标签 Prompt Agent 下的文章

我的超级提示语工具 - 提供帮忙 - 模糊的问题得到模糊的回答，具体的问题一轮就能修好

作者: 纯情

时间: 2026-01-12

分类: 网络

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。