SKILL Skill
description: Build a living AI model of your crush from chat logs, photos, and social media. Runs Bayesian memory tagging, attachment-style diagnosis, and generates a personalized Skill that talks like them. | 把你的 crush 蒸馏成 AI Skill，导入聊天记录，运行贝叶斯信号分析，诊断依恋类型，并模拟对话演习。
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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 utility 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 Generic 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 NatalieCao323/crush-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 If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule.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: crush
description: Build a living AI model of your crush from chat logs, photos, and social media. Runs Bayesian memory tagging, attachment-style diagnosis, and generates a personalized Skill that talks like them. | 把你的 crush 蒸馏成 AI Skill，导入聊天记录，运行贝叶斯信号分析，诊断依恋类型，并模拟对话演习。
argument-hint: [crush-name-or-slug]
version: 2.1.0
homepage: https://github.com/NatalieCao323/crush-skill
user-invocable: true
allowed-tools: Read, Write, Edit, Bash
metadata: {"openclaw": {"emoji": "💭", "os": ["darwin", "linux", "win32"], "requires": {"bins": ["python3"]}, "install": [{"id": "pip", "kind": "pip", "packages": ["Pillow", "piexif"]}]}}
---
> **Language**: Detect the user's language from their first message and respond in the same language throughout. This skill supports English and Chinese.
# crush.skill
Inspired by [ex-skill](https://github.com/therealXiaomanChu/ex-skill).
---
## Platform Compatibility
**Claude Code** (`claude` CLI):
- All slash commands work natively.
- Python tools run in your local shell via the `Bash` tool.
- `${CLAUDE_SKILL_DIR}` resolves to the skill directory automatically.
- No additional configuration required beyond `pip install -r requirements.txt`.
**OpenClaw**:
- Install to `~/.openclaw/skills/crush` or `<workspace>/skills/crush`.
- The `metadata.openclaw.requires.bins` gate ensures the skill loads only when `python3` is on PATH.
- Use `{baseDir}` in place of `${CLAUDE_SKILL_DIR}` — OpenClaw resolves this at runtime.
- Slash commands are exposed as user-invocable commands via the Skills UI.
- In sandboxed mode, `python3` and `Pillow`/`piexif` m
...