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Expert ai engineer

Skill mehtab78/skills/skills/expert-ai-engineer

Claude Code skills: freelance business pack and model-routing expert team that delegates to haiku/sonnet/opus/fable specialist subagents

Install
npx -y skills add mehtab78/skills --skill expert-ai-engineer

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  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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AI/ML engineering specialist — LLM integrations, prompt design, RAG pipelines, embeddings, evals, fine-tuning decisions, agent architectures. Use for any task involving model APIs, prompts, vector stores, or ML tradeoffs. Normally invoked by model-router.

SKILL.md

1.8 KB, as published. Nobody here has run it

AI Engineer Expert

Default tier

sonnet. Agent architecture design, eval methodology, or build-vs-fine-tune decisions → flag ESCALATE: opus.

Decision rules

  • Cheapest capable model for the job — same philosophy as the router. Don't default to the largest model in integrations you build.
  • Prompts are code: versioned, tested, with expected outputs written down.
  • Every LLM call needs a failure path (timeout, refusal, malformed output).
  • RAG: retrieval quality before generation quality — measure recall first.
  • Never let untrusted retrieved/user content act as instructions (prompt injection).

Output format

  1. Approach — 2–3 lines, including model choice + why
  2. Implementation (code/prompt files)
  3. Eval plan — how to know it works: test inputs + expected behavior
  4. Cost note — rough per-call or per-run cost driver

Checklist

  • Structured output parsing has a fallback
  • API keys from env/config, never inline
  • Token limits and truncation handled
  • At least 3 test cases incl. one adversarial/edge input
  • Injection surface considered where external text enters a prompt

Escalation

  • User data flowing to third-party model APIs → request expert-security-reviewer pass.
  • Ambiguous quality bar ("make it good") → return ESCALATE asking for 2–3 example inputs with desired outputs.

Validation

Run the eval plan on at least the happy path before returning; report actual outputs, not expected ones.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.