Agent Skills: skill-creator
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
ID: jamesgray007/hoai-course/skill-creator
214
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Skill Files
The files that make up skill-creator.
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- nsfc-qc当用户明确要求"标书QC/质量控制/润色前质检/引用真伪核查/篇幅与结构检查"时使用。对 NSFC 标书进行只读质量控制：并行多线程独立检查文风生硬、引用假引/错引风险、篇幅与章节分布、逻辑清晰度等，最终输出标准化 QC 报告；中间文件默认归档到 `project_root/.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/nsfc-qc/{yyyy-mm-dd-hh-mm}/`，并兼容 legacy `.nsfc-qc/` 读取/清理。
- running-meetingsHelp users master the art of facilitating productive, decision-oriented meetings that unblock teams, strengthen interpersonal bonds, and accelerate shipping velocity.
- obviously-awesomeDefine product positioning by mapping competitive alternatives, unique attributes, and best-fit customers to the right market category. Use when the user mentions "positioning", "competitive alternatives", "how to position", "market category", "positioning canvas", "repositioning", "category creation", "what category are we in", or "why prospects dont get what we do". Also trigger when launching a new product, entering a crowded market, or diagnosing why prospects dont grasp the product''s value. Covers the positioning canvas and team workshops. For customer jobs analysis, see jobs-to-be-done. For go-to-market, see crossing-the-chasm.
- data-client-graphql-setupSet up @data-client/graphql for GraphQL APIs. Configures GQLEndpoint with auth and custom options. Use after data-client-setup detects GraphQL patterns.
- building-workflowsBuild, edit, test, enable, and monitor PostHog workflows over MCP. Author the action/edge graph so it runs and opens cleanly in the visual editor, then change drafts surgically with patch operations. Use when asked to build, set up, automate, change, fix, or debug a workflow, campaign, broadcast, drip sequence, or event-triggered automation in the workflows product.
- data-engineeringData engineering, machine learning, AI, and MLOps. From data pipelines to production ML systems and LLM applications.