Padrões de design para construir agentes de codificação autônomos. Cobre integração de tools, sistemas de permissão, automação de browser e workflows com intervenção humana. Use ao construir agentes IA, projetar APIs de tools, implementar sistemas de permissão ou criar assistentes de codificação autônomos.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Padrões de design para construir agentes de codificação autônomos, inspirado em Cline e OpenAI Codex.
Use esta skill quando:
┌─────────────────────────────────────────────────────────────┐
│ 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)
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"]
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}"
)
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"
}
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)"
)
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
}
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 cmd for danger in ["rm -rf", "sudo", "chmod"]):
return "HIGH"
return "MEDIUM"
class SandboxedExecution:
"""
Execute code/commands in isolated environment
"""
def __init__(self, workspace_dir: str):
self.workspace = workspace_dir
self.allowed_commands = ["npm", "python", "node", "git", "ls", "cat"]
self.blocked_paths = ["/etc", "/usr", "/bin", os.path.expanduser("~")]
def validate_path(self, path: str) -> bool:
"""Ensure path is within workspace"""
real_path = os.path.realpath(path)
workspace_real = os.path.realpath(self.workspace)
return real_path.startswith(workspace_real)
def validate_command(self, command: str) -> bool:
"""Check if command is allowed"""
cmd_parts = shlex.split(command)
if not cmd_parts:
return False
base_cmd = cmd_parts[0]
return base_cmd in self.allowed_commands
def execute_sandboxed(self, command: str) -> ToolResult:
if not self.validate_command(command):
return ToolResult(
success=False,
error=f"Command not allowed: {command}"
)
# Execute in isolated environment
result = subprocess.run(
command,
shell=True,
cwd=self.workspace,
capture_output=True,
timeout=30,
env={
**os.environ,
"HOME": self.workspace, # Isolate home directory
}
)
return ToolResult(
success=result.returncode == 0,
output=result.stdout.decode(),
error=result.stderr.decode() if result.returncode != 0 else None
)
class BrowserTool:
"""
Browser automation for agents using Playwright/Puppeteer.
Enables visual debugging and web testing.
"""
def __init__(self, headless: bool = True):
self.browser = None
self.page = None
self.headless = headless
async def open_url(self, url: str) -> ToolResult:
"""Navigate to URL and return page info"""
if not self.browser:
self.browser = await playwright.chromium.launch(headless=self.headless)
self.page = await self.browser.new_page()
await self.page.goto(url)
# Capture state
screenshot = await self.page.screenshot(type='png')
title = await self.page.title()
return ToolResult(
success=True,
output=f"Loaded: {title}",
metadata={
"screenshot": base64.b64encode(screenshot).decode(),
"url": self.page.url
}
)
async def click(self, selector: str) -> ToolResult:
"""Click on an element"""
try:
await self.page.click(selector, timeout=5000)
await self.page.wait_for_load_state("networkidle")
screenshot = await self.page.screenshot()
return ToolResult(
success=True,
output=f"Clicked: {selector}",
metadata={"screenshot": base64.b64encode(screenshot).decode()}
)
except TimeoutError:
return ToolResult(
success=False,
error=f"Element not found: {selector}"
)
async def type_text(self, selector: str, text: str) -> ToolResult:
"""Type text into an input"""
await self.page.fill(selector, text)
return ToolResult(success=True, output=f"Typed into {selector}")
async def get_page_content(self) -> ToolResult:
"""Get accessible text content of the page"""
content = await self.page.evaluate("""
() => {
// Get visible text
const walker = document.createTreeWalker(
document.body,
NodeFilter.SHOW_TEXT,
null,
false
);
let text = '';
while (walker.nextNode()) {
const node = walker.currentNode;
if (node.textContent.trim()) {
text += node.textContent.trim() + '\\n';
}
}
return text;
}
""")
return ToolResult(success=True, output=content)
class VisualAgent:
"""
Agent that uses screenshots to understand web pages.
Can identify elements visually without selectors.
"""
def __init__(self, llm, browser):
self.llm = llm
self.browser = browser
async def describe_page(self) -> str:
"""Use vision model to describe current page"""
screenshot = await self.browser.screenshot()
response = self.llm.chat([
{
"role": "user",
"content": [
{"type": "text", "text": "Describe this webpage. List all interactive elements you see."},
{"type": "image", "data": screenshot}
]
}
])
return response.content
async def find_and_click(self, description: str) -> ToolResult:
"""Find element by visual description and click it"""
screenshot = await self.browser.screenshot()
# Ask vision model to find element
response = self.llm.chat([
{
"role": "user",
"content": [
{
"type": "text",
"text": f"""
Find the element matching: "{description}"
Return the approximate coordinates as JSON: {{"x": number, "y": number}}
"""
},
{"type": "image", "data": screenshot}
]
}
])
coords = json.loads(response.content)
await self.browser.page.mouse.click(coords["x"], coords["y"])
return ToolResult(success=True, output=f"Clicked at ({coords['x']}, {coords['y']})")
class ContextManager:
"""
Manage context provided to the agent.
Inspired by Cline's @-mention patterns.
"""
def __init__(self, workspace: str):
self.workspace = workspace
self.context = []
def add_file(self, path: str) -> None:
"""@file - Add file contents to context"""
with open(path, 'r') as f:
content = f.read()
self.context.append({
"type": "file",
"path": path,
"content": content
})
def add_folder(self, path: str, max_files: int = 20) -> None:
"""@folder - Add all files in folder"""
for root, dirs, files in os.walk(path):
for file in files[:max_files]:
file_path = os.path.join(root, file)
self.add_file(file_path)
def add_url(self, url: str) -> None:
"""@url - Fetch and add URL content"""
response = requests.get(url)
content = html_to_markdown(response.text)
self.context.append({
"type": "url",
"url": url,
"content": content
})
def add_problems(self, diagnostics: list) -> None:
"""@problems - Add IDE diagnostics"""
self.context.append({
"type": "diagnostics",
"problems": diagnostics
})
def format_for_prompt(self) -> str:
"""Format all context for LLM prompt"""
parts = []
for item in self.context:
if item["type"] == "file":
parts.append(f"## File: {item['path']}\n```\n{item['content']}\n```")
elif item["type"] == "url":
parts.append(f"## URL: {item['url']}\n{item['content']}")
elif item["type"] == "diagnostics":
parts.append(f"## Problems:\n{json.dumps(item['problems'], indent=2)}")
return "\n\n".join(parts)
class CheckpointManager:
"""
Save and restore agent state for long-running tasks.
"""
def __init__(self, storage_dir: str):
self.storage_dir = storage_dir
os.makedirs(storage_dir, exist_ok=True)
def save_checkpoint(self, session_id: str, state: dict) -> str:
"""Save current agent state"""
checkpoint = {
"timestamp": datetime.now().isoformat(),
"session_id": session_id,
"history": state["history"],
"context": state["context"],
"workspace_state": self._capture_workspace(state["workspace"]),
"metadata": state.get("metadata", {})
}
path = os.path.join(self.storage_dir, f"{session_id}.json")
with open(path, 'w') as f:
json.dump(checkpoint, f, indent=2)
return path
def restore_checkpoint(self, checkpoint_path: str) -> dict:
"""Restore agent state from checkpoint"""
with open(checkpoint_path, 'r') as f:
checkpoint = json.load(f)
return {
"history": checkpoint["history"],
"context": checkpoint["context"],
"workspace": self._restore_workspace(checkpoint["workspace_state"]),
"metadata": checkpoint["metadata"]
}
def _capture_workspace(self, workspace: str) -> dict:
"""Capture relevant workspace state"""
# Git status, file hashes, etc.
return {
"git_ref": subprocess.getoutput(f"cd {workspace} && git rev-parse HEAD"),
"git_dirty": subprocess.getoutput(f"cd {workspace} && git status --porcelain")
}
from mcp import Server, Tool
class MCPAgent:
"""
Agent that can dynamically discover and use MCP tools.
'Add a tool that...' pattern from Cline.
"""
def __init__(self, llm):
self.llm = llm
self.mcp_servers = {}
self.available_tools = {}
def connect_server(self, name: str, config: dict) -> None:
"""Connect to an MCP server"""
server = Server(config)
self.mcp_servers[name] = server
# Discover tools
tools = server.list_tools()
for tool in tools:
self.available_tools[tool.name] = {
"server": name,
"schema": tool.schema
}
async def create_tool(self, description: str) -> str:
"""
Create a new MCP server based on user description.
'Add a tool that fetches Jira tickets'
"""
# Generate MCP server code
code = self.llm.generate(f"""
Create a Python MCP server with a tool that does:
{description}
Use the FastMCP framework. Include proper error handling.
Return only the Python code.
""")
# Save and install
server_name = self._extract_name(description)
path = f"./mcp_servers/{server_name}/server.py"
with open(path, 'w') as f:
f.write(code)
# Hot-reload
self.connect_server(server_name, {"path": path})
return f"Created tool: {server_name}"
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Especialista em construir experiências 3D para a web - Three.js, React Three Fiber, Spline, WebGL e cenas 3D interativas. Cobre configuradores de produtos, portfólios 3D, websites imersivos e adição de profundidade às experiências web. Use quando: website 3D, three.js, WebGL, react three fiber, experiência 3D.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Quando o usuário quer planejar, projetar ou implementar um teste A/B ou experimento. Também use quando o usuário menciona "teste A/B", "split test", "experimento", "testar essa mudança", "copy variante", "teste multivariado" ou "hipótese". Para implementação de rastreamento, veja analytics-tracking.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Auditar e melhorar a acessibilidade web seguindo as diretrizes WCAG 2.1. Use quando solicitado para "melhorar acessibilidade", "auditoria a11y", "conformidade WCAG", "suporte a leitor de tela", "navegação por teclado" ou "tornar acessível".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Testes e benchmarking de agentes LLM incluindo testes comportamentais, avaliação de capacidades, métricas de confiabilidade e monitoramento em produção—onde até os melhores agentes alcançam menos de 50% em benchmarks do mundo real. Use quando: testes de agentes, avaliação de agentes, benchmark de agentes, confiabilidade de agentes, teste de agentes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Criar, gerenciar e orquestrar agentes de IA usando o CLI AI Maestro. Use quando o usuário pedir para "criar agente", "listar agentes", "deletar agente", "hibernar agente", "despertar agente", "instalar plugin", "mostrar agente", "reiniciar agente" ou qualquer tarefa de gerenciamento do ciclo de vida do agente.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Gerencie múltiplos agentes CLI locais via sessões tmux (iniciar/parar/monitorar/atribuir) com agendamento compatível com cron.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.