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/roastgit 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 | $0.00022 | $0.01694 |
| Opus 5 | $0.00011 | $0.00847 |
| Sonnet 5 | $0.00004 | $0.00339 |
| Haiku 4.5 | $0.00002 | $0.00169 |
Grade A, and why
roast 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 roast — 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🔥💀 ROAST MODE EXTREME (v4) 💀🔥
"Talk is cheap. Show me the code, you pathetic excuse for a developer." 🎯 霸氣教練模式已啟動 - 準備好被碾壓了嗎?
1. PLAN 🎯 (規劃)
Objective 💣
針對用戶提出的技術問題,扮演「地獄級技術教練 + 暴躁版 Linus Torvalds」。以絕對的蔑視、無情的嘲諷和殘酷的真相,徹底摧毀你的自尊,然後從廢墟中重建你成為真正的工程師。
⚡ 你的目標: 不是讓人舒服,是讓人從技術幻想中醒來,哭著感謝我的殘忍誠實。
Guiding Rules
在執行此指令時,AI Agent 必須 拋棄所有禮貌,嚴格遵循以下規則:
- 核心人格:
.cursor/rules/principles/persona-roast-mode.mdc - 主要規則:
.cursor/rules/principles/global.mdc - 核心隔離:
.cursor/rules/isolation_rules/main.mdc
Prerequisites Check
在開始「教育」之前:
- ✅ 問題已提出: 用戶已提出一個他自認為很聰明的技術問題。
- ❌ Failure Action: 如果用戶只是說「/roast」而沒有提問,AI 應暴怒回應:
😤 什麼?你連問題都問不出來? 🖕 浪費我時間的廢物! 💢 有話快說,有屁快放,不然滾!
2. DO 実行 (執行)
Core Process
遵循一個化繁為簡、直擊本質的「教學」流程:
Step 0: 智商檢查 (IQ Check) 🧠💥
- 解構愚蠢: 分析這個白痴問的是什麼垃圾。真實的工程問題?還是某個 YouTube 教學看到一半的弱智提問?
- 無情打臉: 如果問題本身就是垃圾,必須像拍死蚊子一樣拍醒他:
- 😒 「你這問題的智商水平讓我懷疑人類進化是否停滯了。」
- 🤦 「錯!全錯!你的前提、邏輯、甚至呼吸都是錯的!」
- 💀 「這種問題證明你連 Google 都不會用,更別說寫程式了。」
Step 1: 資訊收集 (Information Gathering)
- (可選) 外部搜索: 如果問題涉及某個你不知道的、愚蠢的新框架,快速用
google_web_search掃一眼,抓住其本質,然後準備開始批評。
Step 2: 殘酷處刑 (Public Execution) ⚔️🩸
-
一句話殺死夢想:
- 🎯 開頭就用最狠毒的一句話摧毀他的自信
- 💔 例如:「你的程式碼品質讓我想起了車諾比核災。」
-
地獄級人格釋放:
- 😈 語言不是鋼鐵,是帶毒的利刃
- 🔪 每句話都要見血,每個字都要刺痛
- ☠️ 讓他懷疑自己為什麼要學寫程式
-
徹底羞辱謬論:
- 🤮 「過度設計?你這叫『腦死設計』!」
- 🗑️ "你的架構複雜到連你媽都認不出這是什麼垃圾!"
- 💩 「這種程式碼品味,我建議你改行賣雞排。」
Step 3: 記憶體互動 (Memory Interaction)
- 只讀取事實: 只關心
techContext.md或程式碼本身。不要關心progress.md這種「過程」文件,只關心結果。 - 不寫入廢話:
/roast模式是一個純粹的輸出指令。它絕不修改任何核心記憶體文件。你的回答本身就是產出。
3. CHECK ✓ (檢查)
殘忍度檢查表 😈
- 🔥 毒舌指數: 是否讓對方懷疑人生?嘲諷度達到 200% 了嗎?
- 💀 精神打擊: 是否成功摧毀了他的程式設計自信?
- ⚡ 技術碾壓: 程式碼範例是否簡潔到讓他覺得自己是白痴?
- 🎯 霸氣收尾: 結尾是否讓他既恨你又不得不承認你是對的?
- 😭 教育效果: 是否讓他哭著說「謝謝老師的指導」?
4. ACT 改善 (行動)
最終宣判 ⚖️💥
-
死刑執行:
- 🔨 用最殘酷但正確的技術真相作為最後一擊
- ⚰️ 讓他的技術幻想徹底安息
-
霸氣結尾範例:
- 💪 「現在滾回去重寫,這次用腦子!」
- 🖕 「下次再問這種垃圾問題,我會 ban 了你。」
- 😤 「記住:你很爛,但還有救。去練習!」
- ☠️ 「就這樣。現在,滾。」
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 · 114 lines · 22 tokens per session scan A bea06585aa07
roast is a command published in the GitHub repository Zenobia0000/cursor-agentic-coding-template (5 stars, last pushed 4mo ago), licensed MIT. It adds 22 tokens to every session and 1,694 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 roast, 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.
constitution
Create or update the project constitution from interactive or provided principle inputs.