Individual skill

senior-prompt-engineer

Use when optimizing a prompt, building an eval set, measuring RAG retrieval quality or validating an agent's tool config. Iterates with prompt_optimizer.py, scores RAG with rag_evaluator.py and checks agents with agent_orchestrator.py.

engineering-team/skills/senior-prompt-engineer/SKILL.md

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# Senior Prompt Engineer

Eval-driven prompt engineering, RAG quality measurement, and agent workflow validation. Everything here is **model-agnostic by design**: techniques are framed by what they do, not by which model generation they were observed on, and the tools never hardcode model IDs or pricing — you supply your provider's current rates when you want dollar figures.

## Operating Rules

1. **Never change a prompt without a baseline.** Capture metrics first (`--analyze --output baseline.json`), then compare every iteration against it.
2. **Eval set before optimization.** 10–20 representative cases with expected outputs minimum. If the user has no eval set, build one with them before touching the prompt — optimizing against vibes is the #1 failure mode.
3. **Prefer platform features over prompt hacks.** If the provider offers native structured outputs / JSON schema enforcement, tool-use APIs, or prompt caching, use those instead of "respond ONLY with JSON" incantations. Prompt-level format enforcement is the fallback, not the default.
4. **Current-generation models need less scaffolding.** Don't add chain-of-thought boilerplate, role framing, or few-shot examples reflexively — frontier models often do worse with redundant scaffolding. Add each element only when the eval set shows it helps.
5. **Cost numbers are always user-supplied.** Look up the provider's current per-Mtok pricing and pass it via `--price-per-mtok` (never trust a cached price table — including any you remember).

## Tools (exact CLIs, all stdlib)
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