A workflow for building applications with AI and machine learning. It covers language-model apps, RAG—finding relevant stored information before generating an answer—AI agents, and ML pipelines.

It is for creating AI-powered features, retrieval-based applications, agent systems, machine-learning pipelines, and AI observability setups.

It gives developers a structured path from choosing an AI use case and model to building, observing, and measuring the system.

ai-ml — Skill for Claude Code by STELIORD

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add STELIORD/agentic-awesome-skills --skill ai-ml

Clone the repo

git clone --depth 1 https://github.com/STELIORD/agentic-awesome-skills

Made for: Claude Code.

Or install agentic-awesome-skills, the plugin that ships this one along with the rest of its 196 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/steliord/agentic-awesome-skills/ai-ml/github.svg)](https://agentmods.dev/skills/steliord/agentic-awesome-skills/ai-ml)

Your own site

<a href="https://agentmods.dev/skills/steliord/agentic-awesome-skills/ai-ml"><img src="https://agentmods.dev/badge/skills/steliord/agentic-awesome-skills/ai-ml/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/steliord/agentic-awesome-skills/ai-ml"><img src="https://agentmods.dev/badge/skills/steliord/agentic-awesome-skills/ai-ml.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,492 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 100% copy Near-identical to another mod in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00030	$0.01492
Opus 5	$0.00015	$0.00746
Sonnet 5	$0.00006	$0.00298
Haiku 4.5	$0.00003	$0.00149

Measured 7d ago against content hash 73cd3aaf5d79, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

ai-ml scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to ai-ml — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/agentic-awesome-skills-claude/skills/ai-ml/SKILL.md · 258 lines

How it starts

The opening of the file, as written. The whole thing — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AI/ML Workflow Bundle

Overview

Comprehensive AI/ML workflow for building LLM applications, implementing RAG systems, creating AI agents, and developing machine learning pipelines. This bundle orchestrates skills for production AI development.

When to Use This Workflow

Use this workflow when:

Building LLM-powered applications
Implementing RAG (Retrieval-Augmented Generation)
Creating AI agents
Developing ML pipelines
Adding AI features to applications
Setting up AI observability

Workflow Phases

Phase 1: AI Application Design

Skills to Invoke

ai-product - AI product development
ai-engineer - AI engineering
ai-agents-architect - Agent architecture
llm-app-patterns - LLM patterns

Actions

Define AI use cases
Choose appropriate models
Design system architecture
Plan data flows
Define success metrics

Copy-Paste Prompts

Use @ai-product to design AI-powered features

Use @ai-agents-architect to design multi-agent system

Phase 2: LLM Integration

Skills to Invoke

llm-application-dev-ai-assistant - AI assistant development
llm-application-dev-langchain-agent - LangChain agents
llm-application-dev-prompt-optimize - Prompt engineering
gemini-api-dev - Gemini API

Actions

Select LLM provider
Set up API access
Implement prompt templates
Configure model parameters
Add streaming support
Implement error handling

Copy-Paste Prompts

Use @llm-application-dev-ai-assistant to build conversational AI

Use @llm-application-dev-langchain-agent to create LangChain agents

Use @llm-application-dev-prompt-optimize to optimize prompts

Phase 3: RAG Implementation

Skills to Invoke

rag-engineer - RAG engineering
rag-implementation - RAG implementation
embedding-strategies - Embedding selection
vector-database-engineer - Vector databases
similarity-search-patterns - Similarity search
hybrid-search-implementation - Hybrid search

Read the full file on GitHub · 258 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

7d ago First seen · 258 lines · 30 tokens per session scan A 73cd3aaf5d79

Subscribe to this mod's changes

ai-ml is a skill published in the GitHub repository STELIORD/agentic-awesome-skills (1 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 1,492 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-ml, differing in 0 lines, and is treated as a copy.

Building agents Data and AI RAG and vectors workflow-bundle

ai-ml

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

This is a copy

How it starts

AI/ML Workflow Bundle

Overview

When to Use This Workflow

Workflow Phases

Phase 1: AI Application Design

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 2: LLM Integration

Skills to Invoke

Actions

Copy-Paste Prompts

Phase 3: RAG Implementation

Skills to Invoke

What this file has done since we first saw it

Other skills, from other repositories

ai-ml

ai-ml

ai-ml

ai-ml

ai-ml

ai-ml