agentic-development

Load with: base.md + llm-patterns.md + [language].md

alinaqi/claude-bootstrap|Updated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGoose

Installation Guide

Select your AI agent

How to use agentic-development on Cursor

AI-first code editor with Composer

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

›Cursor installed and configured on your machine
›Node.js 16+ with npm — verify with node --version
›Active project directory where you want to add agentic-development

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/alinaqi/claude-bootstrap --skill agentic-development

Fetches agentic-development from alinaqi/claude-bootstrap and configures it for Cursor.

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?

│

│ ── Universal (.agents/skills) ────────────────

│ · Cline · Codex · Goose · Windsurf

│ ●Cursor(selected)

│ · Cursor · Aider · Continue

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/agentic-development

Restart Cursor to activate agentic-development. Access via /agentic-development in your agent's command palette.

⚠

Security Notice

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.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

›View source on GitHub ›Skills CLI docs ›About Cursor ›What are agent skills?

Documentation

List & Monetize Your Skill

Submit your Claude Code skill and start earning

Get started →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Framework Selection by Language

Language/Framework

Default

Why

Python

Pydantic AI

Type-safe, Pydantic validation, multi-model, production-ready

Node.js / Next.js

Claude Agent SDK

Official Anthropic SDK, tools, multi-agent, native streaming

Python: Pydantic AI (Default)

from pydantic_ai import Agent from pydantic import BaseModel class SearchResult(BaseModel): title: str url: str summary: str agent = Agent( 'claude-sonnet-4-20250514', result_type=list[SearchResult], system_prompt='You are a research assistant.', ) # Type-safe result result = await agent.run('Find articles about AI agents') for item in result.data: print(f"{item.title}: {item.url}")

Node.js / Next.js: Claude Agent SDK (Default)

import Anthropic from "@anthropic-ai/sdk"; const client = new Anthropic(); // Define tools const tools: Anthropic.Tool[] = [ { name: "web_search", description: "Search the web for information", input_schema: { type: "object", properties: { query: { type: "string", description: "Search query" }, }, required: ["query"], }, }, ]; // Agentic loop async function runAgent(prompt: string) { const messages: Anthropic.MessageParam[] = [ { role: "user", content: prompt }, ]; while (true) { const response = await client.messages.create({ model: "claude-sonnet-4-20250514", max_tokens: 4096, tools, messages, }); // Check for tool use if (response.stop_reason === "tool_use") { const toolUse = response.content.find((b) => b.type === "tool_use"); if (toolUse) { const result = await executeTool(toolUse.name, toolUse.input); messages.push({ role: "assistant", content: response.content }); messages.push({ role: "user", content: [{ type: "tool_result", tool_use_id: toolUse.id, content: result }], }); continue; } } // Done - return final response return response.content.find((b) => b.type === "text")?.text; } }

Agent Architecture

Three Components (OpenAI)

┌─────────────────────────────────────────────────┐ │ AGENT │ ├─────────────────────────────────────────────────┤ │ Model (Brain) │ LLM for reasoning & │ │ │ decision-making │ ├─────────────────────┼───────────────────────────┤ │ Tools (Arms/Legs) │ APIs, functions, external │ │ │ systems for action │ ├─────────────────────┼───────────────────────────┤ │ Instructions │ System prompts defining │ │ (Rules) │ behavior & boundaries │ └─────────────────────┴───────────────────────────┘

Project Structure

project/ ├── src/ │ ├── agents/ │ │ ├── orchestrator.ts # Main agent coordinator │ │ ├── specialized/ # Task-specific agents │ │ │ ├── researcher.ts │ │ │ ├── coder.ts │ │ │ └── reviewer.ts │ │ └── base.ts # Shared agent interface │ ├── tools/ │ │ ├── definitions/ # Tool schemas │ │ ├── implementations/ # Tool logic │ │ └── registry.ts # Tool discovery │ ├── prompts/ │ │ ├── system/ # Agent instructions │ │ └── templates/ # Task templates │ └── memory/ │ ├── conversation.ts # Short-term context │ └── persistent.ts # Long-term storage ├── tests/ │ ├── agents/ # Agent behavior tests │ ├── tools/ # Tool unit tests │ └── evals/ # End-to-end evaluations └── skills/ # Agent skills (Anthropic pattern) ├── skill-name/ │ ├── instructions.md │ ├── scripts/ │ └── resources/

Workflow Pattern: Explore-Plan-Execute-Verify

1. Explore Phase

// Gather context before acting async function explore(task: Task): Promise<Context> { const relevantFiles = await agent.searchCodebase(task.query); const existingPatterns = await agent.analyzePatterns(relevantFiles); const dependencies = await agent.identifyDependencies(task); return { relevantFiles, existingPatterns, dependencies }; }

2. Plan Phase (Critical)

// Plan explicitly before execution async function plan(task: Task, context: Context): Promise<Plan> { const prompt = ` Task: ${task.description} Context: ${JSON.stringify(context)} Create a step-by-step plan. For each step: 1. What action to take 2. What tools to use 3. How to verify success 4. What could go wrong Output JSON with steps array. `

agentic-development

Installation Guide

How to use agentic-development on Cursor

Prerequisites

Run the install command

Select Cursor when prompted

Verify installation

Security Notice

Documentation

List & Monetize Your Skill

Use Cases

User Story & Requirements Generation

Competitive Analysis

Roadmap Prioritization

Install Skill

Agentic Development Skill

Framework Selection by Language

Python: Pydantic AI (Default)

Node.js / Next.js: Claude Agent SDK (Default)

Core Principle

Agent Architecture

Three Components (OpenAI)

Project Structure

Workflow Pattern: Explore-Plan-Execute-Verify

1. Explore Phase

2. Plan Phase (Critical)

`Stakeholder Communication`

`Implementation Guide`

`Best Practices`

`When to Use This`

`Learning Path`

`Related Skills`

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premortem

deslop

framer-motion

write-a-prd

travel-planner

`Reviews`

`Discussion`