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-graphnpx skills add Li-Evan/Bloom --skill learn-graphgit clone --depth 1 https://github.com/Li-Evan/BloomWhat 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 | $0.00141 | $0.00781 |
| Opus 5 | $0.00071 | $0.00391 |
| Sonnet 5 | $0.00028 | $0.00156 |
| Haiku 4.5 | $0.00014 | $0.00078 |
Grade A, and why
learn-graph 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 3d 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
知识图谱学习法（learn-graph）
核心信条：自己一步步建图谱的过程，本身就是最有效的学习——不要直接套用别人给的图谱。 绝大部分知识，都有一个从常识就能入门的点。
何时用
用户要系统进入一个新领域，或焦虑"学得不够系统 / 不知何时算够"。
流程（关键：和用户一起建，不是直接灌一张完整图）
第一步：锁定目标领域 X 和目的
用户为什么学 X？（接 learn-occam 的"既定问题"）目的决定图谱画到多细。
第二步：构建图谱——只抓三件事
概念/名称 · 用途 · 上下文关系（父子节点）：
- 子节点 = X 依托 / 基于什么；父节点 = X 服务于什么目标。
- 以提问引导用户一起填（自己建图才学得到），别一次性灌完。先给骨架，留节点让他补。
第三步：标注两个关键
- 复用价值：哪些节点父节点多（像 Python）→ 优先学，回报最高。
- 入门点：哪个节点"从常识就能入门"→ 学习路径的起点。
第四步：输出学习路径 + 颗粒度
从入门点出发、沿父子关系排一条有效路径。颗粒度按需自由切换（领域图 → 细分学科图）。"学到哪算够"= 覆盖到能解决第一步那个目的的节点即可，不必学满。
第五步：交接
- 拿不准某节点是不是缺前置知识 → 这正是图谱的强项，已在图上标出。
- 找到入门点要动手 → 转
learn-prototype（在图上找"最垃圾原型"的起点）。
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。
- 强调"自己建"：多用提问让用户参与，别炫一张完美的图。
- 同族 skill：
learn-occam(该不该学)learn-crossover(已会什么)learn-prototype(动手)learn-feynman(自查)。
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.
- 3d ago First seen · 47 lines · 141 tokens per session scan A 73449bcf6083
learn-graph is a skill published in the GitHub repository Li-Evan/Bloom (248 stars, last pushed 2mo ago), licensed MIT. It adds 141 tokens to every session and 781 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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