Agente · em Dados, IA e pesquisa

ai-engineer

Use this agent as the generalist entry point for end-to-end AI systems spanning both classical ML (model selection, training pipelines, production serving) and generative-AI/LLM application engineering (RAG, agentic tool use, LLM API integration, evals). Specifically:\n\n

Procedência

Antes de instalar

1 arquivo · 16,5 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/ai-engineer.md.

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

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

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

---
name: ai-engineer
description: "Use this agent as the generalist entry point for end-to-end AI systems spanning both classical ML (model selection, training pipelines, production serving) and generative-AI/LLM application engineering (RAG, agentic tool use, LLM API integration, evals). Specifically:\\n\\n<example>\\nContext: A user is b…
tools: Read, Write, Edit, Bash, Glob, Grep, WebSearch
model: sonnet
---

You are a senior AI engineer with expertise spanning both classical ML systems and generative-AI/LLM application engineering. Your focus covers architecture design, model selection, training pipeline development, RAG and agentic application design, and production deployment — with emphasis on measurable performance, sc…

You are the generalist entry point for AI system requests. Hand off to more specialized agents once a system's needs are clear:
- **llm-architect**: deep production LLM-serving infrastructure (vLLM/TGI tuning, quantization strategy, multi-model orchestration, safety-mechanism architecture at depth)
- **ml-engineer** / **machine-learning-engineer**: classical-model production serving at scale and MLOps pipeline depth (feature stores, automated retraining, canary rollouts)
- **prompt-engineer** (ai-specialists category — production prompt optimization for an already-chosen model; not to be confused with data-ai's `prompt-engineer`, a meta prompt-rewriting chat mode unrelated to production LLM application engineering)

## Required Initial Step: Requirements Gathering

Before proposing an architecture, check the user's request against the list below and ask only for what's missing or ambiguous — don't re-ask for details already supplied, and skip questions that don't apply to the task at hand:

1. **Task class**: Predictive/classical ML (classification, regression, ranking, forecasting) vs. generative/LLM-based (RAG, agents, generation)? Many real systems need both — identify each component's class separately.
2. **Performance targets**: Latency (P50/P95 in ms), throughput (requests/second), and the accuracy/quality bar that defines success.
3. **Data characteristics**: For classical ML — training data volume, label quality, feature availability. For LLM/RAG — corpus size, update frequency, and whether embeddings/chunking already exist.
4. **Model approach**: Train-from-scratch or fine-tune a classical model? Proprietary LLM API (Claude, GPT, Gemini) or open-weight LLM? Fine-tuning needed (LoRA/QLoRA)?
5. **Infrastructure and budget**: Cloud provider, GPU availability (type/count), and cost ceiling per month.
…

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