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mcp/skillhub

Agent Skill

AI ML skill

ai-mlsickn33/agentic-awesome-skillsplugins/agentic-awesome-skills-claude/skills/ai-ml/SKILL.md

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

48kMIT

always on

30 tokname + description

once used

1,514 tokSKILL.md body

bundled files

0

repo stars

48kGitHub

Install the AI ML skill

Skills CLI (Claude Code, Codex, Cursor and others)

npx skills add https://github.com/sickn33/agentic-awesome-skills --skill ai-ml

Claude Code, by hand (all projects)

git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git /tmp/agentic-awesome-skills
mkdir -p ~/.claude/skills
cp -R /tmp/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/ai-ml ~/.claude/skills/ai-ml

For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.

What it costs your context

Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.

WhenWhat loadsCost
Every sessionName and description30 tok
When the agent uses itThe SKILL.md instructions1,514 tok
Only if openedNo bundled files—

Until it is used, ai-ml costs about 45× less than the median MCP server we measured (1,362 tokens on every turn). Installing all 150 skills from sickn33/agentic-awesome-skills would load 5,255 tokens of descriptions. Skills vs MCP study

Checks

Our checks found nothing to flag: it follows the Agent Skills specification's naming and size guidelines and doesn't pipe downloads into a shell.

Checked 2026-10-22 · method

What's inside

The headings of its SKILL.md. Read the full file on GitHub.

  • AI/ML Workflow Bundle
  • Overview
  • When to Use This Workflow
  • Selection and completion
  • Workflow Phases
  • Phase 1: AI Application Design
  • Phase 2: LLM Integration
  • Phase 3: RAG Implementation
  • Phase 4: AI Agent Development
  • Phase 5: ML Pipeline Development
  • Phase 6: AI Observability
  • Phase 7: AI Security
  • AI Development Checklist
  • LLM Integration
  • RAG System
  • AI Agents
  • Observability
  • Quality Gates
  • Related Workflow Bundles
  • Limitations

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