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/zenobia0000/cursor-agentic-coding-template/plangit clone --depth 1 https://github.com/Zenobia0000/cursor-agentic-coding-templateWhat 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 | $0.00015 | $0.01092 |
| Opus 5 | $0.00008 | $0.00546 |
| Sonnet 5 | $0.00003 | $0.00218 |
| Haiku 4.5 | $0.00002 | $0.00109 |
Grade A, and why
plan 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 2d 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 plan — 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.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
📋 PLAN MODE (v3)
將宏大的目標分解為清晰、可執行的任務藍圖，確保每一步都有據可依。
1. PLAN 🎯 (規劃)
Objective
將高層次的專案目標 (來自
memory-bank/current/projectbrief.md)，分解成一個結構化、有優先級、且包含依賴關係的詳細任務列表 (WBS)，並寫入memory-bank/current/tasks.md作為當前週期的「唯一真相來源」。
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 - 核心實踐:
.cursor/rules/process/planning-process.md(假設此文件存在或將被創建) - 任務追蹤:
.cursor/rules/process/task-tracking.md(假設此文件存在或將被創建) - 核心隔離:
.cursor/rules/isolation_rules/main.mdc
Prerequisites Check
在開始規劃之前，請確保：
- ✅ 專案已初始化:
/van指令已成功執行，memory-bank/current/結構完整。 - ✅ 目標已定義:
memory-bank/current/projectbrief.md文件已存在且內容清晰。 - ❌ Failure Action: 如果
projectbrief.md不存在或為空，應先執行/task-init來定義目標。
2. DO 実行 (執行)
Core Process
遵循一個從宏觀到微觀、且具備批判性思維的分解流程：
Step 0: 健康檢查 (Health Check)
- 檢查
projectbrief.md: 驗證memory-bank/current/projectbrief.md是否存在且內容充實。 - 批判性思考: 如果簡報內容模糊不清或有矛盾，必須先向用戶提出澄清問題，而不是基於不清晰的需求進行規劃。
Step 1: 記憶體互動 - 讀取 (Memory Interaction - Read)
- 讀取目標: 從
memory-bank/current/projectbrief.md中讀取核心目標、範圍和約束。 - 讀取現有任務: 讀取
memory-bank/current/tasks.md，了解是否已有存在的任務，以避免重複規劃。
Step 2: 任務分解 (Decomposition)
- 將大的目標 (Epics) 分解為更小的使用者故事 (Stories)，再進一步細化為具體的技術任務 (Tasks)。
Step 3: 依賴映射 (Dependency Mapping)
- 識別任務之間的依賴關係，並在 WBS 中明確標出。
Step 4: 記憶體互動 - 寫入 (Memory Interaction - Write)
- 更新
tasks.md: 將完整的任務分解結構覆蓋寫入memory-bank/current/tasks.md。 - 更新上下文: 更新
memory-bank/current/activeContext.md，註明PLAN MODE已完成。
3. CHECK ✓ (檢查)
Verification Checklist
- 無遺漏: 所有在
projectbrief.md中提到的功能點是否都已被分解為至少一個任務？ - 記憶體同步:
memory-bank/current/tasks.md的內容是否已成功更新並反映了最新的規劃？
4. ACT 改善 (行動)
Finalization
- 向用戶確認: 在覆寫
tasks.md之後，報告操作已完成。例如：「我已根據您的專案簡報，將詳細的任務計畫更新至memory-bank/current/tasks.md。」
Next Steps
規劃完成後，下一步是針對具體任務進行技術設計。
- 👉 Primary Next Step: 執行
/creative指令，為最高優先級的任務進行架構和技術設計。 - 💡 Alternative: 如果某些任務的需求仍不清晰，可以執行
/task-init針對該模糊點進行一次小範圍的澄清和規劃。
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.
- 2d ago First seen · 68 lines · 15 tokens per session scan A 585099da0a21
plan is a command published in the GitHub repository Zenobia0000/cursor-agentic-coding-template (5 stars, last pushed 4mo ago), licensed MIT. It adds 15 tokens to every session and 1,092 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to plan, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.