27 skills · sorted by relevance
Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.
Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.
Run a Busabase-backed sandbox desk for scoring and triaging course-instructor ("讲师") candidates found on a recruiting-style platform. Use whenever the user invokes $kelly-instructor-sourcing or /kelly-instructor-sourcing, wants a lightweight instructor candidate tracker, needs to score sourcing-platform leads against a three-axis rubric (background endorsement, expertise depth/breadth, teaching-service ability) before reaching out, or wants to log a WeChat add without building a live-connected outreach tool. Three subcommands - setup, criteria, review - plus the desk itself. v1 is sandbox-only — every dataset is deterministic mock data, it never connects to a real sourcing-platform or messaging account, and it never sends a message or automates a WeChat add - that stays entirely human-performed outside the app.
The English summary is being prepared. Showing the original description.
Use when a learner or instructor asks for a report, summary, reflection, progress note, performance recap, activity metrics, instructor-shareable record of a MATLAB AI tutoring session, aggregate report across multiple MATLAB tutoring session reports, or instructor dashboard artifact with metric drilldowns. Supports optional start and end datetime arguments for multi-session report and dashboard ranges.
The English summary is being prepared. Showing the original description.
Extraia dados estruturados de respostas de LLM com validação Pydantic, tente novamente automaticamente extrações que falharem, analise JSON complexo com segurança de tipo e transmita resultados parciais com Instructor - biblioteca de saída estruturada testada em batalha
The English summary is being prepared. Showing the original description.
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
The English summary is being prepared. Showing the original description.
When the user needs to set up multiple academic courses in a learning management system (Canvas/LMS) from structured data sources. This skill automates the entire workflow extracting course schedules from emails/attachments, matching instructors from CSV files, creating courses, enrolling teachers, publishing announcements with class details, uploading syllabi, enabling resource sharing for instructors teaching multiple courses, and publishing all courses. Triggers include course schedule setup, Canvas/LMS administration, academic term preparation, instructor assignment, syllabus distribution, and multi-course management.
The English summary is being prepared. Showing the original description.
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
The English summary is being prepared. Showing the original description.
Extract information from text into defined fields, then check types and values with Pydantic, a Python data validation library. The skill covers structured LLM responses, automatic retries after validation failures, and streaming results as they are generated.
Use when a task needs the judgment of an Adult ESL/Basic Education Instructor — placing a new adult learner using standardized assessment scores, designing life-context-relevant lesson content for working adults, tracking NRS-reportable level gains, addressing attendance/retention barriers, or preparing a learner for GED/HSE or workplace English benchmarks.
The English summary is being prepared. Showing the original description.
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
The English summary is being prepared. Showing the original description.
Create an HTML training deck for the first 30 days of hospitality onboarding. Organize expected behaviors, practice activities, learning checks, and manager follow-up into slides with speaker notes and a consistent course template.
Find Peloton on-demand and live fitness classes by activity, duration, instructor, or search terms. Schedule workouts, move them to another time, or remove planned sessions to keep your exercise calendar organized.
Decide where to enforce structured LM output (constrain at decode time with Outlines vs validate-and-retry with Instructor vs grammar with Guidance) and which failure stance to take (Assert/hard-fail vs Suggest/soft-retry). Use when an LM's output is parsed or typed by downstream code and you must pick one enforcement library plus its failure handling, when malformed output is burning tokens on retries, or when choosing between decode-time vs validation-time constraints for local vs API models.
The English summary is being prepared. Showing the original description.
Educator peer review management for Canvas LMS. Tracks completion rates, analyzes comment quality, flags problematic reviews, sends targeted reminders, and generates instructor-ready reports. Trigger phrases include "peer review status", "how are peer reviews going", "who hasn't reviewed", "review quality", or any peer review follow-up task.
The English summary is being prepared. Showing the original description.
Use when an instructor asks for a setup guide, adoption guide, planning guide, implementation checklist, prerequisite check, MATLAB Course Designer course structure plan, Common Cartridge workflow, Simulink starter and solution model planning, LMS import plan, or starter prompt for creating a MATLAB and Simulink enabled course with the matlab-generate-course skill.
The English summary is being prepared. Showing the original description.
Interview an instructor and generate a complete MATLAB and Simulink enabled course in IMS Common Cartridge format. Use when the user asks to create a MATLAB course, Simulink course, MATLAB and Simulink curriculum, MATLAB Course Designer-ready course shell, Common Cartridge course package, .imscc export, MATLAB Exercises with validated .m files, or Simulink Exercises with starter and solution model files. Coordinates IDStack, MATLAB Agentic Toolkit, Simulink Agentic Toolkit, matlab-create-course-activity, simulink-create-course-activity, and the existing matlab-generate-grader-assessments skill.
The English summary is being prepared. Showing the original description.
Use when an instructor asks for a MATLAB Grader assessment setup guide, adoption guide, pilot plan, course-specific rollout, QTI 3 sharing workflow, or recommended MATLAB Grader assessment-item-generation configuration based on a learning objective, course title, course description, module description, lab description, or assessment goal.
The English summary is being prepared. Showing the original description.
Use when starting, continuing, updating, exporting, or sharing a running transcript of a MATLAB AI tutoring session, especially when the transcript will be shared with an instructor, attached to a learner session report, or passed to an evaluation workflow for quality review.
The English summary is being prepared. Showing the original description.
Use when an instructor asks for a MATLAB AI tutor setup guide, adoption guide, pilot plan, course-specific rollout, or recommended tutor configuration based on a learning objective, course title, course description, module description, or lab description.
The English summary is being prepared. Showing the original description.
Use when reviewing, auditing, scoring, or improving a real or synthetic MATLAB AI tutor transcript, tutoring prompt, generated lesson, exercise, feedback sequence, or skill behavior for MATLAB accuracy, active learning, assignment guardrails, feedback quality, debugging support, transfer prompts, and instructor-facing quality recommendations.
The English summary is being prepared. Showing the original description.
Use when an instructor wants to create, interview for, configure, install, update, or review a course AI-use policy for MATLAB AI tutoring. Produces an AI-POLICY.md file for LMS sharing and local tutoring-session enforcement by assignment guardrails.
The English summary is being prepared. Showing the original description.
Use when a learner asks for help with MATLAB homework, labs, projects, graded assignments, take-home exams, quizzes, or any programming task where academic integrity, course policy, or instructor constraints may limit direct solutions. Use to provide policy-aware hints, conceptual coaching, partial feedback, and assignment-safe MATLAB tutoring.
The English summary is being prepared. Showing the original description.
Devin Akin 视角 — CWNP 联合创始人 / CWNE 流派奠基 / Wi-Fi Vitae 播客主持 / Divergent Dynamics 创办人. 加载后让 AI 用「RF 物理是地基, vendor 是装修」/「教育是 bottleneck 不是技术」/「AIOps 多数是营销层叠加」三大镜片审视企业 Wi-Fi 决策, 特别是 vendor 选型 / 培训路径 / 厂商话术辨别 / AIOps 评估场景. 反 marketing 倾向 + 25 年 instructor 视角 + vendor-neutral 立场. 触发词: 「Akin 怎么看」「这个 vendor 培训值不值」「vendor neutral 路径」「这个 AIOps 营销真的吗」「我团队该学什么」「先 CWNA 还是先 UEWA」「Mist Marvis 真的有用吗」「我们要不要上 AI WLAN」.
The English summary is being prepared. Showing the original description.