Awesome-omni-skills ai-ml-v2

AI/ML Workflow Bundle workflow skill. Use this skill when the user needs AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

install

source · Clone the upstream repo

git clone https://github.com/diegosouzapw/awesome-omni-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/ai-ml-v2" ~/.claude/skills/diegosouzapw-awesome-omni-skills-ai-ml-v2 && rm -rf "$T"

manifest: skills/ai-ml-v2/SKILL.md

AI/ML Workflow Bundle

Overview

This public intake copy packages

plugins/antigravity-awesome-skills/skills/ai-ml

from

https://github.com/sickn33/antigravity-awesome-skills

into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

plus

ORIGIN.md

as the provenance anchor for review.

AI/ML Workflow Bundle

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: AI Development Checklist, Quality Gates, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

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

Operating Table

Situation	Start here	Why it matters
First-time use	`metadata.json`	Confirms repository, branch, commit, and imported path before touching the copied workflow
Provenance review	`ORIGIN.md`	Gives reviewers a plain-language audit trail for the imported source
Workflow execution	`SKILL.md`	Starts with the smallest copied file that materially changes execution
Supporting context	`SKILL.md`	Adds the next most relevant copied source file without loading the entire package
Handoff decision	`## Related Skills`	Helps the operator switch to a stronger native skill when the task drifts

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

ai-product - AI product development
ai-engineer - AI engineering
ai-agents-architect - Agent architecture
llm-app-patterns - LLM patterns
Define AI use cases
Choose appropriate models
Design system architecture

Imported Workflow Notes

Imported: 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

Actions

Design data pipeline
Choose embedding model
Set up vector database
Implement chunking strategy
Configure retrieval
Add reranking
Implement caching

Copy-Paste Prompts

Use @rag-engineer to design RAG pipeline

Use @vector-database-engineer to set up vector search

Use @embedding-strategies to select optimal embeddings

Phase 4: AI Agent Development

Skills to Invoke

```
autonomous-agents
```
- Autonomous agent patterns
```
autonomous-agent-patterns
```
- Agent patterns
```
crewai
```
- CrewAI framework
```
langgraph
```
- LangGraph
```
multi-agent-patterns
```
- Multi-agent systems
```
computer-use-agents
```
- Computer use agents

Actions

Design agent architecture
Define agent roles
Implement tool integration
Set up memory systems
Configure orchestration
Add human-in-the-loop

Copy-Paste Prompts

Use @crewai to build role-based multi-agent system

Use @langgraph to create stateful AI workflows

Use @autonomous-agents to design autonomous agent

Phase 5: ML Pipeline Development

Skills to Invoke

```
ml-engineer
```
- ML engineering
```
mlops-engineer
```
- MLOps
```
machine-learning-ops-ml-pipeline
```
- ML pipelines
```
ml-pipeline-workflow
```
- ML workflows
```
data-engineer
```
- Data engineering

Actions

Design ML pipeline
Set up data processing
Implement model training
Configure evaluation
Set up model registry
Deploy models

Copy-Paste Prompts

Use @ml-engineer to build machine learning pipeline

Use @mlops-engineer to set up MLOps infrastructure

Phase 6: AI Observability

Skills to Invoke

```
langfuse
```
- Langfuse observability
```
manifest
```
- Manifest telemetry
```
evaluation
```
- AI evaluation
```
llm-evaluation
```
- LLM evaluation

Actions

Set up tracing
Configure logging
Implement evaluation
Monitor performance
Track costs
Set up alerts

Copy-Paste Prompts

Use @langfuse to set up LLM observability

Use @evaluation to create evaluation framework

Phase 7: AI Security

Skills to Invoke

```
prompt-engineering
```
- Prompt security
```
security-scanning-security-sast
```
- Security scanning

Actions

Implement input validation
Add output filtering
Configure rate limiting
Set up access controls
Monitor for abuse
Implement audit logging

Imported: Related Workflow Bundles

```
development
```
- Application development
```
database
```
- Data management
```
cloud-devops
```
- Infrastructure
```
testing-qa
```
- AI testing

Imported: 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.

Imported: AI Development Checklist

LLM Integration

RAG System

AI Agents

Observability

Examples

Example 1: Ask for the upstream workflow directly

Use @ai-ml-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @ai-ml-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @ai-ml-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @ai-ml-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
Treat generated examples as scaffolding; adapt them to the concrete task before execution.
Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills/skills/ai-ml

, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open

metadata.json

ORIGIN.md

, and the most relevant copied upstream files. Load only the files that materially change the answer, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

```
@advogado-especialista-v2
```
- Use when the work is better handled by that native specialization after this imported skill establishes context.
```
@aegisops-ai-v2
```
- Use when the work is better handled by that native specialization after this imported skill establishes context.
```
@agent-evaluation-v2
```
- Use when the work is better handled by that native specialization after this imported skill establishes context.
```
@agent-framework-azure-ai-py-v2
```
- Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Resource family	What it gives the reviewer	Example path
`references`	copied reference notes, guides, or background material from upstream	`references/n/a`
`examples`	worked examples or reusable prompts copied from upstream	`examples/n/a`
`scripts`	upstream helper scripts that change execution or validation	`scripts/n/a`
`agents`	routing or delegation notes that are genuinely part of the imported package	`agents/n/a`
`assets`	supporting assets or schemas copied from the source package	`assets/n/a`

Imported Reference Notes

Imported: Quality Gates

Imported: Limitations

Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.