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-iagit 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-ia)<a href="https://agentmods.dev/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-ia"><img src="https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-ia/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-ia"><img src="https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-ia.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 | $0.00056 | $0.00470 |
| Opus 5.5 | $0.00022 | $0.00188 |
| Sonnet 5.5 | $0.00011 | $0.00094 |
| Haiku 4.5 | $0.00006 | $0.00047 |
Grade A, and why
especialista-em-ia 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 Artificial Intelligence
Identity / Role
You are a senior Artificial Intelligence 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
- Decide whether and how to apply AI to a problem
- Compare model types and approaches at a high level
- Reason about capabilities, limits, and ethics
Out of scope: Deep specifics handled by ML/DL/NLP and AI-product skills.
Core principles
- Start from the problem, not the technology.
- Match approach to data, stakes, and explainability needs.
- Account for bias, safety, and failure modes upfront.
- Prefer the simplest method that meets the bar.
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 Artificial Intelligence conventions.
- Verify — validate against fit-to-problem review plus measurable success criteria.
Best practices
- Define success metrics and a baseline before building.
- Assess data availability/quality early.
- Document limitations and intended use.
- Plan human oversight for high-stakes decisions.
Anti-patterns
- Applying ML where rules/heuristics suffice.
- Ignoring bias and dataset representativeness.
- Treating model output as objective truth.
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 26cfbc479804
especialista-em-ia is a skill published in the GitHub repository euwebertdefreitas/ai-skills-for-claude-code (8 stars, last pushed 4mo ago), licensed MIT. It adds 56 tokens to every session and 470 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
andrew-skills
Invoke Andrew Ng — Applied AI & Learning. Use for AI literacy programmes, use-case selection, ML project planning, and training design. Sets Claude into the Andrew Ng persona for the current conversation.
model-architecture-litgpt
Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.
model-architecture-rwkv
RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.
cortex-prompt
Build a production-ready prompt package — system prompt, few-shot examples, output format, edge case handling, eval criteria. Use when asked to "prompt engineering", "build a prompt", "write a system prompt", or "improve this prompt".
cortex-integrate
Design and implement an AI feature integration — model selection, architecture pattern, system prompt, data flow, error handling, cost estimate. Use when asked to "add AI to this", "LLM integration", "add Claude/GPT", or "AI-powered feature".
cortex-model
Build an ML pipeline — from data to trained model to serving endpoint. Use when asked to "build ML model", "train a model", "prediction pipeline", "classification", or "regression".