AI-Shifu Skills

中文 README

Reusable AI-Shifu skills for course production and improvement, from topic selection and deployment to privacy-safe learning analysis.

Included Skills

• AI-Shifu Course Creator（AI 师傅课程创作器）: teaching design, MarkdownFlow expression, and CLI operations for creating, editing, reviewing, publishing, managing, and analyzing AI-Shifu courses. The using agent selects and combines capabilities for the task.
• AI-Shifu Learning Report（AI 师傅学习报告）: analyze one AI-Shifu course and produce a privacy-safe course-learning-report.json plus a polished, self-contained, printable course-learning-report.html for teaching managers and teachers.
• Course Direction Advisor（课程选题顾问）: turn source materials into evidence-bound, market-fit course-topic decisions with competitor analysis, pricing guidance, and GO/HOLD/REWORK/NO-GO recommendations.

Repository Layout

skills/
  ai-shifu-course-creator/
  ai-shifu-learning-report/
  course-direction-advisor/
tools/
  ai-shifu-skill-release/

Build and Release Tools

The AI-Shifu Skill Release tool builds and verifies packages for ClawHub, SkillHub, WorkBuddy, and Doubao, and supports the existing publication workflow. Its implementation now lives in this repository, migrated from ai-shifu/ai-shifu-skill-release (the local ai-shifu-skill-build project).

Builds require Python 3.11+ and Git. They still fetch the committed skills from GitHub ai-shifu/skills remote main; local skill edits are never build inputs. From the repository root:

cd tools/ai-shifu-skill-release
python3 scripts/release.py build --skill-name ai-shifu-course-creator
python3 scripts/release.py verify dist/

Use the exact release directory printed by build. Building and verifying do not upload packages. See the tool README for release.toml configuration, channel-specific dependencies, and publication commands. Maintainer agents can read the tool’s release skill; it is separate from the three business skills above.

Learning Report Path

Use AI-Shifu Learning Report after a course has learning activity and you need an actionable single-course review. It reuses the course creator’s authenticated analytics capabilities, then turns aggregate progress, lesson health, engagement, audience, and audited follow-up signals into:

• a versioned, privacy-safe JSON data artifact with metric definitions and data-quality notes;
• an executive-first HTML report that works without external assets and prints cleanly to PDF;
• three to five evidence-linked teaching recommendations with confidence and a verification method.