autonomous-agent-patterns
Provides architectural patterns and guidance for building autonomous coding agents and tool-calling systems.
Install
mkdir -p .claude/skills/autonomous-agent-patterns && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/766" && unzip -o skill.zip -d .claude/skills/autonomous-agent-patterns && rm skill.zipInstalls to .claude/skills/autonomous-agent-patterns
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use when building AI agents, designing tool APIs, implementing permission systems, or creating autonomous coding assistants.Key capabilities
- →Define agent loops with think, decide, and act phases
- →Implement multi-model architectures for specialized tasks
- →Create tool schemas for file and terminal operations
- →Design permission systems for human-in-the-loop workflows
- →Integrate MCP servers for dynamic tool discovery
How it works
The skill provides patterns for building autonomous agents that iterate through planning, tool execution, and observation. It includes code templates for tool schema definition, file editing with conflict detection, and state checkpointing.
Inputs & outputs
When to use autonomous-agent-patterns
- →Designing agent tool APIs
- →Implementing permission systems
- →Building autonomous coding assistants
- →Developing browser automation
About this skill
🕹️ Autonomous Agent Patterns
Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex.
When to Use This Skill
Use this skill when:
- Building autonomous AI agents
- Designing tool/function calling APIs
- Implementing permission and approval systems
- Creating browser automation for agents
- Designing human-in-the-loop workflows
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,
output="".join(lines)
)
except FileNotFoundError:
return ToolResult(
success=False,
error=f"File not found: {path}"
)
2.2 Essential Agent Tools
CODING_AGENT_TOOLS = {
# File operations
"read_file": "Read file contents",
"write_file": "Create or overwrite a file",
"edit_file": "Make targeted edits to a file",
"list_directory": "List files and folders",
"search_files": "Search for files by pattern",
# Code understanding
"search_code": "Search for code patterns (grep)",
"get_definition": "Find function/class definition",
"get_references": "Find all references to a symbol",
# Terminal
"run_command": "Execute a shell command",
"read_output": "Read command output",
"send_input": "Send input to running command",
# Browser (optional)
"open_browser": "Open URL in browser",
"click_element": "Click on page element",
"type_text": "Type text into input",
"screenshot": "Capture screenshot",
# Context
"ask_user": "Ask the user a question",
"search_web": "Search the web for information"
}
2.3 Edit Tool Design
class EditFileTool(Tool):
"""
Precise file editing with conflict detection.
Uses search/replace pattern for reliable edits.
"""
name = "edit_file"
description = "Edit a file by replacing specific content"
def execute(
self,
path: str,
search: str,
replace: str,
expected_occurrences: int = 1
) -> ToolResult:
"""
Args:
path: File to edit
search: Exact text to find (must match exactly, including whitespace)
replace: Text to replace with
expected_occurrences: How many times search should appear (validation)
"""
with open(path, 'r') as f:
content = f.read()
# Validate
actual_occurrences = content.count(search)
if actual_occurrences != expected_occurrences:
return ToolResult(
success=False,
error=f"Expected {expected_occurrences} occurrences, found {actual_occurrences}"
)
if actual_occurrences == 0:
return ToolResult(
success=False,
error="Search text not found in file"
)
# Apply edit
new_content = content.replace(search, replace)
with open(path, 'w') as f:
f.write(new_content)
return ToolResult(
success=True,
output=f"Replaced {actual_occurrences} occurrence(s)"
)
3. Permission & Safety Patterns
3.1 Permission Levels
class PermissionLevel(Enum):
# Fully automatic - no user approval needed
AUTO = "auto"
# Ask once per session
ASK_ONCE = "ask_once"
# Ask every time
ASK_EACH = "ask_each"
# Never allow
NEVER = "never"
PERMISSION_CONFIG = {
# Low risk - can auto-approve
"read_file": PermissionLevel.AUTO,
"list_directory": PermissionLevel.AUTO,
"search_code": PermissionLevel.AUTO,
# Medium risk - ask once
"write_file": PermissionLevel.ASK_ONCE,
"edit_file": PermissionLevel.ASK_ONCE,
# High risk - ask each time
"run_command": PermissionLevel.ASK_EACH,
"delete_file": PermissionLevel.ASK_EACH,
# Dangerous - never auto-approve
"sudo_command": PermissionLevel.NEVER,
"format_disk": PermissionLevel.NEVER
}
3.2 Approval UI Pattern
class ApprovalManager:
def __init__(self, ui, config):
self.ui = ui
self.config = config
self.session_approvals = {}
def request_approval(self, tool_name: str, args: dict) -> bool:
level = self.config.get(tool_name, PermissionLevel.ASK_EACH)
if level == PermissionLevel.AUTO:
return True
if level == PermissionLevel.NEVER:
self.ui.show_error(f"Tool '{tool_name}' is not allowed")
return False
if level == PermissionLevel.ASK_ONCE:
if tool_name in self.session_approvals:
return self.session_approvals[tool_name]
# Show approval dialog
approved = self.ui.show_approval_dialog(
tool=tool_name,
args=args,
risk_level=self._assess_risk(tool_name, args)
)
if level == PermissionLevel.ASK_ONCE:
self.session_approvals[tool_name] = approved
return approved
def _assess_risk(self, tool_name: str, args: dict) -> str:
"""Analyze specific call for risk level"""
if tool_name == "run_command":
cmd = args.get("command", "")
if any(danger in
---
*Content truncated.*
When not to use it
- →Simple script automation without decision-making
- →Tasks requiring no external tool interaction
Prerequisites
Limitations
- →Requires manual implementation of sandbox environments
- →Max iterations must be configured to prevent infinite loops
How it compares
It provides a modular framework for agent design that includes built-in safety patterns and permission levels, rather than just raw LLM prompting.
Compared to similar skills
autonomous-agent-patterns side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| autonomous-agent-patterns (this skill) | 4 | 6mo | Review | Intermediate |
| nonstop-agent | 0 | 7mo | Review | Beginner |
| llama-factory | 15 | 8mo | No flags | Advanced |
| crewai | 4 | 6mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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