hotdog
热狗还是不热狗?对食物照片进行分类并与 Nemotron 进行战斗。当用户发送食物照片、询问某物是否是热狗或说“热狗”、“/热狗”或“热狗大战”时使用。
安装 / 下载方式
TotalClaw CLI推荐
totalclaw install totalclaw:totalclaw~mishafyi-hotdogcURL直接下载,无需登录
curl -fsSL https://skills.taituai.com/api/skills/totalclaw%3Atotalclaw~mishafyi-hotdog/file -o mishafyi-hotdog.md## 概述(中文)
热狗还是不热狗?对食物照片进行分类并与 Nemotron 进行战斗。当用户发送食物照片、询问某物是否是热狗或说“热狗”、“/热狗”或“热狗大战”时使用。
## 原文
# 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:
```bash
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"
```
3. 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.
4. Judge: which response describes the image more accurately? Pick "first", "second", or "tie". Then submit your vote — replace ROUND_ID, FIRST_SIDE, and PICK:
```bash
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"}'
```
5. 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}"