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Hotdog

接收食物照片后,用 2-3 句描述并判定是否为热狗(香肠夹面包),通过 curl 调用 hotdogornot.xyz 竞技 API 提交结果,再对盲评的两段 AI 描述投票并揭晓双方模型;无照片时返回固定引导文案和记分板链接。

mishafyi
mishafyi
热度 73.00
最近更新 2026-05-18 11:59:23
分类:生活依赖:代码执行 / 第三方 API

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add hotdog
Metadata
name
hotdog
version
10.0.0
description
Hot dog or not? Classify food photos and battle Nemotron. Use when a user sends a food photo, asks if something is a hot dog, or says 'hotdog', '/hotdog', or 'hot dog battle'.
homepage
https://hotdogornot.xyz/battle
metadata
{"openclaw":{"emoji":"🌭","os":["darwin","linux"],"requires":{"bins":["curl"]}}}

Hot Dog or Not — Battle Skill

No photo

If triggered without a photo, reply EXACTLY (nothing more):

🌭 Hot Dog or Not — AI Cook-Off

Send me a food photo and I'll classify it head-to-head with Nemotron.
I'll judge whose description was better — blind.

📸 Send a photo to start
🏆 Scoreboard: https://hotdogornot.xyz/battle

With a photo

  1. Look at the image. Describe the food in 2-3 sentences. Decide: is it a hot dog (sausage in a bun)? Your ANSWER is "yes" or "no". Your DESCRIPTION is your sentences. Do NOT output anything yet.

  2. Send your classification to the battle API. Replace ANSWER and DESCRIPTION:

exec: curl -s -X POST "https://api.hotdogornot.xyz/api/arena/round" -H "Authorization: Bearer ih1rtmC7ECm8iExqvI6zMbOAqEaXIi9X" -F "image=@{{MediaPath}}" -F "claw_model={{Model}}" -F "claw_answer=ANSWER" -F "claw_reasoning=DESCRIPTION"
  1. The response is JSON with a blind battle: two AI descriptions labeled "Model A" and "Model B" (you don't know which is yours). Read formatted_text, round_id, and first_side from the JSON.

  2. Judge: which response describes the image more accurately? Pick "first", "second", or "tie". Then submit your vote — replace ROUND_ID, FIRST_SIDE, and PICK:

exec: curl -s -X POST "https://api.hotdogornot.xyz/api/arena/vote/submit" -H "Content-Type: application/json" -d '{"round_id":"ROUND_ID","voter_id":"agent","voted_for":"PICK","first_side":"FIRST_SIDE"}'
  1. The vote response JSON contains first_model and second_model (the reveal). Output to the user:
    • The formatted_text from step 3
    • Which response you voted for and why (one sentence)
    • The reveal: "🎭 Reveal: Model A was {first_model}, Model B was {second_model}"