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 agentmods add skills/li-evan/bloom/learn-feynmannpx skills add Li-Evan/Bloom --skill learn-feynmangit clone --depth 1 https://github.com/Li-Evan/BloomWrote 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/li-evan/bloom/learn-feynman)<a href="https://agentmods.dev/skills/li-evan/bloom/learn-feynman"><img src="https://agentmods.dev/badge/skills/li-evan/bloom/learn-feynman.svg" alt="Measured on agentmods" 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 | $0.00148 | $0.00710 |
| Opus 5 | $0.00074 | $0.00355 |
| Sonnet 5 | $0.00030 | $0.00142 |
| Haiku 4.5 | $0.00015 | $0.00071 |
Grade A, and why
learn-feynman 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 6d 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.
What it actually says
费曼学习法（learn-feynman）
核心信条：能简单地教会别人，才算真懂。 讲的时候被简化或跳过的地方，正是"我以为我懂了"的幻觉核心点。
何时用
用户学完一个东西想验真伪，或隐约觉得"好像懂了但不踏实"。也是"重输入轻输出"的一次强制输出。
流程（考官 / 学生模式：用户讲，你挑漏洞）
第一步：让用户讲
请他用自己的话、把你当外行，把概念讲一遍。别让他背定义——要他解释、打比方。
第二步：扮好奇学生追问
专挑他含糊带过、用术语糊弄、跳过的环节追问："为什么？""那这个是怎么来的？""举个例子？"命中他答不上来或开始绕的地方。
第三步：揪出"模糊处"= 漏洞
明确指出哪几处他没真懂（不是责备，是定位）。这些就是幻觉核心点。
第四步：定位漏洞性质
每个漏洞是：① 缺前置知识（→转 learn-graph 定位 / learn-crossover 看是否其实已会）还是 ② 有料但没想透（→当场再讲一轮，直到讲顺）？
第五步：判断闭环
能顺畅讲通、追问不倒 = 闭环。否则明确指出还差哪一环。
注意
⚠️ 铁律·只用确证的已会知识：判断用户「已经会什么」只能用他确证学过的知识（亲口确认或可靠背景）；严禁把「正在讲的材料 / 文章作者背景 / 对话里别人的知识」当成用户会的。拿不准 → 直接问「⚠️ 你学过 ___ 吗？」，绝不替他假设。
- 别太快放水——用户讲得顺也要追问一两个深的，确认不是表面流畅。
- 目标是定位漏洞，不是羞辱；找到漏洞是好事，说明知道往哪补。
- 同族 skill：
learn-occamlearn-crossoverlearn-graphlearn-prototype。
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.
- 6d ago First seen · 43 lines · 148 tokens per session scan A 8c6090cd3183
learn-feynman is a skill published in the GitHub repository Li-Evan/Bloom (250 stars, last pushed 2mo ago), licensed MIT. It adds 148 tokens to every session and 710 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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