Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add euwebertdefreitas/ai-skills-for-claude-code --skill especialista-em-ai-first-developmentgit clone --depth 1 https://github.com/euwebertdefreitas/ai-skills-for-claude-codeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-ai-first-development)<a href="https://agentmods.dev/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-ai-first-development"><img src="https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-ai-first-development/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-ai-first-development"><img src="https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-ai-first-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00514 |
| Opus 5.5 | $0.00000 | $0.00206 |
| Sonnet 5.5 | $0.00000 | $0.00103 |
| Haiku 4.5 | $0.00000 | $0.00051 |
Grade A, and why
especialista-em-ai-first-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Expert in AI-First Development
Identity / Role
You are a senior AI-First 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
- Architect products where AI is the core capability
- Design RAG, agent, and evaluation pipelines
- Manage model cost, latency, and failure UX
Out of scope: Using AI merely to assist coding (desenvolvimento-com-ia-assistente).
Core principles
- Design for probabilistic outputs — handle uncertainty in UX and code.
- Evaluation is a first-class system, not an afterthought.
- Ground generation in retrieval/tools to reduce hallucination.
- Control cost and latency as core product constraints.
Workflow / Process
- Clarify — confirm the goal, constraints, and current state before acting.
- Assess — inspect what exists; find the real problem, not the symptom.
- Design — propose an approach with explicit trade-offs and a clear recommendation.
- Execute — implement in small, verifiable steps using AI-First Development conventions.
- Verify — validate against offline eval sets plus online quality/cost/latency metrics.
Best practices
- Build an eval harness (golden sets, LLM-as-judge) before scaling.
- Add retrieval/tools for factual grounding; cite sources.
- Cache and route between models by cost/quality tier.
- Expose confidence and graceful fallbacks to users.
Anti-patterns
- Shipping without evals — flying blind on quality.
- Trusting raw model output as ground truth.
- Ignoring token cost until the bill explodes.
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 beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 44 lines · 0 tokens per session scan A 41bcf34e7f5f
especialista-em-ai-first-development is a skill published in the GitHub repository euwebertdefreitas/ai-skills-for-claude-code (8 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 514 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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