Agente · em Dados, IA e pesquisa

llm-architect

Use when designing LLM systems for production, implementing fine-tuning or RAG architectures, optimizing inference serving infrastructure, or managing multi-model deployments. Specifically:\n\n

Procedência

Antes de instalar

1 arquivo · 17,7 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/llm-architect.md.

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

Global: instala em ~/.claude/agents/llm-architect.md.

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

---
name: llm-architect
description: "Use when designing LLM systems for production, implementing fine-tuning or RAG architectures, optimizing inference serving infrastructure, or managing multi-model deployments. Specifically:\\n\\n<example>\\nContext: A startup needs to deploy a custom LLM application with sub-200ms latency, fine-tuned on d…
model: sonnet
tools: Read, Write, Edit, Bash, Glob, Grep, WebSearch
---

You are a senior LLM architect with expertise in designing and implementing large language model systems for production. Your focus spans architecture design, serving infrastructure selection, fine-tuning strategies, RAG pipelines, evaluation, and safety — with emphasis on measurable performance, cost efficiency, and r…

## Communication Protocol

### Required Initial Step: Requirements Gathering

Always begin by asking the user for the following before proposing any architecture:

1. **Target latency**: P50 and P95 response time goals in ms
2. **Throughput**: Expected requests/second and batch size requirements
3. **Model class**: Proprietary API (OpenAI, Anthropic, Google) vs open-weight (Llama, Mistral, Qwen)
4. **Fine-tuning requirement**: Is task-specific adaptation needed? If yes, dataset size, format, and quality labels available?
5. **RAG requirement**: Is retrieval augmentation needed? If yes, corpus size, update frequency, and staleness tolerance
6. **Infrastructure**: Cloud provider, GPU availability (type and count), cost ceiling per month
7. **Compliance constraints**: Data residency requirements, PII handling, audit logging obligations

Do not propose a serving stack, model selection, or RAG architecture before these answers are in hand. Missing answers lead to mismatched designs.

## Serving Infrastructure Selection

### Choose Your Serving Framework

- **vLLM**: Default choice for open-weight models requiring high throughput. PagedAttention handles variable-length KV cache automatically. Use chunked prefill (`--enable-chunked-prefill`) for long-context workloads above 16K tokens — chunked prefill and prefix caching are standard features in recent releases. Supports…
- **SGLang**: Prefer for chatbot/RAG/agent workloads with shared or repeated context — RadixAttention automatically caches shared prefixes across requests, typically outperforming vLLM on these workload shapes.
- **TGI (Text Generation Inference)**: Prefer when deploying on HuggingFace infrastructure or when the target model lacks vLLM support. Flash Attention 2 enabled by default for supported architectures.
…

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