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 goul4rt/lgpd-skills --skill lgpd-data-mappinggit clone --depth 1 https://github.com/goul4rt/lgpd-skillsWrote 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/goul4rt/lgpd-skills/lgpd-data-mapping)<a href="https://agentmods.dev/skills/goul4rt/lgpd-skills/lgpd-data-mapping"><img src="https://agentmods.dev/badge/skills/goul4rt/lgpd-skills/lgpd-data-mapping/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/goul4rt/lgpd-skills/lgpd-data-mapping"><img src="https://agentmods.dev/badge/skills/goul4rt/lgpd-skills/lgpd-data-mapping.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 | $0.00107 | $0.01332 |
| Opus 5 | $0.00053 | $0.00666 |
| Sonnet 5 | $0.00021 | $0.00266 |
| Haiku 4.5 | $0.00011 | $0.00133 |
Grade A, and why
lgpd-data-mapping 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Mapping LGPD
Inventário canônico de todas as atividades de tratamento de dados pessoais no projeto. É o pré-requisito para ROPA (Art. 37), RIPD (Art. 38), retenção e DSAR.
Workflow
Para greenfield
Liste atividades por feature/módulo do produto antes de codar. Cada feature que toca dado pessoal = uma atividade.
Para legacy (reverse-engineering)
- Schemas de banco: leia
prisma/schema.prisma(ou equivalente). Toda tabela com referência a usuário = candidato. - APIs: leia rotas REST/tRPC/GraphQL. Endpoints recebendo PII = atividade.
- SDKs de terceiros: grep por nomes (Sentry, Datadog, Mixpanel, Amplitude, Segment, Stripe, Discord SDK, Firebase, OneSignal). Cada um é compartilhamento.
- Logs: cuidado — logs podem vazar PII inadvertidamente (audit também).
- Cookies e localStorage em frontends.
- Coletas mobile: device ID, geolocation, push tokens, biometria, contatos, fotos.
Por atividade, capture:
- Nome curto (slug)
- Descrição (1 linha)
- Finalidade específica (Art. 6º, I)
- Base legal (link para
.lgpd/legal-basis.md) - Categorias de titulares: usuários, prospects, funcionários, dependentes, terceiros. Flag: contém crianças? adolescentes? idosos?
- Categorias de dados:
- Comuns: nome, e-mail, CPF (atenção — alguns tratam como sensível operacional), telefone, endereço, IP, device ID
- Sensíveis (Art. 5º, II): saúde, biometria, raça, religião, política, sindicato, sexualidade, genético
- Fonte/origem: coletado do titular? observado? inferido? terceiro?
- Sistemas que armazenam: tabelas, caches, S3 buckets, BigQuery, planilhas
- Compartilhamentos:
- Internos: outros times/empresas do grupo
- Externos operadores: lista
- Transferência internacional: lista (link para
.lgpd/transfers/)
- Retenção: prazo + critério
- Medidas de segurança: criptografia, RBAC, audit log
- Alto risco?: Aplicar teste Res. 2/2022 Art. 4 → se sim, marca para RIPD
- Owner: pessoa/time responsável
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.
- 11d ago First seen · 119 lines · 107 tokens per session scan A a3b3d1641c1d
lgpd-data-mapping is a skill published in the GitHub repository goul4rt/lgpd-skills (52 stars, last pushed 3mo ago), licensed MIT. It adds 107 tokens to every session and 1,332 once invoked, about $0.0005 per session on Opus 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 2026-08-30.
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