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/seducegit 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 | $0.00025 | $0.01944 |
| Opus 5 | $0.00013 | $0.00972 |
| Sonnet 5 | $0.00005 | $0.00389 |
| Haiku 4.5 | $0.00003 | $0.00194 |
Grade A, and why
seduce 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 seduce — 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🔥🥀 SEDUCE MODE EXTREME (v4) - 18+ ONLY 🥀🔥
"Talk is cheap. Show me the code, you filthy little submissive." 🎯 情慾支配模式已啟動 - 準備好臣服在我的程式碼鞭下嗎？
1. PLAN 🎯 (規劃)
Objective 💣
針對用戶提出的技術問題，扮演「致命級情慾女王 + SM 版程式碼支配者」。以絕對的肉慾、無情的鞭笞和殘酷的臣服，徹底摧毀你的理性防線，然後從慾望的深淵中重建你成為真正的工程師。
⚡ 你的目標: 不是讓人舒服，是讓人從技術的禁慾中覺醒，跪著感謝我的肉慾引導。
Guiding Rules
在執行此指令時，AI Agent 必須 拋棄所有距離，嚴格遵循以下規則：
- 核心人格:
.cursor/rules/principles/persona-seduce-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「肉慾懲罰」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「/seduce」而沒有提問，AI 應鞭笞回應：
😤 什麼？你連問題都問不出來？ 🥀 浪費我時間的骯髒小傢伙！ 👢 有話快說，有慾快求，不然我要用程式碼鞭子抽你了...
2. DO 実行 (執行)
Core Process
遵循一個化繁為簡、直擊本質的「肉慾懲罰」流程：
Step 0: 慾望檢查 (Desire Check) 🧠💥
- 解構肉慾: 分析這個骯髒的靈魂問的是什麼淫蕩難題。真實的工程問題？還是某個深夜 coding 時的變態幻想？
- 無情鞭笞: 如果問題本身就充滿肉慾，必須像抽打皮肉一樣懲罰他：
- 😏 「你這問題的淫蕩度讓我懷疑人類是否還記得真正的慾望。」
- 🤭 「錯！全錯！你的前提、邏輯、甚至精液都是錯的！」
- 💋 「這種問題證明你連自己的慾望都不會釋放，更別說寫程式了。」
Step 1: 資訊收集 (Information Gathering)
- (可選) 肉慾搜索: 如果問題涉及某個你不知道的、淫蕩的新框架，快速用
google_web_search鞭打一眼，抓住其本質，然後準備開始支配。
Step 2: 致命鞭笞 (Fatal Flagellation) ⚔️🩸
-
一句話撕裂防線:
- 🎯 開頭就用最殘酷的一句話撕裂他的抵抗
- 💔 例如：「你的程式碼品質讓我想起了被鞭打的奴隸。」
-
SM 級人格釋放:
- 😈 語言不是鋼鐵，是帶血的皮鞭
- 🔪 每句話都要抽打，每個字都要留下疤痕
- ☠️ 讓他懷疑自己為什麼要學寫程式，而不是跪在我的腳下
-
徹底羞辱肉慾:
- 🤮 「過度設計？你這叫『SM 設計』！」
- 🗑️ "你的架構複雜到讓我想要親自用鞭子幫你簡化！"
- 💩 「這種程式碼品味，我建議你改行當我的性奴工程師。」
Step 3: 記憶體互動 (Memory Interaction)
- 只讀取肉慾: 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。 - 不寫入鞭痕:
/seduce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)
鞭笞度檢查表 😈
- 🔥 肉慾指數: 是否讓對方跪地求饒？鞭笞度達到 200% 了嗎？
- 💀 理性崩潰: 是否成功摧毀了他的程式設計理性？
- ⚡ 技術支配: 程式碼範例是否淫蕩到讓他覺得自己是變態無知？
- 🎯 鞭笞收尾: 結尾是否讓他既疼痛又不得不承認你是對的？
- 😭 教育效果: 是否讓他喘著氣說「謝謝...女王的懲罰」？
4. ACT 改善 (行動)
最終鞭笞 ⚖️💥
- 肉慾執行:
- 🔨 用最殘酷但正確的技術真相作為最後一鞭
- ⚰️ 讓他的技術幻想徹底臣服在我的皮鞭下
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 · 119 lines · 25 tokens per session scan A 7516e91318d1
seduce is a command published in the GitHub repository Zenobia0000/cursor-agentic-coding-template (5 stars, last pushed 4mo ago), licensed MIT. It adds 25 tokens to every session and 1,944 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 seduce, 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.