AI-Shifu Skills

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）: convert raw course material into optimized MarkdownFlow teaching scripts and deploy them as live AI-Shifu courses through a five-phase pipeline (segmentation, orchestration, generation, optimization, deployment).
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/

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 workflow, 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.

The report does not expose learner identities, raw course identifiers, or raw follow-up text. Business and credit data stay out of the report unless the user explicitly asks for the optional operations appendix. Reports default to Simplified Chinese; English is used only when explicitly requested.

Usage

Each skill keeps SKILL.md as the behavior source of truth. The name frontmatter field must match the directory name, using lowercase letters, digits, and hyphens only, as required by the Agent Skills standard. That format was originally developed by Anthropic and released as an open standard, and the Claude Code documentation states that its skills follow it. Claude Code itself is more permissive and treats name as an optional display label, but this repository follows the stricter standard rule so the skills stay portable across every skills-compatible agent. Human-readable display names live in the skill list above.

Course Authoring & Deployment Paths

Choose one path based on control needs:

Path A: End-to-End (Recommended)

Use when you want the fastest route from raw material to a live deployed course.

Prepare source material (transcript or course documents).
Run Phase 1–4 to produce optimized MarkdownFlow lesson scripts.
Run Phase 5 to build, import, and publish to the AI-Shifu platform.

Expected artifacts:

Structured segmentation
Lesson-by-lesson MarkdownFlow scripts
Course index and global variable table
Optimized lesson prompts and risk report
Live course on the AI-Shifu platform

Path B: Author Only

Use when you need optimized MarkdownFlow scripts without deploying. Sub-paths:

Segment only: Phase 1 for semantic segments and manual review.
Generate only: Phase 3 on pre-existing segments.
Optimize only: Phase 4 to audit and improve existing scripts.

Path C: Deploy Only

Use when you have pre-existing MarkdownFlow files ready to deploy:

Organize MarkdownFlow files in a course directory.
Run build --course-dir ./course-a/ to generate the import file.
Run import --new --json-file ./course-a/shifu-import.json to create the course.
Run publish <shifu_bid> to make it live.

Path D: Manage Existing

Use management commands (list, show, update, rename, reorder, delete, publish, archive) on courses already on the platform.

Validate Metadata

python3 scripts/validate_skill_quality.py

AI-Shifu

This suite is part of AI-Shifu's course authoring workflow: https://ai-shifu.com

Skills

At a glance

README