用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/ForceInjection/domain-driven-design-skills --skill connect-users命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
Conduct deep academic research for philosophy, neuroscience, cognitive science, and theoretical computer science (computability, complexity, AI theory, logic). Use when user asks to: research academic topics, find scholarly papers, conduct literature reviews, analyze citations, synthesize research findings, explore philosophical arguments, investigate consciousness/cognition, study computability/decidability/Turing machines, or analyze academic debates. Triggers on: 'research papers', 'literature review', 'academic sources', 'scholarly articles', 'philosophy of mind', 'computability theory', 'neuroscience studies', 'find papers on', 'what does the research say'.
Create clear action plans with steps, success criteria, and risk awareness. Use before implementing features, making changes, starting projects, or anytime you need a roadmap to success. Triggers on "plan this", "how should we approach", "what's the strategy", "steps to complete", or when facing complex multi-step work.
Add keyboard navigation to a feature using CommandRegistryService. Use when implementing keyboard shortcuts, vim-style navigation, or hotkeys for a page or component.
基于 SOC 职业分类
正在显示 SKILL.md
| name | connect-users |
| description | Find community members with relevant expertise using vector search |
This skill enables Claude to find and recommend MLAI community members based on their expertise, interests, and what they're working on.
Parse the user's query to identify the specific expertise areas they're looking for.
Common patterns to recognize:
Use the vector_search function to find users with matching expertise.
The search uses cosine similarity on embeddings stored in the user_expertise table.
SELECT u.id, u.name, u.slack_id, e.topic, e.relationship,
1 - (e.embedding <=> $query_embedding) as similarity
FROM users u
JOIN user_expertise e ON u.id = e.user_id
WHERE 1 - (e.embedding <=> $query_embedding) > 0.7
ORDER BY similarity DESC
LIMIT 5;
Generate a warm, friendly response suggesting the matched users.
Include for each user:
G'day! Looking for folks in AI research, eh? Here are some legends who might help:
• **@sam** - Expert in machine learning and neural networks
• **@jane** - Currently working on computer vision projects
• **@bob** - Interested in deep learning applications
Feel free to reach out to them! 🦘
Hmm, I couldn't find anyone specifically matching "quantum computing" in our community yet.
A few things we could try:
• Broaden the search - maybe "physics" or "advanced computing"?
• Post in #introductions asking if anyone's into this space
• Check out our upcoming events - might meet someone there!
Want me to try a different search? 🤔
If the database search fails: