SKILL Skill
description: "通用数字永生框架：从聊天记录、社交媒体、文档等多平台数据中蒸馏任何人的数字分身——支持自己、同事、导师、亲人、伴侣/前任、朋友、公众人物 7 种角色模板，接入国内外 12+ 数据平台。"
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Why use this skill
SKILL is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around ai development skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is OpenClaw Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in agenmod/immortal-skill, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
---
name: immortal-skill
description: "通用数字永生框架：从聊天记录、社交媒体、文档等多平台数据中蒸馏任何人的数字分身——支持自己、同事、导师、亲人、伴侣/前任、朋友、公众人物 7 种角色模板，接入国内外 12+ 数据平台。"
license: MIT
metadata: {"openclaw": {"requires": {"bins": ["python3"]}, "emoji": "♾️"}, "kit_version": "2", "personas": ["self", "colleague", "mentor", "family", "partner", "friend", "public-figure"], "platforms": ["feishu", "dingtalk", "wechat", "imessage", "telegram", "whatsapp", "slack", "discord", "email", "twitter", "social-archive", "manual"]}
---
# 数字永生
## 语言
根据用户**第一条消息**的语言，全程使用同一语言。
## 何时激活
- 用户要「蒸馏 XX」「做数字分身」「保留 TA 的方式/记忆」「让 AI 像 XX 一样」。
- 用户提供关于某人的材料，希望生成可加载的 Agent Skill 包。
## 核心理念
**选择角色 → 多平台采集 → 分维度提取（procedure / interaction / memory / personality）→ 证据分级 → 冲突合并 → 输出符合 Agent Skills 的技能目录。**
## 路径约定
- 本 Skill 根目录记为 **`{baseDir}`**。
- 生成物默认写入 `./skills/immortals/<slug>/`。
- `slug`：小写字母、数字、连字符，与最终 `SKILL.md` 的 `name` 一致。
## 操作顺序
### Phase 0：选择角色模板
向用户询问蒸馏对象的角色，读取对应模板：
```
你想蒸馏谁？
[1] 🪞 自己（全维度数字分身）
[2] 🏢 同事（工作方式与沟通风格）
[3] 🎓 导师/Mentor（教学方式与指导智慧）
[4] 🏠 亲人（家族记忆与生活智慧）
[5] 💕 伴侣/前任（关系记忆与互动模式）
[6] 🤝 朋友（友谊互动与共同经历）
[7] 🌐 公众人物（公开方法论）
```
读取 `{baseDir}/personas/<选择>.md` 了解该角色的特有维度与要求。
同时读取 `{baseDir}/personas/_base.md` 了解通用维度。
### Phase 1：伦理确认
根据角色模板中的伦理要求，在收集材料前告知用户。不同角色的伦理侧重：
- **同事/导师**：限团队内部对齐与培训
- **亲人（已故）**：确认其他家人是否应知情
- **伴侣/前任**：确认目的是正面回忆；严格脱敏
- **公众人物**：仅限公开资料；须有可追溯的公开出处
- **自己**：注意聊天中他人发言的脱敏
### Phase 2：收集材料
读取 `{baseDir}/recipes/intake-protocol.md`，按角色类型确定数据源。
提供以下采集方式：
```
材料怎么提供？
[A] 自动采集（推荐）
飞书 / 钉钉 / Slack / Discord / Telegram / Email
→ 扫描频道 → 拉取消息
[B] 本地数据库
微信（需第三方
...