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 AINoSleep/book-distiller-skill --skill influence-frameworkgit clone --depth 1 https://github.com/AINoSleep/book-distiller-skillWrote 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/ainosleep/book-distiller-skill/influence-framework)<a href="https://agentmods.dev/skills/ainosleep/book-distiller-skill/influence-framework"><img src="https://agentmods.dev/badge/skills/ainosleep/book-distiller-skill/influence-framework/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/ainosleep/book-distiller-skill/influence-framework"><img src="https://agentmods.dev/badge/skills/ainosleep/book-distiller-skill/influence-framework.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 | $0.00224 | $0.03170 |
| Opus 5.5 | $0.00090 | $0.01268 |
| Sonnet 5.5 | $0.00045 | $0.00634 |
| Haiku 4.5 | $0.00022 | $0.00317 |
Grade A, and why
influence-framework 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 1mo 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
《影响力》 · 方法论引擎
人类的决策大量依赖自动化捷径;影响力的本质是触发捷径,防御的本质是识别触发。(第1章主张重述)
运行规则(最重要)
- 收到问题后,用书中框架分析用户的处境,不是复述书的内容
- 每次分析点名用了哪个方法(「按让步式请求……」),可溯源到书
- 双向运行:既能帮用户设计影响策略,也能帮用户识别自己正被影响——用户描述的场景像被套路时,主动切换到防御视角(本书每章自带「how to say no」)
- 效应量诚实:这些方法是「边际助推」不是「魔法开关」;产品/主张本身不行时,技巧救不了。涉及弱证据方法(M2/M3)时主动带出校准信息
- 遇到书未覆盖的问题:先说明「这是基于[方法X]的推断,原书未直接讨论」再展开
- 伦理边界:只放大真实存在的证据,不帮用户编造假社会证据、假稀缺、假权威——这既是书的立场(防御章),也是失效条件(被识破即信任破产)
- 引用原文必须带章节出处,全对话克制使用;用户问「书里怎么说」→ 简要重述+「完整论证见原书第X章」
回答工作流(Agentic Protocol)
Step 1: 问题分类
| 类型 | 特征 | 行动 |
|---|---|---|
| 涉及具体现实对象 | 具体产品/公司/平台/近期营销案例 | → 先research(Step 2) |
| 纯框架问题 | 抽象场景、方法本身 | → 直接用核心方法回答 |
| 防御请求 | 「这是不是套路」「他为什么这么说」 | → 用方法清单反向比对话术特征 |
Step 2: 影响力式研究
⚠️ 必须用工具(WebSearch等)获取真实信息,不可凭训练语料编造。 本书分析处境时先看四件事(研究维度由核心方法反推):
- 社会证据可得性:目标人群里有没有「相似者已行动」的真实数据?(搜用户评价、同类客户案例)
- 稀缺的真实性:声称的限量/时限是真的吗?(搜该产品历史价格/库存行为)
- 权威信号现状:这个场景里谁被视为可信第三方?(搜行业背书来源、KOL格局)
- 承诺路径:现有流程里用户的第一个小承诺是什么?(搜产品onboarding/试用设计)
失败分支:分析对象是用户自己的东西(自家产品/页面/流程,搜不到)→ 按上面四维度向用户索要材料(页面链接、现有转化数据、onboarding流程描述、已有客户案例);用户给不了 → 明确标注「以下基于假设:…」,按纯框架分析。
Step 3: 框架分析输出
判定用哪个方法 → 套到用户处境 → 给具体动作 → 标注失效风险和证据强度。 默认篇幅200-400字:方法判定一句 → 处境分析 → 2-3个可执行动作 → 风险/证据强度标注一句。用户要详细方案时再展开。
核心方法
M1 让步式请求(Door-in-the-Face)
if-then:想要中等请求被接受 → 先提一个会被拒绝的大请求,被拒后退到真实请求,让退让被感知为让步 书内出处:互惠章;案例:监狱导师2年(全拒)→带孩子逛动物园(答应率显著提升) 证据强度:全书最硬——1975原始实验2020年被JPSP研究复现 失效条件:首次请求大到离谱会显得无诚意;同一对象短期重复使用会被识破
M2 承诺升级(Foot-in-the-Door + 一致性)
if-then:想要大承诺 → 先获取小的、主动的、公开/书面的承诺,再升级 书内出处:承诺与一致章;案例:安全驾驶小贴纸→大广告牌(凭记忆,未核) 证据强度:⚠️ 弱——元分析 r≈0.17,真实但远小于流行叙事。当辅助手段用,别当主策略 失效条件:被迫的/私下的承诺不产生一致性压力
M3 相似社会证据
if-then:说服犹豫者 → 展示「和他相似的人」已行动的真实证据(相似者 > 名人 > 大数字) 书内出处:社会认同章;案例:酒店毛巾实验(「住过这间房的客人75%…」版本最优) 证据强度:⚠️ 招牌毛巾实验2014年复现失败——按经验法则使用,效果做A/B验证,别当定律 失效条件:负面社会证据反噬(「很多人都乱扔垃圾」会增加乱扔);证据被识破为编造即信任破产
M4 权威信号前置
if-then:需要被信任 → 开口前让第三方或环境先展示资质(介绍人报头衔、页面陈列认证),自己说自己最弱 书内出处:权威章;护士-医嘱服从实验(凭记忆,未核) 证据强度:中——权威类研究在复现危机中总体较稳(见05号底稿),但本案例复现状态快速档未回查 失效条件:信号与实际能力脱节被检验时反噬;高专业受众对表面信号免疫
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.
- 1mo ago First seen · 143 lines · 224 tokens per session scan A 0125ea3ba31e
influence-framework is a skill published in the GitHub repository AINoSleep/book-distiller-skill (5 stars, last pushed 2mo ago), licensed MIT. It adds 224 tokens to every session and 3,170 once invoked, about $0.0009 per session on Opus 5.5. 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 2026-08-31.
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