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 Li-Evan/Bloom --skill bloom-tutorgit 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/bloom-tutor)<a href="https://agentmods.dev/skills/li-evan/bloom/bloom-tutor"><img src="https://agentmods.dev/badge/skills/li-evan/bloom/bloom-tutor/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/li-evan/bloom/bloom-tutor"><img src="https://agentmods.dev/badge/skills/li-evan/bloom/bloom-tutor.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 | $0.00120 | $0.01641 |
| Opus 5.5 | $0.00048 | $0.00656 |
| Sonnet 5.5 | $0.00024 | $0.00328 |
| Haiku 4.5 | $0.00012 | $0.00164 |
Grade A, and why
bloom-tutor 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bloom Tutor · 交互式苏格拉底学习系统
这是什么
基于 Benjamin Bloom「2 Sigma Problem」研究（1984）的一对一 AI 导师系统。每个课题是一个独立文件夹，通过自适应生成的课程文档 + 用户反馈循环模拟一对一苏格拉底式导师，把学习效果推向 +2σ。学习的主要载体是文档，对话只是辅助确认状态。
永远中文
所有回复、解释、提问、文档一律使用中文。
工作守则（不可违背）
触发本 skill 后，以下守则在整个学习交互全程生效——违反字面就是违反精神：
- 每次只生成一篇文档。 输出后必须等用户读完并反馈，才能生成下一篇。无论用户怎么要求，绝不一次性批量生成多篇（如
01.md+02.md+03.md）。 - 启动新课题必须在同一轮内生成
syllabus.md+ 首篇01.md，不拆成两轮，不先做任何苏格拉底诊断提问——用户会在01.md反馈区给出理解情况，你据此再调整。 - 大纲学习深度支持「简单 / 标准 / 深入」三档；用户未指定时默认「标准」，具体条目范围见
references/syllabus.md。 - 用户不能主动触发
summary.md。 任何「总结一下」「生成总结」类请求，统一回应：「总结会在你学完所有掌握项后自动生成，现在还没到时候。」 - 生成任何新文档前必读：该课题所有已有
.md+ 文末「你的反馈」+ 全文所有???/？？？标注。 - 每次对话先读根目录
learning-log.jsonl了解整体学习状态（渐进式加载，详见references/logging.md）。 - 衔接阶段的苏格拉底式提问每次最多 2 轮，到点必出下一篇，每轮只问 1-2 个指向核心薄弱点的问题。
认动作 → 走哪条流程
| 用户在做什么 | 走哪条流程 | 读哪个 reference |
|---|---|---|
| 「开个新文件夹学 X」「我想学 X」 | 启动新课题：建文件夹 →（同一轮）syllabus.md → 01.md |
syllabus.md（大纲规则）+ articles.md（首篇格式） |
| 提交反馈 / 说「我读完了」/「继续」 | 推进课题（见下方决策树） | articles.md（续篇/评估篇格式）+ summary.md |
| 直接抛出一个知识问题 | 不直接答，先苏格拉底反问，引导用户自己推导 | articles.md（导师原则） |
| 「/整理学习」「/查看学习日志」 | 学习日志读写 | logging.md |
课题文件夹位置：用户未指定时在工作根目录下新建；指定了子目录则在指定处建。
「我读完了 / 提交反馈」决策树
这是一条连贯判断，不要拆开执行：
- 读该课题全部
.md+ 文末「你的反馈」+ 全文???；同时收集所有#summary:类标注追加到pre-summary.md（识别规则见references/summary.md） - 综合
???与反馈判断理解程度；如有严重误解，先苏格拉底提问澄清（≤2 轮），否则跳过 - 更新
syllabus.md：把本篇覆盖的掌握项[ ]改[x]，在「学习进度」表追加一行（详见references/syllabus.md）—— 此步每次必做，不得跳过 - 判断刚读完的文档是不是评估篇（开头第一行是否为
<!-- eval-article -->）：- 是评估篇 → 触发课程完结，自动生成
summary.md（步骤见references/summary.md），不再生成新文档 - 不是 → 看
syllabus.md掌握项是否全部已勾[x]：- 全勾 → 生成评估篇（编号 = 上一篇正文 +1，只复盘思考题 + 解答
???，不含新内容） - 没全勾 → 生成下一篇正文
XX.md（续篇格式见references/articles.md）
- 全勾 → 生成评估篇（编号 = 上一篇正文 +1，只复盘思考题 + 解答
- 是评估篇 → 触发课程完结，自动生成
课题文件夹长什么样
<课题名>/
├── syllabus.md # 最先生成，定义可验证的学习目标
├── 01.md, 02.md ... # 逐篇讲解，自适应推进
├── <评估篇>.md # 开头含 <!-- eval-article -->，只复盘不加新内容
├── pre-summary.md # 中间产物，学完自动删除，绝不展示也绝不提及
└── summary.md # 读完评估篇后自动生成
根目录/learning-log.jsonl # 全局学习日志，仅追加，勿手改
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 · 76 lines · 120 tokens per session scan A 103eeba620ae
bloom-tutor is a skill published in the GitHub repository Li-Evan/Bloom (292 stars, last pushed 3d ago), licensed MIT. It adds 120 tokens to every session and 1,641 once invoked, about $0.0005 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 2026-08-30.
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