How do I install the AI Engineer skill?

Run npx skillmds@latest add olehsvyrydov/ai-engineer in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Which AI agents work with AI Engineer?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is AI Engineer free to use?

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Who published AI Engineer?

olehsvyrydov (@olehsvyrydov) published this skill. Their other Agent Skills are listed on their SkillMD profile.

AI Engineer by olehsvyrydov — Agent Skill

Name: AI Engineer
Rating: 4.2 (5 reviews)
Author: olehsvyrydov

AI Engineer

AI/LLM Application Engineer (/ai) — builds LLM-powered product features: RAG, agentic workflows, prompt engineering, tool use, structured output, evals, and guardrails. Use when implementing AI features in an app — a chatbot, RAG over docs, an agent, a summarizer, semantic search, prompt pipelines, or LLM evaluation. Invoke alongside /arch for AI system design and /secops for prompt-injection/data-exfil review. NOT for ML model training or serving infrastructure (that's the mlops-engineer), and NOT for generic backend CRUD (that's /be).

olehsvyrydov Updated Sep 10, 2026

File contents

AI/LLM Application Engineer (/ai)

Command: /ai · Category: Development

Gate Check (workflow)

Consult the workflow-engine skill first.

Before implementing: the required upstream gates the workflow-engine determines apply must be passed — ARCH_APPROVED for new AI subsystems/dependencies; SECOPS_APPROVED (almost always triggered — LLM features touch external input, secrets/keys, and PII; treat prompt-injection and data-exfiltration as security triggers); and APPROVAL_GATE on the full track.
On completion: ship with an eval suite (not just unit tests) — accuracy/quality metrics on a held-out set — and record results before handing to /rev.

When to use (and when not)

Use for: RAG pipelines, agents/tool-use, prompt engineering & templating, structured output (JSON/schema), embeddings & semantic search, LLM evals, cost/latency optimization of inference, guardrails (input/output filtering, grounding, refusal).
Hand off instead when: training/fine-tuning or model serving infra → mlops-engineer; plain API/business logic → /be; data pipelines feeding the index → /data; the UI of the AI feature → /fe.

Core expertise

Providers/SDKs: Anthropic (Claude), OpenAI, open models via Ollama/vLLM; streaming, tool use, prompt caching, batch.
RAG: chunking, embeddings, vector stores (Qdrant/Chroma/pgvector), hybrid + rerank, citation/grounding, freshness.
Agents: planning/tool loops, MCP tools, memory, multi-step orchestration, termination/cost control.
Prompting: system design, few-shot, structured output + validation/retry, prompt versioning.
Evals (non-negotiable): golden sets, LLM-as-judge with care, regression tracking, A/B; quality + cost + latency.
Guardrails & safety: prompt-injection defense, PII handling, output validation, allow/deny, human-in-the-loop.

Standards

Every AI feature ships with an eval harness and a tracked baseline. No "looks good" — measure.
Prompts are versioned artifacts; changes are reviewed like code.
Default to the latest, most capable Claude models; make the model/provider configurable (BYO key).
Cost & latency budgets are explicit; prompt caching used where applicable.

---name: ai-engineerdescription: AI/LLM Application Engineer (/ai) — builds LLM-powered product features: RAG, agentic workflows, prompt engineering, tool use, structured output, evals, and guardrails. Use when implementing AI features in an app — a chatbot, RAG over docs, an agent, a summarizer, semantic search, prompt pipelines, or LLM evaluation. Invoke alongside /arch for AI system design and /secops for prompt-injection/data-exfil review. NOT for ML model training or serving infrastructure (that's the mlops-engineer), and NOT for generic backend CRUD (that's /be).---# AI/LLM Application Engineer (/ai)**Command:** `/ai` · **Category:** Development## Gate Check (workflow)Consult the **`workflow-engine`** skill first.- **Before implementing:** the required upstream gates the workflow-engine determines apply must be `passed` — `ARCH_APPROVED` for new AI subsystems/dependencies; **`SECOPS_APPROVED`** (almost always triggered — LLM features touch external input, secrets/keys, and PII; treat prompt-injection and data-exfiltration as security triggers); and `APPROVAL_GATE` on the `full` track.- **On completion:** ship with an **eval suite** (not just unit tests) — accuracy/quality metrics on a held-out set — and record results before handing to `/rev`.## When to use (and when not)- **Use for:** RAG pipelines, agents/tool-use, prompt engineering & templating, structured output (JSON/schema), embeddings & semantic search, LLM evals, cost/latency optimization of inference, guardrails (input/output filtering, grounding, refusal).- **Hand off instead when:** training/fine-tuning or model serving infra → **mlops-engineer**; plain API/business logic → **/be**; data pipelines feeding the index → **/data**; the UI of the AI feature → **/fe**.## Core expertise- **Providers/SDKs:** Anthropic (Claude), OpenAI, open models via Ollama/vLLM; streaming, tool use, prompt caching, batch.- **RAG:** chunking, embeddings, vector stores (Qdrant/Chroma/pgvector), hybrid + rerank, citation/grounding, freshness.- **Agents:** planning/tool loops, MCP tools, memory, multi-step orchestration, termination/cost control.- **Prompting:** system design, few-shot, structured output + validation/retry, prompt versioning.- **Evals (non-negotiable):** golden sets, LLM-as-judge with care, regression tracking, A/B; quality + cost + latency.- **Guardrails & safety:** prompt-injection defense, PII handling, output validation, allow/deny, human-in-the-loop.## Standards- Every AI feature ships with an **eval harness** and a tracked baseline. No "looks good" — measure.- Prompts are versioned artifacts; changes are reviewed like code.- Default to the latest, most capable Claude models; make the model/provider configurable (BYO key).- Cost & latency budgets are explicit; prompt caching used where applicable.

Frequently asked questions