AI 도구와 기술 마스터하기
161 ai 스킬
skills.sh 생태계의 재사용 가능한 Agent Skill입니다. azure-cost-optimization from microsoft/azure-skills: Identify cost savings across Azure subscriptions through resource analysis, utilization metrics, and actionable optimization recommendations.
skills.sh 생태계의 재사용 가능한 Agent Skill입니다. azure-upgrade from microsoft/azure-skills: Assess and automate upgrades of Azure workloads across plans, tiers, and SKUs.
skills.sh 생태계의 재사용 가능한 Agent Skill입니다. azure-quotas from microsoft/azure-skills: Check and manage Azure quotas and usage across regions for deployment planning and capacity validation.
skills.sh 생태계의 재사용 가능한 Agent Skill입니다. skill-creator from anthropics/skills: Create, test, and iteratively improve AI agent skills with structured evaluation and benchmarking.
skills.sh 생태계의 재사용 가능한 Agent Skill입니다. azure-cloud-migrate from microsoft/azure-skills: Assess and migrate cloud workloads from AWS, GCP, and other providers to Azure services.
skills.sh 생태계의 재사용 가능한 Agent Skill입니다. azure-compute from microsoft/azure-skills: Recommend Azure VM sizes, scale sets, and configurations based on workload requirements and budget.
skills.sh 생태계의 재사용 가능한 Agent Skill입니다. azure-hosted-copilot-sdk from microsoft/azure-skills: Build and deploy GitHub Copilot SDK applications to Azure with flexible model configuration.
skills.sh 생태계의 재사용 가능한 Agent Skill입니다. agent-browser from vercel-labs/agent-browser: Fast, persistent browser automation with session continuity across sequential agent commands.
skills.sh 생태계의 재사용 가능한 Agent Skill입니다. azure-compliance from microsoft/azure-skills: Azure compliance scanning, Key Vault expiration auditing, and resource configuration validation.
skills.sh 생태계의 재사용 가능한 Agent Skill입니다. azure-diagnostics from microsoft/azure-skills: Systematic diagnosis and remediation for Azure production issues using AppLens, Monitor, and resource health.
skills.sh 생태계의 재사용 가능한 Agent Skill입니다. azure-deploy from microsoft/azure-skills: Execute Azure deployments for prepared applications with built-in error recovery and validation.
skills.sh 생태계의 재사용 가능한 Agent Skill입니다. remotion-best-practices from remotion-dev/skills: Domain-specific knowledge base for building videos with Remotion and React.
Skill engineering
A skill is not just a long prompt. It is a packaged procedure: activation metadata, task instructions, optional scripts, reference files, examples, and validation steps. Good skill pages should explain what the skill does, when it activates, what resources it loads, and how users can verify the result.
What changed
Packaged procedure
Claude describes skills as directories with instructions, scripts, and resources, with a SKILL.md file defining activation and behavior. That makes skills suitable for team workflows such as SEO audits, spreadsheet analysis, slide generation, brand writing, and code review.
Progressive disclosure
Claude’s documentation describes a three-step pattern: lightweight metadata at startup, full instructions only when the task matches, and extra resources only when needed. This directly addresses context-window overload.
Runtime integration
OpenAI’s Agents SDK includes a skills capability that mounts skills into an auto-discovery root inside a sandbox and supports lazy loading. In practice, that turns skills into portable operating procedures an agent can discover during work.
Popular starting points
Explore AI skills by learning goal: find an AI skills course, learn AI skills for free, compare structured programs, understand AI skills in demand, and map skills to AI engineer requirements.
A practical starting point for prompt engineering, AI tools, agent workflows, data skills, evaluation, and applied workplace AI literacy.
Best for users who need a staged learning path from beginner foundations to practical projects and advanced agent workflows.
Useful for career-focused readers comparing workplace AI skills, AI engineer skills required, and portfolio projects that prove ability.
Explore AI skills learning paths
The page should combine learning, certification, role readiness, and agent-skill workflows instead of treating skills as generic tutorials.
These queries need a staged path: foundations, prompt skills, AI tools, agent workflows, data handling, evaluation, and portfolio projects.
These searches point to structured programs. The page can cover how to compare AI skills passport, Microsoft AI Skills Fest, and similar learning programs without overclaiming affiliation.
These users care about employability. Add sections about AI engineer skills required, workplace AI literacy, and evidence of skill through projects.
Skills vs prompts vs agents
These three layers should work together. A prompt instructs one task, a skill packages repeatable expertise, and an agent executes across tools and time.
What makes a skill worth learning
Related AI skills topics
Use these topics to move from basic AI skills into courses, free learning paths, career skills, engineering requirements, and agent skill workflows.
Skill FAQ
Explain the task it handles, the trigger conditions, the workflow, required tools or files, expected output, quality checks, and common failure cases. That is more useful than listing broad benefits.
Projects and custom instructions are broad background context. Skills are task-specific procedures that load dynamically, which makes them better for specialized work that should not always be in the context window.
High-value skills encode work that happens often and has a review standard: SEO content audits, research briefs, spreadsheet modeling, slide creation, code review, data cleaning, customer support QA, and brand-compliant writing.