senior-prompt-engineer
Use when the user asks to optimize prompts, design prompt templates, evaluate LLM outputs with an eval set, measure RAG retrieval quality, validate agent/tool configurations, analyze token usage, or design structured-output contracts. Covers eval-driven prompt iteration, RAG metrics (relevance, faithfulness, coverage), agent workflow validation, and token/cost budgeting — all model-agnostic, with three stdlib Python tools.
Profile →npx skills add alirezarezvani/claude-skills --skill senior-prompt-engineer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 —
- Operating Rules
- Tools (exact CLIs, all stdlib)
- 1. Prompt Optimizer — scripts/promptoptimizer.py
- 2. RAG Evaluator — scripts/ragevaluator.py
- 3. Agent Orchestrator — scripts/agentorchestrator.py
- Workflows
- Prompt Optimization (eval-gated)
- Few-Shot Example Design
- Structured Output Design
- RAG Tuning Loop
- Agent Config Review
- References
- Related Skills
Full analysis (human-readable report) python3 scripts/prompt_optimizer.py prompt.txt --analyze Save machine-readable baseline for later comparison python3 scripts/prompt_optimizer.py prompt.txt --analyze --json --output baseline.json Token estimate; cost only if you supply your provider's current rate python3 scripts/prompt_optimizer.py prompt.txt --tokens --model claude --price-per-mtok 3.00 Whitespace/redundancy-trimmed version python3 scripts/prompt_optimizer.py prompt.txt --optimize --output optimized.txt Extract Input/Output few-shot pairs to JSON python3 scripts/prompt_optimizer.py prompt.txt --extract-examples --output examples.json
What does the senior-prompt-engineer skill do?
Use when the user asks to optimize prompts, design prompt templates, evaluate LLM outputs with an eval set, measure RAG retrieval quality, validate agent/tool configurations, analyze token usage, or design structured-output contracts. Covers eval-driven prompt iteration, RAG metrics (relevance, faithfulness, coverage), agent workflow validation, and token/cost budgeting — all model-agnostic, with three stdlib Python tools.
How do I install it?
Run `npx skills add alirezarezvani/claude-skills --skill senior-prompt-engineer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From alirezarezvani/claude-skills, a repository with 23,791 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.