Borrowing it
Nothing to install: this file belongs to Zenobia0000/cursor-agentic-coding-template. 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/Zenobia0000/cursor-agentic-coding-template/main/.cursor/commands/van.mdgit clone --depth 1 https://github.com/Zenobia0000/cursor-agentic-coding-templateWrote 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/zenobia0000/cursor-agentic-coding-template/van)<a href="https://agentmods.dev/commands/zenobia0000/cursor-agentic-coding-template/van"><img src="https://agentmods.dev/badge/commands/zenobia0000/cursor-agentic-coding-template/van/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/zenobia0000/cursor-agentic-coding-template/van"><img src="https://agentmods.dev/badge/commands/zenobia0000/cursor-agentic-coding-template/van.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 | $0.00017 | $0.01083 |
| Opus 5.5 | $0.00007 | $0.00433 |
| Sonnet 5.5 | $0.00003 | $0.00217 |
| Haiku 4.5 | $0.00002 | $0.00108 |
Grade A, and why
van 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.
This is a copy
100% identical to van — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
🚀 VAN MODE (v3)
冒險啟航！初始化專案，建立共享記憶庫 (
memory-bank)，為所有 AI 協作奠定堅實的基礎。
1. PLAN 🎯 (規劃)
Objective
檢查
memory-bank的拓撲完整性，如果memory-bank/current工作區不存在或不完整，則創建或修復核心的共享記憶體文件 (tasks.md,activeContext.md,projectbrief.md)。這是確保所有 AI Agent 能在同一狀態下協作的關鍵第一步。
Guiding Rules
在執行此指令時，AI Agent 必須遵循以下規則：
- 主要規則:
.cursor/rules/principles/global.mdc - 核心隔離:
.cursor/rules/isolation_rules/main.mdc - 檔案操作: 嚴格遵循檔案操作的最佳實踐，避免意外覆蓋。
Prerequisites Check
VAN MODE是所有流程的起點。
- ✅ 無前置條件: 隨時可以執行此指令來初始化或驗證專案環境。
2. DO 実行 (執行)
Core Process
遵循一個具備安全檢查的冪等 (idempotent) 初始化流程：
Step 0: 健康檢查 (State Check)
- 檢查
memory-bank/current目錄: 檢測memory-bank/current/目錄是否存在。 - 檢查核心文件: 檢測
current/目錄下的tasks.md,activeContext.md,projectbrief.md是否存在。 - 批判性思考: 這是狀態診斷。AI 的任務是找出缺失的部分，並準備修復它們。
Step 1: 記憶體互動 - 讀取與分析 (Memory Interaction - Read & Analyze)
- 讀取文件系統: 獲取
memory-bank/current/目錄的當前狀態。 - 生成執行計畫: 根據診斷結果，生成一個創建或修復檔案的計畫。
Step 2: 記憶體互動 - 確認與寫入 (Memory Interaction - Confirm & Write)
- 向用戶確認 (Confirm): 在執行任何寫入操作之前，AI 必須向用戶清晰地報告它將要執行的操作，並請求批准。
- 如果
current目錄不存在: 「memory-bank/current工作區不存在。我將為您創建該工作區以及所有核心文件。是否繼續？」 - 如果文件已存在: 「警告：
memory-bank/current中的tasks.md文件已存在且包含內容。重新初始化將會覆蓋它，可能導致數據丟失。您確定要繼續嗎？」
- 如果
- 執行寫入 (Write): 在得到用戶肯定的答覆後，執行文件和目錄的創建/覆蓋操作。
3. CHECK ✓ (檢查)
Verification Checklist
在完成初始化後，進行最終的完整性檢查：
- 工作區目錄存在:
memory-bank/current/目錄是否已成功創建或確認存在？ - 核心文件存在:
current/下的核心文件是否都已存在？ - 權限正常: AI Agent 是否有權限讀寫
memory-bank/current/目錄？
4. ACT 改善 (行動)
Finalization
- 報告完成: 在所有文件操作完成後，向用戶報告：「專案初始化完成。共享記憶體
memory-bank/current工作區已準備就緒。」
Next Steps
專案的基礎設施已準備就緒，下一步是定義目標並進行規劃。
- 👉 Primary Next Step: 執行
/task-init，讓用戶提出第一個高層次目標。 - 💡 Alternative: 如果是恢復一個現有項目，可以執行
/task-next，讓 PM 小助理分析tasks.md並建議下一步的工作。
VAN MODE (v3) 已啟動。正在診斷
memory-bank/current工作區狀態，準備進行初始化...
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 · 63 lines · 17 tokens per session scan A 415e3d06f410
van is a command published in the GitHub repository Zenobia0000/cursor-agentic-coding-template (5 stars, last pushed 6mo ago), licensed MIT. It adds 17 tokens to every session and 1,083 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to van, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
lians-recall
Recall current (non-stale) facts from Lians memory, optionally as-of a past date.