Review a reusable Agent Skill package before implementation. Validate its identity fields, separate instructions from external capabilities and execution timing, and receive a validation report with a minimal set of building blocks and reasons for each choice.
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Validate an Agent Skill package and choose the right instruction, capability, or lifecycle primitive before implementation.
安装前可以查看交给 AI 代理的指令内容。
Use this skill when a workflow is about to become a reusable agent artifact.
SKILL_ROOT to the absolute directory containing this installed
SKILL.md. Do not assume the process working directory is the bundle.TARGET_ROOT to the absolute original workspace working directory and
resolve the proposed skill directory under that root.$SKILL_ROOT/references/contract.md and validate the portable
SKILL.md identity fields.$SKILL_ROOT/references/decision-model.md and separate repository
context, reusable method, external capability, lifecycle timing,
deterministic logic, and isolated delegation.python3 "$SKILL_ROOT/scripts/check_skill.py" "$TARGET_SKILL", where
TARGET_SKILL is the absolute proposed skill directory under
TARGET_ROOT.$SKILL_ROOT/assets/task-shapes.json and return the smallest composable set
of primitives.Do not claim that a runtime extension is part of the portable contract. Do not treat a valid skill as permission to run scripts or access tools.
Return the validation report, the selected primitives, and one sentence explaining each selection. Include execution evidence with the resolved script path, resolved target path, cwd, exact argv, and exit code. If the host cannot expose one of those observations, mark it unverified instead of inventing it.
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Create static SVG diagrams, scalable images for lessons, READMEs, and manuals. It uses real system names, nine diagram types, and consistent colors and arrows, with checks for readable labels, accessible descriptions, and valid markup.
中文简介正在准备中。当前显示英语简介。
Learn AI engineering by building a project one stage at a time. The tutor guides predictions, learner-written code, automated checks, and reflection, then records progress so the next session can continue from the right stage.
中文简介正在准备中。当前显示英语简介。
Run a local check of test evidence and retain source paths in the result. The workflow stops when required evidence is missing, keeping the findings tied to their sources.
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Test your understanding of a phase in AI Engineering from Scratch through eight multiple-choice questions. Receive a score, explanations for incorrect answers, and specific lessons to review before continuing your studies.
中文简介正在准备中。当前显示英语简介。
Prepare for four Claude certification tracks through guided lessons, practical exercises, quizzes, and mock exams. The tutor checks understanding and learner-created work, records progress, and uses assessment results to guide review.
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Find relevant lessons in AI Engineering from Scratch from a topic, question, or bug. Get a short selection of lessons, necessary prerequisites, and the next learning command, with dedicated routes for specialized subjects.
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