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/task-next.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/task-next)<a href="https://agentmods.dev/commands/zenobia0000/cursor-agentic-coding-template/task-next"><img src="https://agentmods.dev/badge/commands/zenobia0000/cursor-agentic-coding-template/task-next/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/task-next"><img src="https://agentmods.dev/badge/commands/zenobia0000/cursor-agentic-coding-template/task-next.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 | $0.00020 | $0.01216 |
| Opus 5.5 | $0.00008 | $0.00486 |
| Sonnet 5.5 | $0.00004 | $0.00243 |
| Haiku 4.5 | $0.00002 | $0.00122 |
Grade A, and why
task-next 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 task-next — 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
👨💼 TASK NEXT (PM Assistant v3)
您的專案經理小助手已上線。為您過濾上下文噪音，分析戰況，並從多個策略角度建議下一步的最佳行動。
1. PLAN 🎯 (規劃)
Objective
在您需要重新聚焦時，為您提供一份關於當前
memory-bank/current/tasks.md的摘要簡報，並從多個策略角度建議接下來應該執行的任務。此指令是一個只讀分析器，旨在幫助決策，不會修改tasks.md。
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 - 規劃與追蹤:
.cursor/rules/process/planning-process.md和.cursor/rules/process/task-tracking.md - 核心隔離:
.cursor/rules/isolation_rules/main.mdc
Prerequisites Check
在獲取簡報之前，請確保：
- ✅
tasks.md存在:memory-bank/current/tasks.md是您專案的「唯一真相來源」。 - ❌ Failure Action: 如果
tasks.md不存在，AI 必須建議用戶先運行/van來初始化專案。
2. DO 実行 (執行)
Core Process
PM 小助手將為您執行以下分析流程：
Step 0: 健康檢查 (Health Check)
- 檢查
tasks.md狀態: 驗證memory-bank/current/tasks.md是否存在、有效，且包含待辦任務。 - 批判性思考: 如果所有任務都已完成，AI 應報告：「恭喜！所有任務都已完成。建議執行
/reflect進行復盤。」 如果沒有可執行的任務（所有pending任務都被阻塞），AI 應報告：「目前沒有可立即開始的新任務，我們需要先完成正在進行中的『XXX』任務。」
Step 1: 記憶體互動 - 讀取 (Memory Interaction - Read)
- 讀取任務列表: 完整讀取並解析
memory-bank/current/tasks.md的內容。
Step 2: 多維度評估 (Multi-dimensional Evaluation)
- 識別候選者: 找出所有狀態為
pending且其依賴項均已completed的可執行任務。 - 評估視角: 從以下 PM 視角對每個候選任務進行評估：
- 👑 關鍵路徑 (Critical Path): 哪個任務是後續最多待辦任務的阻塞點？
- 💎 最高價值 (Highest Value): 哪個任務最貼近
projectbrief.md中定義的核心目標？ - ⚡️ 快速取勝 (Quick Win): 哪個任務預估工作量最小，可以快速完成以建立動能？
- 🛡️ 風險緩解 (Risk Mitigation): 哪個任務能解決最大的技術或業務不確定性？
Step 3: 記憶體互動 - 寫入 (Memory Interaction - Write)
- 輸出 PM 簡報: 將分析結果彙總成一份清晰、結構化的「PM 簡報」，作為輸出直接呈現給用戶。此指令不修改
tasks.md。 - (可選) 更新上下文: 可以在
memory-bank/current/activeContext.md中輕量地記錄一次「PM 簡報已提供」。
3. CHECK ✓ (檢查)
Verification Checklist
- 簡報是否清晰: 輸出的 PM 簡報是否易於理解，並清楚解釋了每個建議背後的原因？
- 只讀原則: AI 是否確保了沒有修改
tasks.md？
4. ACT 改善 (行動)
Finalization
- 等待用戶決策: AI 的任務在提供簡報後即完成，等待用戶做出最終決策。
Next Steps
AI 將根據用戶的決策，進入相應的模式。
- 👉 Primary Next Step: 用戶選擇一個建議後，AI 應準備執行
/creative或/implement指令，來處理被選中的任務。 - 💡 Alternative: 如果用戶認為所有建議都不合適，AI 應建議：「看起來我們的計畫可能需要調整。建議執行
/plan來重新審視和分解任務。」
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 · 66 lines · 20 tokens per session scan A cc0178a40903
task-next is a command published in the GitHub repository Zenobia0000/cursor-agentic-coding-template (5 stars, last pushed 6mo ago), licensed MIT. It adds 20 tokens to every session and 1,216 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 task-next, differing in 0 lines, and is treated as a copy.
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workpm
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