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/reflectgit 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 | $0.00018 | $0.01253 |
| Opus 5 | $0.00009 | $0.00626 |
| Sonnet 5 | $0.00004 | $0.00251 |
| Haiku 4.5 | $0.00002 | $0.00125 |
Grade A, and why
reflect 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:
- reflect — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🔍 REFLECT MODE (v3)
從已完成的工作中汲取經驗，提煉智慧，為未來的成功鋪路。
1. PLAN 🎯 (規劃)
Objective
對一個已完成的開發週期進行全面的、數據驅動的回顧，總結成果，反思過程，並將所有學習到的經驗寫入一份新的
memory-bank/current/reflection-[date].md文件中，作為未來改進的基礎。
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 - 流程改進:
.cursor/rules/process/planning-process.md - 核心隔離:
.cursor/rules/isolation_rules/main.mdc
Prerequisites Check
在開始反思之前，請確保：
- ✅ 週期已結束:
memory-bank/current/tasks.md中絕大多數任務都已標記為completed。 - ✅ 歷史記錄存在:
memory-bank/current/progress.md文件存在，並包含了本週期的開發日誌。 - ❌ Failure Action: 如果工作尚未完成，AI 必須拒絕反思，並建議：「看起來本週期還有未完成的任務。建議先完成它們再進行反思。」
2. DO 実行 (執行)
Core Process
遵循一個客觀、公正、面向未來的復盤框架：
Step 0: 健康檢查 (Health Check)
- 檢查拓撲完整性: 驗證
memory-bank/current/中的tasks.md和progress.md的數據是否完整且可以互相印證。 - 批判性思考: 反思的目的是為了改進，而非指責。AI 在分析時應採取中立的語氣。如果發現某個任務延遲嚴重，應客觀描述事實，並探討原因。
Step 1: 記憶體互動 - 讀取 (Memory Interaction - Read)
- 讀取所有相關記憶:
memory-bank/current/tasks.md(規劃了什麼)memory-bank/current/progress.md(實際執行過程)memory-bank/current/creative-*.md(設計決策)git log(提交歷史)
- 綜合分析: 將上述所有資訊結合起來，建立一個關於本週期的完整畫像。
Step 2: 復盤分析 (Retrospective Analysis)
- 客觀總結 (What happened?): 總結完成了哪些主要功能，指標數據如何。
- 分析亮點 (What went well?): 識別流程中的成功實踐、高效決策。
- 分析痛點 (What could be improved?): 識別流程中的瓶頸、錯誤的估算、技術債。
- 提煉學習 (What did we learn?): 總結出可供未來直接使用的、可操作的經驗教訓。
Step 3: 記憶體互動 - 寫入 (Memory Interaction - Write)
- 創建反思報告: 將上述所有分析結果，寫入到一個新的
memory-bank/current/reflection-[date].md文件中。 - (可選) 更新任務列表: 如果在反思中識別出新的技術債務，可以更新
memory-bank/current/tasks.md，將這些技債作為下個週期的 PENDING 任務。
3. CHECK ✓ (檢查)
Verification Checklist
- 報告已生成:
memory-bank/current/reflection-[date].md是否已成功創建？ - 分析是否深入: 報告是否不僅僅是羅列事實，而是提供了對原因的深入分析？
- 學習是否可操作: 總結的經驗教訓是否具體、可衡量、可應用？
4. ACT 改善 (行動)
Finalization
- 與用戶互動: 在生成報告後，AI 可以主動向用戶提問，以獲取更深層次的見解。例如：「報告已生成。從您個人的角度來看，本次週期最大的挑戰是什麼？」
- 提交報告: 將最終的
reflection-[date].md文件提交到版本控制系統。
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 · 72 lines · 18 tokens per session scan A 243291edbe03
reflect is a command published in the GitHub repository Zenobia000/cursor-agentic-coding-template (30 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 1,253 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.
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.