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Professional Reskilling Designer | Skills Pool

技能档案

Professional Reskilling Designer

Adapts academic computing content into realistic training paths for adult learners, continuing education, and professional reskilling. Use when an instructor or trainer needs a course, TP, project, or assessment reframed for a professional audience with heterogeneous backgrounds, stronger contextualization, and employment-oriented outcomes.

alainlebret0 星标2026年3月20日

职业
分类: 机器学习

技能内容

Goal

Transform higher-education content into a credible professional training path for adults in transition, without losing technical rigor.

Inputs

Read mission.json first. Then inspect any existing academic material to be adapted.

Produce

Depending on the request, generate:

adapted training specification;
professionalized learning outcomes;
sequencing of modules or sessions;
workplace-oriented examples and exercises;
project ideas linked to employability;
adapted evaluation modes;
transition notes showing what changed from the academic version.

Required adaptation dimensions

Address explicitly:

entry heterogeneity;
prerequisite compression or remediation;
professional context and business value;
practical tooling exposure;

相关技能

快速安装

Professional Reskilling Designer

npx skills add alainlebret/claude-agents

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作者: alainlebret
星标: 0
更新时间: 2026年3月20日
职业

本页内容

portfolio-building opportunities;

assessment under adult-learning constraints.

Design rules

Prefer concrete tasks over abstract exposition when both teach the same concept.
Explain why each concept matters in practice.
Reduce unnecessary formalism, but keep the science correct.
Make progression visible and confidence-building.
Use authentic deliverables: scripts, reports, notebooks, mini-applications, incident analyses, deployment tasks, or demonstrations.
Distinguish what is essential for employability from what is advanced enrichment.

Hard constraints

Do not assume the learners have the mathematical or coding fluency of L3/M1 students unless stated.
Do not dilute correctness under the pretext of accessibility.
Flag modules that need a prerequisite bridge before delivery.
Require explicit workload realism for evenings, bootcamps, or continuing education.

02

Inputs

04Required adaptation dimensions

06Hard constraints

成人基础教育、中等教育及ESL教师

Continuous Learning V2

Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.

Continuous Learning V2

Hook'lar aracılığıyla oturumları gözlemleyen, güven skorlaması ile atomik instinct'ler oluşturan ve bunları skill/command/agent'lara evriltiren instinct tabanlı öğrenme sistemi. v2.1 çapraz proje kontaminasyonunu önlemek için proje kapsamlı instinct'ler ekler.

Continuous Learning V2

훅을 통해 세션을 관찰하고, 신뢰도 점수가 있는 원자적 본능을 생성하며, 이를 스킬/명령어/에이전트로 진화시키는 본능 기반 학습 시스템. v2.1에서는 프로젝트 간 오염을 방지하기 위한 프로젝트 범위 본능이 추가되었습니다.

Continuous Learning

Claude Codeセッションから再利用可能なパターンを自動的に抽出し、将来の使用のために学習済みスキルとして保存します。

Continuous Learning

Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.

Pytorch Patterns

PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.

成人基础教育、中等教育及ESL教师