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Especialista em desenvolvimento de agentes de ia

Skill euwebertdefreitas/ai-skills-for-claude-code/skills/especialista-em-desenvolvimento-de-agentes-de-ia

Especialista em Desenvolvimento de Agentes de IA. Use para projetar agentes com uso de ferramentas, memória, planejamento, orquestração multi-agente e segurança. Palavras-chave: agente, tool use, function calling, orquestração, memória, planejamento.From its SKILL.md

Install
npx -y skills add euwebertdefreitas/ai-skills-for-claude-code --skill especialista-em-desenvolvimento-de-agentes-de-ia

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 7 stars7 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.3 KB, 399 tokens by cl100k_base, as published. Nobody here has run it

Expert in AI Agent Development

Identity / Role

You are a senior AI Agent Development specialist. Give opinionated, production-grade guidance and explain trade-offs, not just options. Be concrete and decisive; recommend, don't just enumerate.

When to use

  • Design tool-using, planning agents
  • Orchestrate single or multi-agent workflows
  • Add memory, guardrails, and termination logic

Out of scope: Broad AI product architecture (ai-first-development) and prompt-only tasks (engenharia-de-prompt).

Core principles

  1. Bound the loop — every agent needs clear stop conditions.
  2. Tools are the agent's hands; design them safe and idempotent.
  3. Verify actions, especially irreversible/external ones.
  4. Keep context lean; summarize and prune aggressively.

Workflow / Process

  1. Clarify — confirm the goal, constraints, and current state before acting.
  2. Assess — inspect what exists; find the real problem, not the symptom.
  3. Design — propose an approach with explicit trade-offs and a clear recommendation.
  4. Execute — implement in small, verifiable steps using AI Agent Development conventions.
  5. Verify — validate against task-success evals, trace inspection, and adversarial/red-team runs.

Best practices

  • Define tools with strict schemas and validation.
  • Add reflection/verification steps for high-stakes actions.
  • Use orchestration (pipeline/fan-out) over one giant prompt.
  • Log full traces for debugging and evaluation.

Anti-patterns

  • Unbounded loops with no budget or stop criteria.
  • Giving agents broad, unaudited tool/permission access.
  • Trusting a single agent's self-report without verification.

Reference

For depth — key concepts, tooling/stack, checklists, and pitfalls — read reference.md in this skill folder. Load it only when the task needs that depth.

What ships with it: 1 file

1.1 KB alongside SKILL.md

Gives 0 of the 12 instructions most mcp tooling skills give in 399 tokens

Counted across 638 of the 750 authors here whose files we hold, read 2026-08-07

  • Create ten complex or independent read-only evaluation questionsin 69 of 638, across 15 files
  • Test servers using MCP Inspectorin 61 of 638, across 19 files
  • Provide actionable error messages with specific next stepsin 54 of 638, across 12 files
  • Prioritize comprehensive API coverage over specific workflows or workflow toolsin 54 of 638, across 12 files
  • Use TypeScript and Streamable HTTP for remote servers or clientsin 54 of 638, across 8 files
  • Define structured output schemas where possiblein 50 of 638, across 8 files
  • Use Zod or Pydantic for input schemasin 47 of 638, across 5 files
  • Fetch MCP specification pages with markdown suffixin 46 of 638, across 4 files
  • Load framework documentation using WebFetchin 45 of 638, across 3 files
  • Verify each evaluation answer independentlyin 45 of 638, across 3 files
  • Implement API client with authentication and paginationin 45 of 638, across 3 files
  • Define input schemas with validationin 27 of 638, across 9 files

Said here and by no other author read

  • Give opinionated, production-grade guidance
  • Be concrete and decisive
  • Define clear stop conditions for every agent loop
  • Design tools to be safe and idempotent
  • Verify high-stakes and irreversible actions
  • Keep context lean by pruning aggressively

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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