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 commands/johnnywuj81/tokenknows/bookgit clone --depth 1 https://github.com/johnnywuj81/tokenknowsWrote 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/commands/johnnywuj81/tokenknows/book)<a href="https://agentmods.dev/commands/johnnywuj81/tokenknows/book"><img src="https://agentmods.dev/badge/commands/johnnywuj81/tokenknows/book.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 | $0.00044 | $0.00278 |
| Opus 5 | $0.00022 | $0.00139 |
| Sonnet 5 | $0.00009 | $0.00056 |
| Haiku 4.5 | $0.00004 | $0.00028 |
Grade A, and why
book scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
按 tokenknows:distill skill 的标准流程,把当前 session 蒸馏成 book:
注意: book 类型生成耗时较长 (5-10 分钟), 调多次 LLM 顺序生成各章, 适合:
- 长达数小时的深入对话
- 单一主题 deep dive (e.g. "MCP 协议从原理到实现")
- 想沉淀成参考材料的内容
短对话不要用 book, 用 weekly_report / tech_design 更经济.
- 提示用户 "book 类型预计 5-10 分钟, 是否继续?"
- 拆 event 时按主题分组 (book outline 阶段 LLM 会再次组织成卷/章)
submit_session_eventsdistill_document(document_type="book", time_window=$ARGUMENTS 默认 last_30_days)- 轮询时长放宽到 600s
- 展示时先列卷-章 outline, 再问用户要看哪章
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 · 20 lines · 44 tokens per session scan A 4c33865a35f5
book is a command published in the GitHub repository johnnywuj81/tokenknows (4 stars, last pushed 15d ago), licensed MIT. It adds 44 tokens to every session and 278 once invoked, about $0.0002 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 2026-08-31.
Other commands, from other repositories
wiki-ingest
Ingest a source document (or folder) into the llmwiki.
okf
Command "okf" from kimsanguine/llm-brain, covering 인자 파싱, 기본 private export, 🔴 share-ready gate (외부 공유 전 one-way door), legacy step 1: private dry-run 검토 and legacy step 2: private export.
import
Bulk-import CSV, JSON, or JSONL data into Gnosys memories. Also serves as the parent command for project bundle import.
ingest
Ingest a local file (PDF, DOCX, TXT, MD, etc.) into Gnosys memory. Extracts text, splits into chunks, and creates atomic memories.
prd-to-html
PRD.md를 사람이 읽기 좋은 단일 HTML 문서로 변환합니다.
convert
Convert a file or URL to Markdown using md-anything.