Borrowing it
Nothing to install: this file belongs to gatelynch/llm-knowledge-base. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/gatelynch/llm-knowledge-base/main/.claude/commands/init-llm.mdgit clone --depth 1 https://github.com/gatelynch/llm-knowledge-baseWrote 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/gatelynch/llm-knowledge-base/init-llm)<a href="https://agentmods.dev/commands/gatelynch/llm-knowledge-base/init-llm"><img src="https://agentmods.dev/badge/commands/gatelynch/llm-knowledge-base/init-llm/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/commands/gatelynch/llm-knowledge-base/init-llm"><img src="https://agentmods.dev/badge/commands/gatelynch/llm-knowledge-base/init-llm.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00026 | $0.01676 |
| Opus 5.5 | $0.00010 | $0.00670 |
| Sonnet 5.5 | $0.00005 | $0.00335 |
| Haiku 4.5 | $0.00003 | $0.00168 |
Grade A, and why
init-llm 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 2mo 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
初始化知識庫
帶領新使用者完成知識庫設定:收集個人資料與偏好、掃描現有檔案、提出整理計畫,並更新 CLAUDE.md。從使用者的回答中判斷語言,並以相同語言回應。
流程
1. 訪談——你是誰?
用一則訊息問以下問題,然後等使用者回覆:
讓我們來設定你的知識庫。先告訴我一些關於你的事:
- 稱呼 — 我該怎麼稱呼你?
- 簡介 — 用 1-2 句話描述你是誰、你關心什麼(這會寫進「關於使用者」段落)
- 語言 — 你偏好哪種輸出語言?(例如:繁體中文、English、日本語)
- 領域 — 這個知識庫的核心主題是什麼?(例如:AI、教育、設計、軟體開發)
2. 訪談——你怎麼工作?
收到第 1 步的答案後,再問(一則訊息,等使用者回覆):
再問幾個關於工作方式的問題:
- 互動風格 — 你偏好「先問再動」還是「直接行動、快速推進」?
- 探索 vs. 生成 — 碰到新主題時,你比較想先深度討論(
/thinking-partner風格),還是直接生成整理好的內容(/compile風格)?- 寫作風格 — 你有特定的寫作口吻或風格嗎?(選填,不確定可跳過)
- 核心主題 — 列出你的 3-5 個核心主題,附簡短描述(例如:「個人知識管理——建立可持續的知識系統」)
- 成果資料夾 —
artifacts/下有哪些子資料夾是你已經知道會用到的?(例如:文章/、教學記錄/、演講/)
3. 掃描現有檔案
掃描 vault 中不屬於範本結構的 .md 檔案與資料夾:
- 用 Glob 找出根目錄及非範本資料夾中的檔案
- 以下為範本內建資料夾,略過不處理:
raw/、wiki/、brainstorming/、artifacts/、attachments/、docs/、.claude/、.git/ - 找出可以移入
raw/或artifacts/的檔案 - 找出可以對應到
raw/或artifacts/子資料夾的現有資料夾
如果是全新 clone、沒有額外檔案,跳至第 5 步。
4. 提出整理計畫
用清單格式呈現計畫,讓使用者可以逐條同意、拒絕或修改:
- 列出每個檔案/資料夾的移動目標,附上簡短理由
- 列出依據使用者角色與領域要在
artifacts/下新建的子資料夾 - 列出不異動的項目(例如:
README.md、CLAUDE.md、docs/)
等使用者明確同意後才繼續執行。
5. 執行
取得同意後:
-
建立資料夾 — 建立已協議的新子資料夾(例如:
artifacts/文章/) -
移動檔案 — 用
mv移動檔案(不用cp,避免重複) -
更新 CLAUDE.md — 替換以下佔位符與段落:
佔位符替換:
[YOUR_BIO — 用 1-2 句話描述你是誰、你關心什麼]→ 使用者簡介[YOUR_NAME]→ 使用者稱呼(出現在「互動原則」)[YOUR_LANGUAGE]→ 偏好語言(出現在「互動原則」、「Vault 概覽」、「重要說明」三處)[YOUR_DOMAIN]→ 知識庫領域(出現在「Vault 概覽」)[DATE]→ 今天的日期
段落更新:
- 互動原則:依使用者偏好調整(例如:若偏好「直接行動」,移除「先告訴我你的計畫」那條)
- 寫作風格:如果使用者提供了寫作風格,取消 HTML comment 並填入內容;若跳過則保留 comment 原狀
- 核心主題:取消 HTML comment,填入使用者提供的 3-5 個主題
- 知識庫架構:更新
artifacts/下的子資料夾註解,反映使用者實際建立的資料夾 - 筆記分類:依使用者的領域調整分類描述(例如教師的分類會不同於工程師)
- 重要說明:替換
[YOUR_LANGUAGE]為實際語言
-
建立索引檔案 — 如果
wiki/indexes/All-Sources.md和wiki/indexes/All-Concepts.md不存在,建立空白表頭
6. 總結與下一步
告訴使用者完成了哪些事(移動了哪些檔案、更新了 CLAUDE.md、建立了哪些資料夾)。
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.
- 2mo ago First seen · 102 lines · 26 tokens per session scan A c103a2c06209
init-llm is a command published in the GitHub repository gatelynch/llm-knowledge-base (343 stars, last pushed 7mo ago), licensed MIT. It adds 26 tokens to every session and 1,676 once invoked, about $0.0001 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.
init-llm compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| init-llmgatelynch/llm-knowledge-base | Command | 343 | 7mo ago | MIT | A | 26 | Claude Code |
| minutes-ideassilverstein/minutes | Command | 1.6k | yesterday | MIT | A | 48 | Claude Code, OpenCode |
| learnReflexioAI/claude-smart | Command | 791 | 3d ago | Apache-2.0 | A | 14 | Claude Code |
| memory-storeverygoodplugins/mcp-automem | Command | 66 | today | MIT | A | 10 | Claude Code |
| analyze-contextRohiRIK/OpenLtm | Command | 29 | 8d ago | MIT | A | 39 | Claude Code |
| cc-memoryTheLobbi/claude | Command | 21 | 7d ago | MIT | A | 0 | Claude Code |
| lians-recallLians-ai/Lians | Command | 17 | 4d ago | Apache-2.0 | A | 19 | Claude Code |