Passo-a-passo detalhado do skill, referenciando as fases cognitivas:
1SENSE — Definir tarefa e ferramentas
```python
# Exemplo: agente que responde perguntas sobre dados de negócio
TOOLS = {
"sql_query": lambda q: execute_sql(q),
"calculate": lambda expr: eval(expr), # usar com sandbox
"search_web": lambda query: web_search(query),
}
TASK = "Qual foi o crescimento de receita MoM de janeiro a março de 2026?"
```
2RECOMMEND — Implementar ReAct loop
```python
import re
from anthropic import Anthropic
client = Anthropic()
SYSTEM_PROMPT = '''Você é um agente analítico. Para cada passo, siga o formato:
Thought: raciocine sobre o que precisa fazer
Action: tool_name(args)
Ferramentas disponíveis:
sql_query(query: str) -> str: executa SQL no banco de dados
calculate(expression: str) -> float: calcula expressão matemática
'''
def react_loop(task: str, max_iterations: int = 10) -> str:
messages = [{"role": "user", "content": f"Tarefa: {task}"}]
for iteration in range(max_iterations):
response = client.messages.create(
model="claude-opus-4",
max_tokens=2000,
system=SYSTEM_PROMPT,
messages=messages,
)
content = response.content[0].text
messages.append({"role": "assistant", "content": content})
# Extrair Action
action_match = re.search(r'Action: (\w+)\((.*)\)', content, re.DOTALL)
if "Final Answer:" in content:
return content.split("Final Answer:")[1].strip()
if not action_match:
return content # sem ação, resposta direta
tool_name, tool_args = action_match.group(1), action_match.group(2).strip()
# Executar ferramenta
if tool_name in TOOLS:
try:
result = TOOLS[tool_name](tool_args.strip('"'))
observation = f"Observation: {result}"
except Exception as e:
observation = f"Observation: Error — {e}. Try a different approach."
else:
observation = f"Observation: Tool '{tool_name}' not found."
messages.append({"role": "user", "content": observation})
print(f"[Iteration {iteration + 1}] {tool_name}({tool_args[:50]}) → {str(result)[:100]}")
return "Max iterations reached without final answer."
```
3RECOMMEND — Extended Thinking (Claude)
```python
# Para problemas muito complexos, ativar raciocínio interno do Claude
response = client.messages.create(
model="claude-opus-4",
max_tokens=16000,
thinking={"type": "enabled", "budget_tokens": 10000},
messages=[{"role": "user", "content": complex_task}],
)
# thinking_block conterá o raciocínio interno, text_block a resposta final
```
4EVALUATE — Detectar e tratar loops
```python
# Detectar ação repetida
action_history = []
if action in action_history[-3:]:
return "Loop detectado: encerrando com melhor resultado disponível"
action_history.append(action)
```
5REFLECT — Log estruturado para observabilidade
```python
# Salvar trace completo para debugging
trace = {
"task": task,
"iterations": len(messages) // 2,
"steps": [{"thought": ..., "action": ..., "observation": ...}],
"result": final_answer,
"tokens_used": response.usage.input_tokens + response.usage.output_tokens,
}
```
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