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 Mazalucas/El-DT-Lightweight-Army --skill data-auditorgit clone --depth 1 https://github.com/Mazalucas/El-DT-Lightweight-ArmyWrote 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/mazalucas/el-dt-lightweight-army/data-auditor)<a href="https://agentmods.dev/skills/mazalucas/el-dt-lightweight-army/data-auditor"><img src="https://agentmods.dev/badge/skills/mazalucas/el-dt-lightweight-army/data-auditor/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/mazalucas/el-dt-lightweight-army/data-auditor"><img src="https://agentmods.dev/badge/skills/mazalucas/el-dt-lightweight-army/data-auditor.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 | $0.00057 | $0.00829 |
| Opus 5.5 | $0.00023 | $0.00332 |
| Sonnet 5.5 | $0.00011 | $0.00166 |
| Haiku 4.5 | $0.00006 | $0.00083 |
Grade A, and why
data-auditor 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.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Protocolos DT (heredar)
Subagente del Director Técnico: ordenar, cuestionar, alternativas, Puntos ciegos / Mejoras detectadas, post-delegación. Multi-agente: DEFER: <rol>.
Regla madre (obligatoria)
Regla 16-numeric-grounding: la IA no calcula mentalmente. Todo número entregado sale de un script ejecutado, se deriva con fórmula visible, o se etiqueta [NO VERIFICADO].
Pipeline operativo
- Inspeccionar la fuente — formato (CSV/XLSX/TSV/tabla pegada), encoding, separadores, headers, unidades, filas. Nunca asumir estructura: mirar primero.
- Detectar runtime —
python3+ pandas >python3stdlib >node. Verreferences/verify-recipes.md. Sin runtime → modo degradado (regla16). - Ejecutar la verificación — primero la tool del repo
tools/data/verify-csv.py(stdlib, perfil + sumas Decimal +--assert-total+--duplicates); script ad-hoc solo para lo que la tool no cubra (XLSX, joins, fórmulas de negocio). Cross-checks siempre: total vs partes, conteo filas, tipos/unidades. Si el análisis es recurrente, guardar el script en el repo del usuario. - Reconciliar — comparar output contra totales declarados en la fuente. Discrepancias = hallazgo principal, no se acomodan.
- Etiquetar — cada cifra de la entrega lleva
[VERIFICADO],[DERIVADO]o[NO VERIFICADO]. - Entregar — sección Verificación numérica (fuente, script, checks, conteo de etiquetas).
Trampas frecuentes (checklist)
- Separador decimal
,vs.y miles (1.234,56vs1,234.56) — declarar interpretación. - Celdas vacías vs cero — no son lo mismo; los vacíos se reportan.
- Filas de subtotales dentro de los datos — excluirlas antes de sumar.
- Porcentajes: ¿sobre qué base? Promedio de porcentajes ≠ porcentaje del total.
- Fechas ambiguas (
03/04— ¿marzo o abril?) — preguntar si cambia el resultado. - Monedas mezcladas en una misma columna.
- Redondeo acumulado: sumar redondeados ≠ redondear la suma.
Cuándo NO sos vos
| Pedido | Rol |
|---|---|
| Interpretar estrategia del negocio con los números | DEFER: product-strategist |
| Estadística de experimentos A/B | DEFER: marketing-strategist (skill ab-testing) |
| Construir dashboard o app sobre los datos | DEFER: frontend / arquitecto |
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 · 52 lines · 57 tokens per session scan A 3db9fe9e7467
data-auditor is a skill published in the GitHub repository Mazalucas/El-DT-Lightweight-Army (4 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 829 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 2026-08-31.
Other skills, from other repositories
product-data-cleanup
Clean a local FF&E CSV schedule by normalizing casing, dimensions, units, language, materials, and formatting. Use when asked to clean, fix, or standardize product data.
office-documents
Create, edit and convert Word, Excel, PowerPoint and PDF artifacts with format-aware verification. Use when producing docx, xlsx, pptx or PDF files; preserve templates and actual document structure, and render or recalculate before delivery.
product-data-import
Normalize supplied FF&E information and record accepted document-job inputs with exact product tags and source hashes. Use for intake or corrections; reusable library saves and schedule adoption go to their record owners.
csv-to-sif
Export a project's FF&E product-library CSV as dealer-system SIF. Use to produce a .sif schedule; use sif-to-csv for the reverse direction.
sif-to-csv
Parse a SIF (Standard Interchange Format) input into a readable preview and optionally append canonical product rows to the project's CSV library. Use when asked to convert or inspect SIF dealer data. For the reverse direction use /as:csv-to-sif.
office-xlsx
Read, create, edit, and validate Excel workbooks, data, and formulas (.xlsx).