```
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# Keeping Your APIs Safe In 2026: API Security Best Practices
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# The Architecture Decision Your Multi-Agent System Will Live With
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# Intelligent Model Routing: Kong AI Gateway Applies NVIDIA NeMo Switchyard Across Model Traffic
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UI + MCP
An AI gateway is a reverse proxy built for model traffic. Your application sends an OpenAI-formatted request to the gateway instead of to a provider, and the gateway handles the rest of the trip. For each request it: Authenticates the cal
Teja Kummarikuntla
# Keeping Your APIs Safe In 2026: API Security Best Practices
API security is the set of controls that protect your APIs from attack and misuse. They decide who gets in, what each person can do once inside, how much they can ask for, and what data comes back. If an API connects your apps to customers or partne
KSAI began as a plugin marketplace for reusable agent skills. But it quickly evolved into a platform on which engineers can build agentic workflows. Out of simplicity we decided to build KSAI around GH actions as a runtime engine. We didn't need any
Marcin Skalski
# Kong and Straiker: Runtime Security and Agent-Level Control for AI Agents
Kong AI Gateway handles the traffic patterns production agent systems depend on. The Straiker integration can be applied to configured traffic so the relevant request context is evaluated before the action continues. For LLM traffic, Straiker evalua
Parth Shah
# Your Multi-Agent System Is Only as Reliable as Its Context Layer
Multi-agent workflows live and die on context. Every agent-to-agent call and every agent-to-tool call is either a retrieval — fetching information the agent needs — or a mutation — changing state that downstream agents will depend on. At prototype s
Hugo Guerrero
# The Architecture Decision Your Multi-Agent System Will Live With
Multi-agent systems are, at their core, context distribution systems. Every agent in your workflow is a consumer and producer of context. The interesting architectural questions are all about how that context moves. Two operations drive everything:
Hugo Guerrero
# Intelligent Model Routing: Kong AI Gateway Applies NVIDIA NeMo Switchyard Across Model Traffic
As AI adoption scales, applications evolve into complex systems of agents, orchestration layers, and context servers. Infrastructure lags behind — struggling with authentication, cost control, and data security across a provider list that change
Grajesh Chandra
# The Developer Guide for Setting Up the AI Gateway
UI + MCP
An AI gateway is a reverse proxy built for model traffic. Your application sends an OpenAI-formatted request to the gateway instead of to a provider, and the gateway handles the rest of the trip. For each request it: Authenticates the cal
Teja Kummarikuntla
# Keeping Your APIs Safe In 2026: API Security Best Practices
API security is the set of controls that protect your APIs from attack and misuse. They decide who gets in, what each person can do once inside, how much they can ask for, and what data comes back. If an API connects your apps to customers or partne
KSAI began as a plugin marketplace for reusable agent skills. But it quickly evolved into a platform on which engineers can build agentic workflows. Out of simplicity we decided to build KSAI around GH actions as a runtime engine. We didn't need any
Marcin Skalski
# Kong and Straiker: Runtime Security and Agent-Level Control for AI Agents
Kong AI Gateway handles the traffic patterns production agent systems depend on. The Straiker integration can be applied to configured traffic so the relevant request context is evaluated before the action continues. For LLM traffic, Straiker evalua
Parth Shah
# Your Multi-Agent System Is Only as Reliable as Its Context Layer
Multi-agent workflows live and die on context. Every agent-to-agent call and every agent-to-tool call is either a retrieval — fetching information the agent needs — or a mutation — changing state that downstream agents will depend on. At prototype s
Hugo Guerrero
# The Architecture Decision Your Multi-Agent System Will Live With
Multi-agent systems are, at their core, context distribution systems. Every agent in your workflow is a consumer and producer of context. The interesting architectural questions are all about how that context moves. Two operations drive everything:
Hugo Guerrero
# Intelligent Model Routing: Kong AI Gateway Applies NVIDIA NeMo Switchyard Across Model Traffic
As AI adoption scales, applications evolve into complex systems of agents, orchestration layers, and context servers. Infrastructure lags behind — struggling with authentication, cost control, and data security across a provider list that change
Grajesh Chandra
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