Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add chengkj99/kj-skills --skill phenomenon-insightgit clone --depth 1 https://github.com/chengkj99/kj-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/chengkj99/kj-skills/phenomenon-insight)<a href="https://agentmods.dev/skills/chengkj99/kj-skills/phenomenon-insight"><img src="https://agentmods.dev/badge/skills/chengkj99/kj-skills/phenomenon-insight/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/chengkj99/kj-skills/phenomenon-insight"><img src="https://agentmods.dev/badge/skills/chengkj99/kj-skills/phenomenon-insight.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00141 | $0.01477 |
| Opus 5 | $0.00071 | $0.00739 |
| Sonnet 5 | $0.00028 | $0.00295 |
| Haiku 4.5 | $0.00014 | $0.00148 |
Grade A, and why
phenomenon-insight scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
phenomenon-insight：现象本质洞察引擎
把一个具体事件或日常观察，拆成能解释“为什么会这样”的底层机制，再转成可传播、可写作、可拍短视频的核心观点。
使用边界
用它处理“现象 → 洞察 → 内容观点”的前置分析，不直接替代成稿技能。
- 要写完整文章或脚本，洞察完成后接
content-creator。 - 要写成康健本人风格，洞察完成后接
kangjian-skill。 - 要做 AI 编程领域选题，洞察完成后接
ai-programming-topic-planner。 - 要基于已发布内容做连载规划，先按 LLM Wiki 的连载去重规则检查已发布内容。
不要把它用于纯信息查询、事实核验、情绪安慰、没有具体事实支撑的玄学判断。
核心流程
每次都按 6 步走。用户只给一句话时，也先基于已有信息分析；如果关键事实缺失，在输出末尾列“需要补充的证据”，不要卡住。
1. 复述现象
用一句话把现象说清楚，避免一开始就下判断。
格式：
这个现象不是：[表面标签]
它更像是：[更准确的问题定义]
2. 拆隐含假设
列出用户可能默认相信的 3-7 个前提，并判断哪些值得保留，哪些需要推翻。
优先拆这几类假设：
- 关系假设：谁应该支持谁、谁应该回应谁。
- 平台假设：平台按什么逻辑分发、评价、放大或过滤。
- 人性假设：人为什么不行动、不点赞、不表达、不购买、不承认。
- 时间假设：过去有效的经验是否仍然适用。
- 群体共识：大家都这么想，是否只是习惯性正确。
参考第一性原理模块时，优先使用 implicit-assumption 的问题：这个结论的大前提是什么？这个前提本身怎么被证明？
3. 分层解释机制
至少从 3 个层面解释，不要只给单因果。
默认层次：
| 层次 | 要回答的问题 |
|---|---|
| 表层行为 | 看起来发生了什么 |
| 系统机制 | 平台、组织、市场或工具如何放大这个现象 |
| 人性机制 | 恐惧、比较、身份、成本、奖励如何驱动行为 |
| 关系机制 | 熟人、陌生人、同事、读者之间的心理距离如何影响反应 |
| 长期机制 | 这个现象如果持续，会把人推向什么结果 |
如果是内容/平台现象，必须包含“分发机制”和“观众心理”。如果是职业/AI 编程现象，必须包含“激励结构”和“能力迁移”。
4. 找反常识
把“大家以为 X，其实是 Y”写清楚。反常识必须有机制支撑，不能只是耍聪明。
好洞见的标准：
- 不是描述现象，而是解释机制。
- 能推翻一个常见误解。
- 能让读者把自己的经历代入进去。
- 有边界条件，知道什么时候不成立。
5. 提炼核心洞见
输出 3 种密度：
本质判断：一句完整判断，解释这个现象真正说明了什么。
传播金句：一句更短、更适合标题/口播的表达。
边界条件：这句话在哪些情况下不成立。
避免空泛金句。凡是可以套到任何话题上的句子，都要重写。
6. 转成内容入口
输出可直接接到内容生产的结构：
- 3 个标题：痛点型、反常识型、方法型各一个。
- 短视频结构：Hook → 现象 → 误区 → 机制 → 反转 → 建议。
- 公众号结构：生活锚点 → 问题重定义 → 三层机制 → 反常识判断 → 可执行建议 → 余味收尾。
- 可延展选题：续集、反例篇、方法篇各 1 个。
输出格式
默认用下面格式。用户要求简短时，保留“本质判断 + 机制 + 内容入口”三块。
**现象重定义**
[一句话]
**隐含假设**
- [假设]：保留/推翻，因为...
**机制拆解**
- 表层行为：...
- 系统机制：...
- 人性机制：...
- 关系机制：...
- 长期机制：...
**反常识**
大家以为 [X]，其实 [Y]。原因是 [Z]。
**核心洞见**
- 本质判断：...
- 传播金句：...
- 边界条件：...
**内容转化**
- 痛点型标题：...
- 反常识标题：...
- 方法型标题：...
- 短视频结构：...
- 公众号结构：...
- 延展选题：...
**还需要验证**
- [如果有事实缺口，列出需要补充的数据/案例]
质量门禁
交付前自检：
- 是否从“发生了什么”推进到了“为什么会这样”？
- 是否拆出了至少 3 个隐含假设？
- 是否同时解释了系统机制和人性机制？
- 核心洞见是否能写成“大家以为 X，其实 Y”？
- 是否给了边界条件，避免过度解释？
- 内容入口是否能直接交给
content-creator或kangjian-skill继续生产？
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 150 lines · 141 tokens per session scan A 9f014a92bb3a
phenomenon-insight is a skill published in the GitHub repository chengkj99/kj-skills (14 stars, last pushed 11d ago), licensed MIT. It adds 141 tokens to every session and 1,477 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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