Utilizzo degli Agent Skills con l'API

client = anthropic.Anthropic()

response = client.beta.messages.create(
    model="claude-opus-4-8",
    max_tokens=4096,
    betas=["code-execution-2025-08-25", "skills-2025-10-02"],
    container={
        "skills": [{"type": "anthropic", "skill_id": "pptx", "version": "latest"}]
    },
    messages=[
        {"role": "user", "content": "Create a presentation about renewable energy"}
    ],
    tools=[{"type": "code_execution_20250825", "name": "code_execution"}],
)

client = anthropic.Anthropic()

# Passo 1: Usa una Skill per creare un file
response = client.beta.messages.create(
    model="claude-opus-4-8",
    max_tokens=4096,
    betas=["code-execution-2025-08-25", "skills-2025-10-02"],
    container={
        "skills": [{"type": "anthropic", "skill_id": "xlsx", "version": "latest"}]
    },
    messages=[
        {
            "role": "user",
            "content": "Create an Excel file with a simple budget spreadsheet",
        }
    ],
    tools=[{"type": "code_execution_20250825", "name": "code_execution"}],
)


# Passo 2: Estrai gli ID dei file dalla risposta
def extract_file_ids(response):
    file_ids = []
    for item in response.content:
        if item.type == "bash_code_execution_tool_result":
            content_item = item.content
            if content_item.type == "bash_code_execution_result":
                # lista con tipo concreto: List[BashCodeExecutionOutputBlock]
                for file in content_item.content:
                    file_ids.append(file.file_id)
    return file_ids


# Passo 3: Scarica il file usando la Files API
for file_id in extract_file_ids(response):
    file_metadata = client.beta.files.retrieve_metadata(file_id=file_id)
    file_content = client.beta.files.download(file_id=file_id)

    # Passo 4: Salva su disco
    file_content.write_to_file(file_metadata.filename)
    print(f"Downloaded: {file_metadata.filename}")

client = anthropic.Anthropic()
file_id = "file_abc123"
# Ottieni i metadati del file
file_info = client.beta.files.retrieve_metadata(file_id=file_id)
print(f"Filename: {file_info.filename}, Size: {file_info.size_bytes} bytes")

# Elenca tutti i file
files = client.beta.files.list()
for file in files.data:
    print(f"{file.filename} - {file.created_at}")

# Elimina un file
client.beta.files.delete(file_id=file_id)

# La prima richiesta crea il container
response1 = client.beta.messages.create(
    model="claude-opus-4-8",
    max_tokens=4096,
    betas=["code-execution-2025-08-25", "skills-2025-10-02"],
    container={
        "skills": [{"type": "anthropic", "skill_id": "xlsx", "version": "latest"}]
    },
    messages=[{"role": "user", "content": "Analyze this sales data"}],
    tools=[{"type": "code_execution_20250825", "name": "code_execution"}],
)

# Continua la conversazione con lo stesso container
messages = [
    {"role": "user", "content": "Analyze this sales data"},
    {"role": "assistant", "content": response1.content},
    {"role": "user", "content": "What was the total revenue?"},
]

response2 = client.beta.messages.create(
    model="claude-opus-4-8",
    max_tokens=4096,
    betas=["code-execution-2025-08-25", "skills-2025-10-02"],
    container={
        "id": response1.container.id,  # Reuse container
        "skills": [{"type": "anthropic", "skill_id": "xlsx", "version": "latest"}],
    },
    messages=messages,
    tools=[{"type": "code_execution_20250825", "name": "code_execution"}],
)

messages = [{"role": "user", "content": "Process this large dataset"}]
max_retries = 10

response = client.beta.messages.create(
    model="claude-opus-4-8",
    max_tokens=4096,
    betas=["code-execution-2025-08-25", "skills-2025-10-02"],
    container={
        "skills": [
            {
                "type": "custom",
                "skill_id": "skill_01AbCdEfGhIjKlMnOpQrStUv",
                "version": "latest",
            }
        ]
    },
    messages=messages,
    tools=[{"type": "code_execution_20250825", "name": "code_execution"}],
)

# Gestisci pause_turn per operazioni lunghe
for i in range(max_retries):
    if response.stop_reason != "pause_turn":
        break

    messages.append({"role": "assistant", "content": response.content})
    response = client.beta.messages.create(
        model="claude-opus-4-8",
        max_tokens=4096,
        betas=["code-execution-2025-08-25", "skills-2025-10-02"],
        container={
            "id": response.container.id,
            "skills": [
                {
                    "type": "custom",
                    "skill_id": "skill_01AbCdEfGhIjKlMnOpQrStUv",
                    "version": "latest",
                }
            ],
        },
        messages=messages,
        tools=[{"type": "code_execution_20250825", "name": "code_execution"}],
    )

response = client.beta.messages.create(
    model="claude-opus-4-8",
    max_tokens=4096,
    betas=["code-execution-2025-08-25", "skills-2025-10-02"],
    container={
        "skills": [
            {"type": "anthropic", "skill_id": "xlsx", "version": "latest"},
            {"type": "anthropic", "skill_id": "pptx", "version": "latest"},
            {
                "type": "custom",
                "skill_id": "skill_01AbCdEfGhIjKlMnOpQrStUv",
                "version": "latest",
            },
        ]
    },
    messages=[
        {"role": "user", "content": "Analyze sales data and create a presentation"}
    ],
    tools=[{"type": "code_execution_20250825", "name": "code_execution"}],
)

# Opzione 1: Carica file individuali (un flag --file per ogni file)
ant beta:skills create \
  --display-title "Financial Analysis" \
  --file financial_skill/SKILL.md \
  --file financial_skill/analyze.py \
  --beta skills-2025-10-02

# Opzione 2: Carica un archivio zip
ant beta:skills create \
  --display-title "Financial Analysis" \
  --file financial_analysis_skill.zip \
  --beta skills-2025-10-02

# Elenca tutte le Skill
ant beta:skills list

# Elenca solo le Skill personalizzate
ant beta:skills list --source custom

ant beta:skills retrieve \
  --skill-id skill_01AbCdEfGhIjKlMnOpQrStUv

# Passaggio 1: Elimina tutte le versioni
ant beta:skills:versions list \
  --skill-id skill_01AbCdEfGhIjKlMnOpQrStUv \
  --transform version --raw-output \
  | while read -r VERSION; do
      ant beta:skills:versions delete \
        --skill-id skill_01AbCdEfGhIjKlMnOpQrStUv \
        --version "$VERSION" >/dev/null
    done

# Passaggio 2: Elimina la Skill
ant beta:skills delete \
  --skill-id skill_01AbCdEfGhIjKlMnOpQrStUv >/dev/null

# Crea una nuova versione
VERSION_NUMBER=$(ant beta:skills:versions create \
  --skill-id skill_01AbCdEfGhIjKlMnOpQrStUv \
  --file updated_skill/SKILL.md \
  --transform version --raw-output)

# Usa una versione specifica
ant beta:messages create \
  --beta code-execution-2025-08-25 \
  --beta skills-2025-10-02 <<YAML
model: claude-opus-4-8
max_tokens: 4096
container:
  skills:
    - type: custom
      skill_id: skill_01AbCdEfGhIjKlMnOpQrStUv
      version: $VERSION_NUMBER
messages:
  - role: user
    content: Use updated Skill
tools:
  - type: code_execution_20250825
    name: code_execution
YAML

# Usa la versione più recente
ant beta:messages create \
  --beta code-execution-2025-08-25 \
  --beta skills-2025-10-02 <<'YAML'
model: claude-opus-4-8
max_tokens: 4096
container:
  skills:
    - type: custom
      skill_id: skill_01AbCdEfGhIjKlMnOpQrStUv
      version: latest
messages:
  - role: user
    content: Use latest Skill version
tools:
  - type: code_execution_20250825
    name: code_execution
YAML

# Crea una Skill personalizzata per l'analisi DCF
from anthropic.lib import files_from_dir

dcf_skill = client.beta.skills.create(
    display_title="DCF Analysis",
    files=files_from_dir("/path/to/dcf_skill"),
)

# Usa con Excel per creare un modello finanziario
response = client.beta.messages.create(
    model="claude-opus-4-8",
    max_tokens=4096,
    betas=["code-execution-2025-08-25", "skills-2025-10-02"],
    container={
        "skills": [
            {"type": "anthropic", "skill_id": "xlsx", "version": "latest"},
            {"type": "custom", "skill_id": dcf_skill.id, "version": "latest"},
        ]
    },
    messages=[
        {
            "role": "user",
            "content": "Build a DCF valuation model for a SaaS company with the attached financials",
        }
    ],
    tools=[{"type": "code_execution_20250825", "name": "code_execution"}],
)
print(response)

# La prima richiesta crea la cache
response1 = client.beta.messages.create(
    model="claude-opus-4-8",
    max_tokens=4096,
    betas=[
        "code-execution-2025-08-25",
        "skills-2025-10-02",
        "prompt-caching-2024-07-31",
    ],
    container={
        "skills": [{"type": "anthropic", "skill_id": "xlsx", "version": "latest"}]
    },
    messages=[{"role": "user", "content": "Analyze sales data"}],
    tools=[{"type": "code_execution_20250825", "name": "code_execution"}],
)

# Aggiungere/rimuovere Skill invalida la cache
response2 = client.beta.messages.create(
    model="claude-opus-4-8",
    max_tokens=4096,
    betas=[
        "code-execution-2025-08-25",
        "skills-2025-10-02",
        "prompt-caching-2024-07-31",
    ],
    container={
        "skills": [
            {"type": "anthropic", "skill_id": "xlsx", "version": "latest"},
            {
                "type": "anthropic",
                "skill_id": "pptx",
                "version": "latest",
            },  # Cache miss
        ]
    },
    messages=[{"role": "user", "content": "Create a presentation"}],
    tools=[{"type": "code_execution_20250825", "name": "code_execution"}],
)

client = anthropic.Anthropic()

try:
    response = client.beta.messages.create(
        model="claude-opus-4-8",
        max_tokens=4096,
        betas=["code-execution-2025-08-25", "skills-2025-10-02"],
        container={
            "skills": [
                {
                    "type": "custom",
                    "skill_id": "skill_01AbCdEfGhIjKlMnOpQrStUv",
                    "version": "latest",
                }
            ]
        },
        messages=[{"role": "user", "content": "Process data"}],
        tools=[{"type": "code_execution_20250825", "name": "code_execution"}],
    )
except anthropic.BadRequestError as e:
    if "skill" in str(e):
        print(f"Skill error: {e}")
        # Gestisci gli errori specifici della skill
    else:
        raise