autonomous-agent-patterns

Design patterns for building autonomous coding agents with tool integration, permissions, and human oversight.

sickn33/antigravity-awesome-skills|Updated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGoose

What it does

Core agent loop architecture with think-decide-act-observe cycle; supports multi-model selection for different task types
Tool design patterns including file operations, code understanding, terminal execution, and browser automation with schema-based definitions
Permission system with four levels (auto, ask-once, ask-each, never) and risk assessment for approval workflows

Repository

sickn33/antigravity-awesome-skills

Last updated

Apr 8, 2026

Installation Guide

Select your AI agent

How to use autonomous-agent-patterns on Cursor

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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 autonomous-agent-patterns

Run the install command

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

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill autonomous-agent-patterns

Fetches autonomous-agent-patterns from sickn33/antigravity-awesome-skills 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/autonomous-agent-patterns

Restart Cursor to activate autonomous-agent-patterns. Access via /autonomous-agent-patterns 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

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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

1. Core Agent Architecture

1.1 Agent Loop

┌─────────────────────────────────────────────────────────────┐ │ AGENT LOOP │ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ Think │───▶│ Decide │───▶│ Act │ │ │ │ (Reason) │ │ (Plan) │ │ (Execute)│ │ │ └──────────┘ └──────────┘ └──────────┘ │ │ ▲ │ │ │ │ ┌──────────┐ │ │ │ └─────────│ Observe │◀─────────┘ │ │ │ (Result) │ │ │ └──────────┘ │ └─────────────────────────────────────────────────────────────┘

class AgentLoop: def __init__(self, llm, tools, max_iterations=50): self.llm = llm self.tools = {t.name: t for t in tools} self.max_iterations = max_iterations self.history = [] def run(self, task: str) -> str: self.history.append({"role": "user", "content": task}) for i in range(self.max_iterations): # Think: Get LLM response with tool options response = self.llm.chat( messages=self.history, tools=self._format_tools(), tool_choice="auto" ) # Decide: Check if agent wants to use a tool if response.tool_calls: for tool_call in response.tool_calls: # Act: Execute the tool result = self._execute_tool(tool_call) # Observe: Add result to history self.history.append({ "role": "tool", "tool_call_id": tool_call.id, "content": str(result) }) else: # No more tool calls = task complete return response.content return "Max iterations reached" def _execute_tool(self, tool_call) -> Any: tool = self.tools[tool_call.name] args = json.loads(tool_call.arguments) return tool.execute(**args)

1.2 Multi-Model Architecture

class MultiModelAgent: """ Use different models for different purposes: - Fast model for planning - Powerful model for complex reasoning - Specialized model for code generation """ def __init__(self): self.models = { "fast": "gpt-3.5-turbo", # Quick decisions "smart": "gpt-4-turbo", # Complex reasoning "code": "claude-3-sonnet", # Code generation } def select_model(self, task_type: str) -> str: if task_type == "planning": return self.models["fast"] elif task_type == "analysis": return self.models["smart"] elif task_type == "code": return self.models["code"] return self.models["smart"]

2. Tool Design Patterns

2.1 Tool Schema

class Tool: """Base class for agent tools""" @property def schema(self) -> dict: """JSON Schema for the tool""" return { "name": self.name, "description": self.description, "parameters": { "type": "object", "properties": self._get_parameters(), "required": self._get_required() } } def execute(self, **kwargs) -> ToolResult: """Execute the tool and return result""" raise NotImplementedError class ReadFileTool(Tool): name = "read_file" description = "Read the contents of a file from the filesystem" def _get_parameters(self): return { "path": { "type": "string", "description": "Absolute path to the file" }, "start_line": { "type": "integer", "description": "Line to start reading from (1-indexed)" }, "end_line": { "type": "integer", "description": "Line to stop reading at (inclusive)" } } def _get_required(self): return ["path"] def execute(self, path: str, start_line: int = None, end_line: int = None) -> ToolResult: try: with open(path, 'r') as f: lines = f.readlines() if start_line and end_line: lines = lines[start_line-1:end_line] return ToolResult( success=True,

autonomous-agent-patterns

What it does

Category

Repository

Last updated

Installation Guide

How to use autonomous-agent-patterns 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

🕹️ Autonomous Agent Patterns

When to Use This Skill

1. Core Agent Architecture

1.1 Agent Loop

1.2 Multi-Model Architecture

2. Tool Design Patterns

2.1 Tool Schema

`Stakeholder Communication`

`Implementation Guide`

`Best Practices`

`When to Use This`

`Learning Path`

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