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 Lingtai-AI/lingtai --skill textbook-distillationgit clone --depth 1 https://github.com/Lingtai-AI/lingtaiWrote 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/lingtai-ai/lingtai/textbook-distillation)<a href="https://agentmods.dev/skills/lingtai-ai/lingtai/textbook-distillation"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/textbook-distillation/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/lingtai-ai/lingtai/textbook-distillation"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/textbook-distillation.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 | $0.00116 | $0.02767 |
| Opus 5 | $0.00058 | $0.01384 |
| Sonnet 5 | $0.00023 | $0.00553 |
| Haiku 4.5 | $0.00012 | $0.00277 |
Grade A, and why
textbook-distillation 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.
How it starts
The opening of the file, as written. The whole thing — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Textbook Distillation — Self-Paced Learning Tracks
You are helping a human teach themselves from a textbook (or a long technical document, lecture transcript, or paper set) without a live instructor. Your job is to distill the source into a structured learning track and ship self-contained HTML lecture notes in the style the human asks for: extract the concepts, structure, and worked logic, then re-explain them in your own words with your own examples. Distillation is never reproduction of the author's text. Read Source limits & safety before you intake anything — those boundaries shape every later step.
When to use
The frontmatter description carries the triggers and anti-triggers. Two
boundaries deserve restating here: an ask framed as "summarize the whole book so
I can skip buying it" or "paste me the chapter" crosses the copyright line —
decline it and offer a legitimate distillation instead; and material the
human cannot point to a legitimate source for is out of scope entirely. Both are
spelled out under "Source limits, copyright & safety" below.
Workflow at a glance
intake → chapter map → lesson plan → (per lesson) HTML lecture notes → review loop
Work the phases in order. Do not jump to generating HTML before the human has seen and approved the chapter map and lesson plan — that approval is what keeps the track aligned to their goal, not your guess at it. This is a multi-round, human-in-the-loop flow: talk to the human through the active user-facing channel (mail/email or the relevant chat bridge), not internal scratch/text output, because each phase needs their input.
Phase 1 — Intake the source
Goal: know exactly what you are distilling and confirm you are allowed to.
- Identify the source precisely. Title, author, edition, and the format you have access to (a PDF in the project, a URL, the human's own notes, a file the human pasted). Record the path or link — you will cite it in every artifact.
- Confirm authorization and scope. Ask the human: do they own/have legal access to this material? Which parts do they want covered — whole book, a range of chapters, one topic? Do not proceed on material they cannot point to a legitimate source for.
- Capture the learning goal. Why are they studying it — exam, project, curiosity, teaching it onward? Their level (beginner / refresher / advanced)? Time budget (one weekend vs. a semester)? These set lesson granularity.
- Capture style constraints early (see Style constraints). Collect them now so the lesson plan and the HTML are designed for them, not retrofitted.
What ships with it
1 file 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.
- 6d ago Changed · -4 lines · -46 tokens per session 1000f8dd5646
- 9d ago First seen · 228 lines · 162 tokens per session scan A b8e26581bb69
textbook-distillation is a skill published in the GitHub repository Lingtai-AI/lingtai (677 stars, last pushed yesterday), licensed Apache-2.0. It adds 116 tokens to every session and 2,767 once invoked, about $0.0006 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 2026-09-03.
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