Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/MauricioPerera/KDDnpx agentmods add skills/mauricioperera/kdd/kdd-privacy-scanWrote 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/mauricioperera/kdd/kdd-privacy-scan)<a href="https://agentmods.dev/skills/mauricioperera/kdd/kdd-privacy-scan"><img src="https://agentmods.dev/badge/skills/mauricioperera/kdd/kdd-privacy-scan/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/mauricioperera/kdd/kdd-privacy-scan"><img src="https://agentmods.dev/badge/skills/mauricioperera/kdd/kdd-privacy-scan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00121 | $0.01729 |
| Opus 5.5 | $0.00048 | $0.00692 |
| Sonnet 5.5 | $0.00024 | $0.00346 |
| Haiku 4.5 | $0.00012 | $0.00173 |
Grade A, and why
kdd-privacy-scan 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 2mo 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Privacy Scan (Capa 3 de KDD -- PII / flujo de datos)
Produce hallazgos de datos personales y su base legal de tratamiento en el formato de contrato que audita la Capa 3 de KDD. Esta skill NO es el gate -- es la parte creativa/no determinista (mapear puntos de recoleccion, clasificar la categoria de dato, juzgar si hay base legal declarada) que el gate despues valida. Distincion central: el agente decide QUE es un hallazgo de privacidad; el gate solo audita que el artefacto sellado cumpla forma y politica de calidad de datos.
Como kdd-compliance-scan, este dominio NO
vendoriza ningun sellador externo -- vos mismo escribis findings.json ya en forma
final, sin un finalizer aparte.
Cuando usarla
El usuario pide mapear/auditar los datos personales que un repo/producto recolecta, procesa o almacena, y quiere el resultado gobernado por KDD (versionable, gateado en CI, con politica declarativa), no un reporte suelto en prosa.
Insumos que necesitas antes de empezar
repo_root: raiz del repositorio/producto a mapear.scan_dir: donde vas a escribirfindings.json. Por defectoprivacy/scandentro del repo KDD (coincide con el default devalidate_privacy_findings.py); si estas gobernando un repo EXTERNO, cualquier directorio disponible sirve, con tal de pasarselo explicito al gate.- El schema completo vive en
knowledge/data_models/privacy/findings.schema.json-- consultalo si dudas de un campo, no adivines la forma.
Flujo
1. Mapea los puntos de recoleccion
Recorre el repo_root (formularios, endpoints de API, SDKs de terceros/analytics,
logs, colas de eventos) buscando donde entra un dato que identifica o puede
identificar a una persona: nombre, email, telefono, IP, identificadores de
dispositivo, datos financieros, de salud, biometricos, geolocalizacion, etc.
No inventes un finding para llenar un cupo: un mapeo que concluye "todo lo recolectado tiene base legal declarada y categoria baja" es un resultado valido.
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.
- 2mo ago First seen · 143 lines · 121 tokens per session scan A 1ed922c75426
kdd-privacy-scan is a skill published in the GitHub repository MauricioPerera/KDD (11 stars, last pushed yesterday), licensed MIT. It adds 121 tokens to every session and 1,729 once invoked, about $0.0005 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 2026-08-31.
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