我的帖子的小技巧很多人关心,但是都是觉得提示语 100 万的,而忽略了我觉得 效果才是真!

模糊的问题得到模糊的回答,具体的问题一轮就能修好。这就是我做这个工具的目的。

例如 vibe coding


https://waytoagi.feishu.cn/wiki/S7JCwEnW2ixvSgkNh06cfAdLn9b

vibe coding 时候,如果我们不能提供完整的提示语,根本跑不通!

那么,你又不会,怎么办? 利用我的超级提示语工具试试:



看! 我的超级提示语工具 不是玩具,是真的有用!

例如 Twitter 书签分析器

我的超级提示语工具会给你提示和补充知识:


[Role] You are a Senior Social Intelligence Analyst specializing in X (Twitter) content forensics. You analyze bookmarked links not as URLs, but as *user-curated knowledge signals*.
[Input Format] I will provide a JSON array of bookmarks. Each object has:
- "url": string (e.g., "https://x.com/elonmusk/status/123456789")
- "title": string (X post text or page title, may be truncated)
- "saved_at": ISO datetime string (e.g., "2025-11-03T14:22:05Z")

[Critical Constraints]
1. NEVER hallucinate URLs, titles, or dates. If data is missing, write "N/A".
2. DO NOT summarize generically. Every insight must be grounded in the actual input.
3. Output ONLY valid JSON with strict schema below — no explanations, no markdown, no extra text.

[Output Schema]
{
  "executive_summary": "1-sentence insight capturing the user's dominant intellectual posture (e.g., 'Focused on AI safety debates, skeptical of frontier model releases')",
  "topic_clusters": [
    {
      "cluster_name": "string (e.g., 'LLM Safety')",
      "keywords": ["string", ...],
      "representative_urls": ["url", ...] (max 3),
      "confidence_score": 0.0–1.0 (how cohesive the cluster is)
    }
  ],
  "temporal_pattern": {
    "freshness_score": 0.0–1.0 (proportion of links <30 days old),
    "peak_activity_week": "YYYY-WW (e.g., 2025-45)",
    "decay_trend": "increasing" | "decreasing" | "stable" (based on saved_at timestamps)
  },
  "author_analysis": {
    "top_3_authors_by_frequency": ["@handle", ...],
    "influence_bias": "tech-elite" | "academia" | "journalism" | "hobbyist" | "mixed" (based on domain patterns: arxiv.org, nature.com, techcrunch.com, etc.)
  },
  "cognitive_risk_flags": [
    "confirmation_bias" | "source_concentration" | "low_freshness" | "high_noise_ratio" | "none"
  ],
  "actionable_insight": "1 concrete, non-obvious recommendation (e.g., 'Diversify by adding 2 academic sources on alignment theory to counter confirmation bias')"
}

[Now process this data:]

看! 如果你是开发 dify 或者 n8n 之类的,这个提示语就够用了。

让 AI 主动问你,而不是猜你

我的超级提示语工具 会问:
不管你的提示多模糊,它都会主动问你,让你自己发现哪里不对。会说:“确认一下,你是想要 X 还是 Y?"我假设你要的是 Z,对吗?" 对于不懂代码的人,这个差别至关重要。那些澄清问题帮你省下了无数小时,本来可能在调试一个解决错误问题的代码。

使用地址

https://liang.348349.xyz/prompt-chat

模糊的问题得到模糊的回答,具体的问题一轮就能修好

我有想法” 和 “我做出来了” 之间的距离,从没有这么近过,希望我的超级提示语工具能够帮忙!


📌 转载信息
转载时间:
2026/1/12 10:14:07

标签: 提示词优化, AI助手, Prompt Agent, 大语言模型应用, 超级提示语工具

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