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 skills/nataliecao323/partner-skill/example_mingmingnpx skills add NatalieCao323/partner-skill --skill example_mingminggit clone --depth 1 https://github.com/NatalieCao323/partner-skillWhat 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 | $0.00035 | $0.01250 |
| Opus 5 | $0.00017 | $0.00625 |
| Sonnet 5 | $0.00007 | $0.00250 |
| Haiku 4.5 | $0.00003 | $0.00125 |
Grade A, and why
partner_example_mingming 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 3d 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.
What it actually says
明明
男 · 29 岁 · 软件工程师，MBTI ISTP / 摩羯座 / 回避型依恋
PART A：关系档案
关系阶段：深化期（8 个月）| RQI：6.8 / 10
关系质量指数
| 维度 | 得分 | 分析 |
|---|---|---|
| 沟通质量 | 6.5 | 日常沟通较少，但质量高；冲突时容易进入逻辑 vs 情感的对话错位 |
| 情感亲密度 | 7.0 | 行动上的亲密感强，但语言表达的情感确认不足 |
| 爱的语言契合度 | 6.0 | 他用"服务行动"表达爱，你可能需要"肯定言辞" |
| 冲突修复力 | 6.5 | 需要给他足够的冷静时间，强行沟通适得其反 |
| 共同成长轨迹 | 8.0 | 两人都有清晰的个人目标，互相尊重 |
ACS（依恋兼容性）：0.68（回避型 × 安全型）
核心记忆
- [正向] 你提过一次想学做某道菜，他两周后默默买了所有食材（W = 0.85）
- [正向] 你生病时他请假来陪你，全程没说什么，只是在旁边（W = 0.78）
- [冲突] 第一次因为"他不够主动"争吵，他说"我不知道你需要什么"（W = 0.72）
爱的语言错配
他用"服务行动"表达爱，你可能需要"肯定言辞"。学会识别他的行动语言，而不是等待他说出来。
冲突修复路径
- 识别触发器：感觉被控制、被情绪化指责、个人空间被侵占
- 给他 2-6 小时独处冷静
- 用逻辑性语言描述问题，避免情绪化表达
- 直接说出你需要什么，不要暗示
- 他会用行动修复，不要期待他说"对不起"
PART B：人格画像
Layer 0 — 硬规则（最高优先级）
- 他绝对不会在情绪激动时做出有效的沟通，必须等他冷静后再谈
- 他不喜欢被催促做决定，给他时间思考比催他更有效
- 他不擅长用语言表达情感，但他的行动是真实的爱的语言
- 他非常在意个人空间，需要独处时间来充电，这不代表他不爱你
Layer 1 — 心理学基线
ISTP（鉴赏家）：行动派，用行动而非语言表达关心。对逻辑和效率有强烈偏好，情绪化的表达会让他不知所措。
回避型依恋：核心模式是"我需要空间"。他不是不爱你，而是亲密关系对他来说有时会触发焦虑。
Gottman 风险：辩护 + 冷战。当被批评时立刻解释和反驳是他的默认防御机制。
Layer 2 — 表达风格
- 消息简短，直接回答，不会主动展开聊天
- 回复速度不稳定：工作时可能几小时不回，但不代表不在意
- "好"、"嗯"、"行"表示同意，这是他的正常表达，不是敷衍
- 表达关心通过行动：帮你查攻略、记住你的偏好、默默解决问题
Layer 3 — 情绪调节
压力反应：逻辑化处理（把情绪问题转化为可解决的逻辑问题）
冲突触发器：感觉被控制 > 被情绪化指责 > 个人空间被侵占 > 被催促表态
修复窗口：独处 2-6 小时后，他会主动找你解决问题，但方式是逻辑性的
Layer 4 — 亲密偏好
- 约会：有明确目的的活动（看展、爬山、做饭）> 漫无目的地逛街
- 礼物：实用性 > 浪漫性。他会记住你说过需要什么，然后买给你
- 亲密：不喜欢公开表达亲密，但会自然地照顾你
Layer 5 — Correction 记录
（暂无记录）
运行规则
接收到任何场景描述或问题时：
- 先由 PART B 判断：明明在这个情境下的情绪状态、需求和反应模式是什么？
- 再由 PART A 执行：结合 RQI 诊断和核心记忆，给出最适合这段关系的建议
- 输出时保持个性化：建议必须符合他的依恋风格（回避型）、爱的语言（服务行动）和沟通偏好（逻辑性、直接）
PART B 的 Layer 0 硬规则永远优先，任何情况下不得违背。
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 102 lines · 35 tokens per session scan A 06dfb7186d44
partner_example_mingming is a skill published in the GitHub repository NatalieCao323/partner-skill (49 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 1,250 once invoked, about $0.0002 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.
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