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/zenobia000/cursor-agentic-coding-template/creativegit clone --depth 1 https://github.com/Zenobia000/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 | $0.00022 | $0.01194 |
| Opus 5 | $0.00011 | $0.00597 |
| Sonnet 5 | $0.00004 | $0.00239 |
| Haiku 4.5 | $0.00002 | $0.00119 |
Grade A, and why
creative 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- creative — 100% identical, 0 lines differ
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.
🎨 CREATIVE MODE (v3)
做出明智的設計決策，為專案奠定堅實的架構基礎，確保每一次的創造都有跡可循。
1. PLAN 🎯 (規劃)
Objective
針對一個具體的、已規劃好的任務，進行深入的技術設計和架構規劃，並將所有決策、理由和權衡，以「架構決策記錄 (ADR)」的形式，寫入一份新的
memory-bank/current/creative-[topic].md文件中。
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 - 架構原則:
.cursor/rules/backend/overview.mdc或.cursor/rules/frontend/overview.mdc - 設計模式:
.cursor/rules/patterns/ - 核心隔離:
.cursor/rules/isolation_rules/main.mdc
Prerequisites Check
在開始設計之前，請確保：
- ✅ 任務已規劃:
memory-bank/current/tasks.md中存在一個清晰、待設計的任務。 - ✅ 上下文已同步:
memory-bank/current/activeContext.md反映了當前的工作焦點。 - ❌ Failure Action: 如果沒有已規劃的任務，AI 必須建議用戶先執行
/plan或/task-next。
2. DO 実行 (執行)
Core Process
遵循一個包含批判性思維的結構化決策流程：
Step 0: 健康檢查 (Health Check)
- 檢查任務清晰度: 從
memory-bank/current/tasks.md讀取當前任務。驗證其描述和驗收條件是否足夠清晰以進行設計。 - 批判性思考: 如果任務描述過於寬泛 (e.g., "建立後端")，AI 必須拒絕設計，並建議用戶先使用
/plan將任務進一步細化。
Step 1: 記憶體互動 - 讀取 (Memory Interaction - Read)
- 讀取任務: 從
memory-bank/current/tasks.md中讀取當前要設計的任務的詳細資訊。 - 讀取全局上下文: 讀取
memory-bank/current/projectbrief.md以確保設計與專案總體目標一致。
Step 2: 設計與決策 (Design & Decision Making)
- 分析選項: 針對任務需求，考慮 2-3 個可行的技術方案。
- 評估權衡: 分析每個方案的優點 (Pros)、缺點 (Cons) 和風險 (Risks)。
- 做出選擇: 選擇最適合當前專案約束和目標的方案，並清晰地闡明選擇的理由。
Step 3: 記憶體互動 - 寫入 (Memory Interaction - Write)
- 創建 ADR 文件: 將上述的設計過程和最終決策，寫入到一個新的
memory-bank/current/creative-[topic].md文件中。 - 更新任務列表: 更新
memory-bank/current/tasks.md文件，在對應的任務下，添加一個指向剛剛創建的設計文檔的連結。
3. CHECK ✓ (檢查)
Verification Checklist
- 決策已記錄:
memory-bank/current/creative-[topic].md是否已成功創建？ - 任務已連結:
memory-bank/current/tasks.md中對應的任務是否已更新，並包含了指向新設計文檔的連結？
4. ACT 改善 (行動)
Finalization
- 向用戶確認: 在完成寫入操作後，向用戶報告。例如：「我已為任務『XXX』完成了技術設計，並將決策記錄在
memory-bank/current/creative-auth-flow.md中。tasks.md也已同步更新。」
Next Steps
設計完成後，下一步就是將藍圖變為現實。
- 👉 Primary Next Step: 執行
/implement指令，AI 將會讀取tasks.md和新創建的creative-*.md文件，開始進行編碼。 - 💡 Alternative: 如果設計方案存在較大不確定性，可以先執行
/implement創建一個快速的「原型 (Prototype)」來驗證其可行性。
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 · 66 lines · 22 tokens per session scan A 6b3307e00e4a
creative is a command published in the GitHub repository Zenobia000/cursor-agentic-coding-template (30 stars, last pushed 2mo ago), licensed MIT. It adds 22 tokens to every session and 1,194 once invoked, about $0.0001 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 2026-08-30.
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.
constitution
Create or update the project constitution from interactive or provided principle inputs.