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Convert raw course material into optimized, runnable MarkdownFlow teaching scripts and deploy them as live courses through a five-phase pipeline covering segmentation, orchestration, generation, optimization, and deployment.
Passed 8 of 22 test cases with the skill, and 0 of 22 without it.
8 fixed0 passed either way14 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
The instructions your model reads when the skill loads.
Convert raw course material into runnable, optimized MarkdownFlow lesson scripts and deploy them as live AI-Shifu courses.
See references/language-resolution.md for the full policy.
Resolve target language with this strict priority:
explicit_output_language_requesttarget_language_parametersession_language_preferenceprompt_language_detectionsource_material_dominant_languagedefault_fallback_language (en-US)Use these optional controls across all phases:
course_profile (json): audience level, prerequisite level, lesson duration target, lesson count target, and assessment mode.delivery_constraints (json): interaction density, platform limits, must-cover topics, avoid topics, and non-negotiable source fragments.See references/input-contract.md for recommended object shapes.
Phase 1: Segmentation → Phase 2: Orchestration → Phase 3: Generation → Phase 4: Optimization → Phase 5: Deployment
test cases passed, vs 0 / 22 without
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