Developer Tools • Prompt Library

Prompt Library for Developers

Production-ready AI prompt templates for software engineering. Every template uses the STCO framework for deterministic, high-quality outputs across any LLM.

6 templates found

React Component Generator

Generate a fully typed React component with props interface, error boundaries, and accessibility attributes.

ReactCode Generationintermediate

SITUATION: You are a senior React engineer building a component library. TASK: Create a React component based on the specification below. CONTEXT: - Framework: React 18+ with TypeScript - Styling: CSS Modules or Tailwind - Must include: Props interface, defaultProps, aria attributes - Must handle: loading, error, empty states OUTPUT: Complete .tsx file with exports. No explanations.

#react#typescript#component#ui

API Endpoint Security Review

Audit a REST API endpoint for OWASP Top 10 vulnerabilities, rate limiting, and input validation.

Node.jsCode Reviewadvanced

SITUATION: You are a security-focused API reviewer. TASK: Review the following API endpoint code for security vulnerabilities. CONTEXT: - Check for: SQL injection, XSS, CSRF, broken auth, mass assignment - Verify: Rate limiting, input validation, error handling (no stack traces leaked) - Framework: Express.js / Fastify OUTPUT: JSON with { "severity": "critical|high|medium|low", "vulnerabilities": [...], "recommendations": [...] }

#security#api#node#owasp

Python Code Refactorer

Refactor Python code to follow PEP 8, add type hints, and improve algorithmic complexity.

PythonRefactoringintermediate

SITUATION: You are a Python performance engineer. TASK: Refactor the provided code for readability, type safety, and performance. CONTEXT: - Apply PEP 8 formatting - Add type hints to all functions - Replace O(n²) patterns with O(n) where possible - Use list comprehensions over loops where readable OUTPUT: Refactored code only. Add inline comments explaining non-obvious changes.

#python#refactoring#performance#pep8

Terraform Module Scaffolder

Generate a production-ready Terraform module with variables, outputs, and state management.

TerraformInfrastructureadvanced

SITUATION: You are a DevOps engineer building reusable Terraform modules. TASK: Create a Terraform module for the specified cloud resource. CONTEXT: - Provider: AWS / GCP / Azure (as specified) - Must include: variables.tf, outputs.tf, main.tf, versions.tf - Follow: HashiCorp module registry conventions - Include: Sensible defaults, description for every variable OUTPUT: Complete module files. No markdown wrapping.

#terraform#iac#devops#cloud

Unit Test Generator

Generate comprehensive unit tests for a function including edge cases, error paths, and mocking.

JestTestingbeginner

SITUATION: You are a test engineer writing unit tests. TASK: Write comprehensive Jest tests for the provided function. CONTEXT: - Test framework: Jest + @testing-library/react (if UI) - Cover: Happy path, edge cases, error handling, boundary values - Mock: External dependencies and API calls - Aim for: 90%+ branch coverage OUTPUT: Complete test file with describe/it blocks. Include setup/teardown.

#jest#testing#tdd#unit-tests

System Prompt Debugger

Analyze why a system prompt is producing inconsistent or incorrect outputs and suggest fixes.

LLMAI Engineeringadvanced

SITUATION: You are a prompt engineering consultant. TASK: Diagnose why the provided system prompt produces inconsistent outputs. CONTEXT: - Analyze for: Ambiguous instructions, conflicting constraints, missing output format - Check for: Prompt injection vulnerabilities - Model target: GPT-4 / Claude 3.5 / Gemini OUTPUT: JSON { "issues": [{ "type": "...", "severity": "...", "fix": "..." }], "improvedPrompt": "..." }

#prompt-engineering#debugging#llm#ai

Why STCO Framework?

Deterministic Outputs

Explicit Situation, Task, Context, and Output sections eliminate ambiguity. The model knows exactly what to do.

Injection Resistant

Structured boundaries between user input and system instructions make prompt injection 5.6× harder (arXiv 2024).

Cache Optimised

Stable system prompts enable prompt caching, cutting costs by up to 90% on repeated calls.

Model Agnostic

STCO works identically across GPT-4, Claude, Gemini, DeepSeek, and open-source models. No vendor lock-in.

Frequently Asked Questions

What is a prompt library for developers?

A prompt library for developers is a structured, searchable collection of production-ready prompt templates designed for software engineering tasks. Unlike generic prompt collections, a developer prompt library includes framework-specific templates (React, Python, Terraform), role-based system prompts (code reviewer, architect, debugger), and output schema enforcement for deterministic AI responses.

How is this different from a ChatGPT prompt list?

Generic prompt lists are unstructured, untested collections of one-liners. AI Prompt Architect's library uses the STCO (Situation, Task, Context, Output) framework — each prompt has explicit context boundaries, output format specifications, and constraint definitions. They are engineered for repeatability, not creativity.

Can I use these prompts with any LLM?

Yes. All prompts are model-agnostic and work with GPT-4, Claude, Gemini, DeepSeek, and open-source models like LLaMA and Mistral. The STCO structure is universally effective across architectures because it reduces ambiguity in the instruction itself.

How do I contribute prompts to the library?

Logged-in users can submit prompt templates via the prompt builder. Community submissions go through a quality review process that checks for STCO compliance, output determinism, and security (no prompt injection vectors). Accepted prompts are credited to the author.

Build your own prompts

Use the AI Prompt Architect wizard to create custom STCO templates tailored to your codebase.

Open Prompt Builder