mdf-teaching-optimizer

中文文档

Audit and optimize existing MarkdownFlow teaching prompts to improve coverage alignment, interaction effectiveness, and syntax stability.

Use Cases

• Review existing lesson prompts against source materials to detect missing or weakly taught points.
• Improve teaching logic flow and interaction quality without rewriting the entire course.
• Reduce runtime risks from variable misuse and unstable MarkdownFlow syntax.

Boundaries

• This component optimizes existing prompts; it is not a from-scratch course generator.
• It does not replace human instructional judgment for domain-specific pedagogy.
• It outputs optimization suggestions and revised prompt text, not final publishing assets.

Quickstart (3 minutes)

python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
mdf-teaching-optimizer --prompt examples/input_prompt.md --source examples/source_material.md --output output/result.json
python -m json.tool output/result.json | head -n 40

Minimal Runnable Example

Input:

• examples/input_prompt.md
• examples/source_material.md

Output includes:

• summary
• findings (risk level, checks, recommendations)
• optimized_prompt

Architecture: Skill Core + Adapters

• Skill Core: src/mdf_teaching_optimizer/core.py
  • Platform-agnostic audit + optimization logic.
• CLI Adapter: src/mdf_teaching_optimizer/cli.py
  • Local executable interface for pipelines.
• OpenClaw Adapter: adapters/openclaw
• Claude Adapter: adapters/claude
• Codex Adapter: adapters/codex

Multi-Agent Compatibility

• OpenClaw example: examples/openclaw_call.md
• Claude example: examples/claude_call.md
• Codex example: examples/codex_call.md

AI-Shifu Ecosystem

This repository is one reusable optimization component in AI-Shifu's course production workflow.

• Website: https://ai-shifu.com

Development

pip install -e ".[dev]"
ruff check .
pytest -q