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 paid-content-reviewgit 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/paid-content-review)<a href="https://agentmods.dev/skills/chengkj99/kj-skills/paid-content-review"><img src="https://agentmods.dev/badge/skills/chengkj99/kj-skills/paid-content-review/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/paid-content-review"><img src="https://agentmods.dev/badge/skills/chengkj99/kj-skills/paid-content-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00375 | $0.04142 |
| Opus 5 | $0.00187 | $0.02071 |
| Sonnet 5 | $0.00075 | $0.00828 |
| Haiku 4.5 | $0.00038 | $0.00414 |
Grade A, and why
paid-content-review 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
paid-content-review：付费内容发布前质检
把一篇或一整门付费内容，在上线前过一遍系统化质检，产出按严重程度排序、点名到具体位置、可直接执行的审查报告。
核心判断不是「单章写得好不好」，而是这套内容是否真的值得付费购买——整门课要构成一套系统课程而非 N 篇松散文章；单篇要拉开与免费科普的差距。
默认发布渠道：知识星球基础内容。 学员是按篇付费注意力的——每一篇都要独立站得住，不能靠「整课体系」掩盖单篇注水。
付费内容的风险往往不在单章质量，而在全局一致性 + 付费价值 + 篇级用户体验：编号断层、跨章重复、深浅不均、跟不上工具版本、太像免费文章、术语绕、结构散。向 AI 泛问「有什么问题」只能得到泛泛框架，必须先把审什么维度 + 审多大范围 + 用什么标准三件事固定下来。
适用边界（先确认是不是该用我）
- ✅ 用我：内容已写完，要在收费 / 公开发布前做把关——找硬伤、评估付费价值、判断能不能现在上线。
- ❌ 不用我：还在写正文 →
course-generator；还没想好写什么 →ai-programming-topic-planner；把一段实战记录整理成案例 →coding-session-to-tutorial。
知识星球篇级必过：用户视角三硬指标
课程将作为知识星球基础内容发布。审查时（单篇全文 / 整课深读章）必须从付费学员读完这一篇的视角，逐篇核对以下三条——缺一条即记问题，严重缺深度或结构混乱可升为 P0/P1：
| # | 硬指标 | 用户会怎么感受 | 必须看到什么 | 失败即记 |
|---|---|---|---|---|
| U1 | 有深度、有价值 | 「不是表面介绍，带走能用的东西」 | 至少一项可复用增量：方法/步骤、判断标准（何时用/何时别用）、踩坑点、取舍对比、可复用模板 | 只讲「是什么」；无作者增量；通篇像免费科普/官方文档摘抄 |
| U2 | 专业又易懂 | 「术语靠谱，但讲得不绕；新手跟得上，老手也有收获」 | 术语准确；先给直观解释再给术语（或术语后立即白话）；关键处有进阶注脚/对比/边界，让老手也能得点 | 术语堆砌难懂；或过度口语丢专业度；只有入门没有进阶增量；只有高深没有新手入口 |
| U3 | 逻辑清晰 | 「读起来顺，知道为什么学、学什么、怎么做、下一步」 | 叙事大致遵循：开篇问题意识 → 概念/方法 → 示例/操作 → 小结/下一步；段落不跳戏、小节标题能串成故事线 | 开篇无问题/场景；概念与操作倒置；有概念无示例；缺小结或下一步；中途大段跑题 |
三条与维度的关系：
- U1 ↔ 主要落在 D6 / D10（深浅与付费价值），细则见 D14
- U2 ↔ 主要落在 D9（表达一致性）+ D14 的易懂/双层读者
- U3 ↔ 主要落在 D14（单篇叙事结构）；整课衔接另见 D3
深读时先过三硬指标再评其他维度；报告里用「U1/U2/U3」或「深度/易懂/逻辑」点名，便于作者改稿。
第一步：判定审查模式
| 模式 | 何时用 | 报告模板 |
|---|---|---|
| 整课模式（course） | 审查对象是一门课：有总纲/课表/manifest + 多篇正文目录 | references/course-review-report.md |
| 单篇模式（single） | 审查对象是单篇/单讲内容 | references/single-piece-review.md |
用户可显式指定模式；未指定时按上表自动判定（给到目录/课表 → 整课；给到单个文件 → 单篇）。
专项子模式（可叠加在两种模式上，用户点名时启用，否则做全维度审查）：
- 准确性专项：只扫命令/参数/配置/API，逐条标注是否符合当前版本（最该单独做的一项，见 D5）。
- 定价视角：站在「定价 ¥X 的付费学员」角度，专审撑不撑得起价、和免费有没有拉开差距（D10/D11）。
- 单章深审：在整课里挑一章，只跑「准确性 + 深浅 + 可操作性 + 三硬指标（U1–U3）」，逐行给问题与改法。
- 知识星球篇体验专项：只审 U1/U2/U3（深度价值 / 专业易懂 / 逻辑结构），适合改稿期快速过篇。
第二步：先收集三要素（缺则先问，别盲审）
What ships with it
4 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 · 147 lines · 375 tokens per session scan A a60a828f8898
paid-content-review is a skill published in the GitHub repository chengkj99/kj-skills (14 stars, last pushed 11d ago), licensed MIT. It adds 375 tokens to every session and 4,142 once invoked, about $0.0019 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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