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-observability-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-observability-scan)<a href="https://agentmods.dev/skills/mauricioperera/kdd/kdd-observability-scan"><img src="https://agentmods.dev/badge/skills/mauricioperera/kdd/kdd-observability-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-observability-scan"><img src="https://agentmods.dev/badge/skills/mauricioperera/kdd/kdd-observability-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 | $0.00134 | $0.01600 |
| Opus 5 | $0.00067 | $0.00800 |
| Sonnet 5 | $0.00027 | $0.00320 |
| Haiku 4.5 | $0.00013 | $0.00160 |
Grade A, and why
kdd-observability-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 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability Scan (Capa 3 de KDD -- gaps de logging/monitoreo)
Produce hallazgos de gaps de observabilidad en el formato de contrato que audita la Capa 3 de KDD. Esta skill NO es el gate -- es la parte creativa/no determinista (identificar rutas criticas, revisar su manejo de errores y cobertura de alertas/tracing, juzgar el impacto de cada gap) que el gate despues valida. Distincion central: el agente decide QUE es un gap de observabilidad; el gate solo audita que el artefacto sellado cumpla forma y politica de calidad de datos.
Como los demas dominios nativos de KDD (kdd-compliance-scan,
kdd-privacy-scan, kdd-accessibility-scan, kdd-dependency-eol-scan),
esta skill NO vendoriza ningun sellador externo -- vos mismo escribis
findings.json ya en forma final.
Cuando usarla
El usuario pide auditar si los errores de un sistema se detectan a tiempo (logueados, alertados, trazables) 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 revisar.scan_dir: donde vas a escribirfindings.json. Por defectoobservability/scandentro del repo KDD (coincide con el default devalidate_observability_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/observability/findings.schema.json-- consultalo si dudas de un campo, no adivines la forma.
Flujo
1. Identifica las rutas criticas
Antes de revisar codigo, fija por escrito (2-3 lineas) que operaciones son criticas para este sistema: las que mueven dinero, autentican usuarios, escriben datos irreversibles, o cuyo fallo silencioso tendria el mayor costo de deteccion tardia. Esto acota donde buscar -- no hace falta revisar cada linea del repo, si el sistema entero.
2. Revisa manejo de errores en esas rutas
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 · 139 lines · 134 tokens per session scan A d6b913946723
kdd-observability-scan is a skill published in the GitHub repository MauricioPerera/KDD (7 stars, last pushed 2d ago), licensed MIT. It adds 134 tokens to every session and 1,600 once invoked, about $0.0007 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 2026-08-31.
Other skills, from other repositories
agent-introspection-debugging
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.
crit
Review code changes, a plan, a live page (running dev server), or a local HTML file with Crit inline comments and structured human feedback. Use only when the user explicitly invokes /crit or directly asks to use Crit; a generic review request does not count.
crit-story
Author a crit story and continue the interactive review loop only when the user explicitly invokes crit-story or directly asks you to generate a crit story. Do not infer this skill from generic review, PR, or diff-review requests.
dev-doctor
Run a development-focused health check on the AIWG repository structure.
config-validator
Validate AIWG configuration files and project setup for correctness and completeness.
aiwg-doctor
Run a comprehensive health check on the AIWG installation and workspace with pass/fail diagnostics and remediation steps.