AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
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.
npx skills add sickn33/agentic-awesome-skills --skill ai-mlgit clone --depth 1 https://github.com/sickn33/agentic-awesome-skillsWrote 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.
[](https://agentmods.dev/skills/sickn33/agentic-awesome-skills/ai-ml)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/ai-ml"><img src="https://agentmods.dev/badge/skills/sickn33/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.
<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/ai-ml"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/ai-ml.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.01576 |
| Opus 5.5 | $0.00012 | $0.00630 |
| Sonnet 5.5 | $0.00006 | $0.00315 |
| Haiku 4.5 | $0.00003 | $0.00158 |
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 12d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- ai-ml — 100% identical, 4 lines differ
- ai-ml — 100% identical, 4 lines differ
- ai-ml — 100% identical, 4 lines differ
- ai-ml — 100% identical, 0 lines differ
- ai-ml — 100% identical, 4 lines differ
- ai-ml — 100% identical, 4 lines differ
- ai-ml — 100% identical, 4 lines differ
- ai-ml — 100% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 262 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
Selection and completion
Select only phases needed for the requested product; this workflow does not authorize delegation, hosted calls, deployment, background jobs or external messages. Read the actual selected skill and available tool schema. Define task-level quality, retrieval/source checks, data permissions, latency/currency limits and failure recovery before implementation. Verify each used capability in the target environment; unchecked checklist items are not completed because an instruction exists.
Workflow Phases
Phase 1: AI Application Design
Skills to Invoke
ai-product- AI product developmentai-engineer- AI engineeringai-agents-architect- Agent architecturellm-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 developmentllm-application-dev-langchain-agent- LangChain agentsllm-application-dev-prompt-optimize- Prompt engineeringgemini-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
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.
- 12d ago Changed · +4 lines fbc3584f7e6a
- 1mo ago First seen · 258 lines · 30 tokens per session scan A 73cd3aaf5d79
ai-ml is a skill published in the GitHub repository sickn33/agentic-awesome-skills (48,548 stars, last pushed 3d ago), licensed MIT. It adds 30 tokens to every session and 1,576 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
Other skills, from other repositories
ai-ml
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
rag-builder
Professional Rag Builder skill. Integrate LLM API workflows, safe system prompt guidelines, and agentic workflows.
semantic-search
Professional Semantic Search Expert skill. Build performant, secure, and scalable backend logic and RESTful or GraphQL APIs.
vector-database
Professional Vector Database Expert skill. Design and maintain secure, optimized, and performant databases and data storage solutions.
rag-retrieval-audit
Diagnose missing evidence in a RAG pipeline with labeled queries, chunk/trace inspection, Recall@K/MRR/nDCG, and stage-isolated failure taxonomy. Use when retrieval looks fine but answers fail, or when index/chunk/filter/rerank changes need before/after proof.
ai-ml
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.