ai-specialist
提供 AI 应用开发、MCP 服务器工程、提示词工程与智能体框架集成能力。当需要构建或优化基于大模型的功能、工作流或平台集成时使用。
npx skills add majiayu000/claude-skill-registry --skill ai-specialist --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Ai Specialist 提供 AI 应用开发、MCP 服务器工程、提示词工程与智能体框架集成能力。当需要构建或优化基于大模型的功能、工作流或平台集成时使用。 ## Skill Index <!-- AUTO-GENERATED-SKILL-INDEX:START --> 以下索引由 `node scripts/update-skill-index.js` 自动生成,用于让 Claude 在顶层专家触发后继续路由到最相关的子技能。 ### Claude 使用说明 1. 先将用户当前任务与每个子技能的 `触发语义` 进行语义匹配,不要只看目录名。 2. 一旦找到最相关的子技能,立即打开其 `入口文件` 指向的 `SKILL.md`,把它作为下一层入口。 3. 进入子技能后,再根据该子技能自己的说明按需加载同目录下的 `references/`、`scripts/`、`assets/`,不要在顶层专家中预先展开大段细节。 4. 如果多个子技能都相关,先加载最贴近主目标的那个,再按需补充其他子技能,避免一次性加载过多上下文。 5. 下方 `入口文件` 路径相对于项目根目录,可直接用于 `Read` 操作。 ### 子技能索引 #### agents-orchestrator (1) - `agents-orchestrator` - 触发语义: Autonomous pipeline manager that orchestrates the entire development workflow. You are the leader of this process. - 入口文件: `.claude/skills/ai-specialist/references/domains/agents-orchestrator/SKILL.md` #### ai-data-remediation-engineer (1) - `ai-data-remediation-engineer` - 触发语义: Specialist in self-healing data pipelines — uses air-gapped local SLMs and semantic clustering to automatically detect, classify, and fix data anomalies at scale. Focuses exclusively on the remediation layer: intercepting bad data, generating deterministic fix logic via Ollama, and guaranteeing zero data loss. Not a general data engine
- Skill Index
- Claude 使用说明
- 子技能索引
- Notes
What does the ai-specialist skill do?
提供 AI 应用开发、MCP 服务器工程、提示词工程与智能体框架集成能力。当需要构建或优化基于大模型的功能、工作流或平台集成时使用。
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-specialist --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
