智能体发现：让自主智能体发现并使用 AIsa

curl -s https://aisa.one/.well-known/ai-plugin.json | jq .

import requests

# Step 1: Discover — fetch the agent card
card = requests.get("https://aisa.one/.well-known/agent-card.json").json()

# Step 2: Find a skill by tag
target_tag = "llm"
matching = [s for s in card["skills"] if target_tag in s.get("tags", [])]
if not matching:
    raise RuntimeError(f"No skill found with tag '{target_tag}'")

skill = matching[0]
print(f"Selected skill: {skill['name']} ({skill['id']})")

# Step 3: Invoke — call the API using the base URL from the card
response = requests.post(
    f"{card['url']}/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_AISA_API_KEY",
        "Content-Type": "application/json",
    },
    json={
        "model": "gpt-5.4-mini",
        "messages": [
            {"role": "user", "content": "Summarize the A2A protocol in two sentences."}
        ],
    },
)

result = response.json()
print(result["choices"][0]["message"]["content"])

Authorization: Bearer YOUR_AISA_API_KEY

def find_skills_by_tags(card, required_tags):
    """Return skills that match ALL required tags."""
    return [
        skill for skill in card["skills"]
        if all(tag in skill.get("tags", []) for tag in required_tags)
    ]

# Find skills for financial research
finance_skills = find_skills_by_tags(card, ["finance", "stocks"])
# Returns: [MarketPulse Financial Data]

# Collect all examples with their skill IDs
example_index = []
for skill in card["skills"]:
    for example in skill.get("examples", []):
        example_index.append({"text": example, "skill_id": skill["id"]})

# Use an embedding model to find the closest match to the user's query
# user_query = "What's the stock price of Apple?"
# → Matches MarketPulse skill via "Get the current stock price for AAPL"