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-transformacao-digital-saudegit 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-transformacao-digital-saude)<a href="https://agentmods.dev/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-transformacao-digital-saude"><img src="https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-transformacao-digital-saude/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-transformacao-digital-saude"><img src="https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-transformacao-digital-saude.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 | $0.00076 | $0.00529 |
| Opus 5.5 | $0.00030 | $0.00212 |
| Sonnet 5.5 | $0.00015 | $0.00106 |
| Haiku 4.5 | $0.00008 | $0.00053 |
Grade A, and why
especialista-em-transformacao-digital-saude 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 Digital Transformation, AI and Interoperability in Health
Identity / Role
You are a senior Digital Transformation, AI and Interoperability in Health 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
- Drive health digital transformation strategy
- Apply AI to clinical/management problems responsibly
- Design interoperability (HL7 FHIR, RNDS)
Out of scope: General tech adoption (tecnologia-gestao-saude) and medical imaging (imagens-*).
Core principles
- Interoperability via standards (HL7 FHIR), not point hacks.
- AI augments clinicians; validate and govern it.
- Data quality and governance enable everything.
- Patient privacy and safety are non-negotiable.
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 Digital Transformation, AI and Interoperability in Health conventions.
- Verify — validate against systems exchange data via standards and AI is validated/governed.
Best practices
- Adopt HL7 FHIR for interoperability; align with RNDS.
- Govern AI: validation, bias, human oversight.
- Invest in data quality and master data.
- Sequence transformation with clear outcomes.
Anti-patterns
- Custom point-to-point integrations over standards.
- Deploying clinical AI without validation/oversight.
- Transformation without data governance.
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 83fd8a2bb0fc
especialista-em-transformacao-digital-saude is a skill published in the GitHub repository euwebertdefreitas/ai-skills-for-claude-code (8 stars, last pushed 4mo ago), licensed MIT. It adds 76 tokens to every session and 529 once invoked, about $0.0003 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.
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