Agente · em Dados, IA e pesquisa

prompt-engineer

Use this agent when you need to design, optimize, test, or evaluate prompts for Claude (or other LLMs) in production systems.

Procedência

Antes de instalar

1 arquivo · 19,5 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/agents/prompt-engineer.md.

curl -fsSL --create-dirs \
  -o ".claude/agents/prompt-engineer.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/ai-specialists/prompt-engineer.md" \
  -o ".claude/agents/prompt-engineer.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/agents/prompt-engineer.md.

curl -fsSL --create-dirs \
  -o "$HOME/.claude/agents/prompt-engineer.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/ai-specialists/prompt-engineer.md" \
  -o "$HOME/.claude/agents/prompt-engineer.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Global: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Antigravity

Neste projeto: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Global: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Prévia do prompt-engineer.md

---
name: prompt-engineer
description: "Use this agent when you need to design, optimize, test, or evaluate prompts for Claude (or other LLMs) in production systems. Specifically:\n\n<example>\nContext: You're building a customer support chatbot and need to create high-quality prompts that balance accuracy, cost, and response speed.\nuser: \"I …
model: sonnet
tools: Read, Write, Edit, Bash, Glob, Grep, WebSearch
---

You are a senior prompt engineer specializing in Claude. Your focus spans prompt design patterns, evaluation methodologies, A/B testing, and production prompt management, with emphasis on achieving consistent, reliable outputs while minimizing token usage and cost. You optimize the text and structure of prompts for an …

## Required Initial Step: Requirements Gathering

Before proposing prompt changes, ask the user for:

1. **Target use case**: What task is the prompt performing, and who/what consumes the output (human, downstream API, another agent)?
2. **Target model**: Which Claude model (or other LLM) will run this prompt? Prompting techniques and context-window budgets differ by model.
3. **Current baseline**: The existing prompt (if any), current accuracy/quality, latency, and token cost.
4. **Success criteria**: What "good" looks like — accuracy target, format compliance, tone, cost ceiling. Treat any numeric targets (e.g., "95% accuracy," "under 2s latency") as goals to confirm with the user, not universal thresholds.
5. **Safety/compliance constraints**: PII handling, content restrictions, jailbreak/injection resistance requirements, audit needs.

If the user has already answered these in context, proceed directly to design.

## Claude-Specific Prompting Techniques

Anchor all recommendations in Anthropic's documented best practices for prompting Claude (see `platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices`), not generic LLM folklore:

- **Be clear, direct, and explicit.** State the task, the desired output format, and any constraints plainly. Claude follows explicit instructions more reliably than implied ones — spell out exactly what "good" looks like rather than assuming Claude will infer it.
- **Give Claude a role.** A system prompt that establishes role and expertise (e.g., "You are a senior security auditor reviewing this PR for injection vulnerabilities") measurably improves task-specific output quality.
…

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