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 gethouston/houston --skill cargar-leads-en-airtablegit clone --depth 1 https://github.com/gethouston/houstonWrote 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/gethouston/houston/cargar-leads-en-airtable)<a href="https://agentmods.dev/skills/gethouston/houston/cargar-leads-en-airtable"><img src="https://agentmods.dev/badge/skills/gethouston/houston/cargar-leads-en-airtable/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/gethouston/houston/cargar-leads-en-airtable"><img src="https://agentmods.dev/badge/skills/gethouston/houston/cargar-leads-en-airtable.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 | $0.00106 | $0.02079 |
| Opus 5 | $0.00053 | $0.01040 |
| Sonnet 5 | $0.00021 | $0.00416 |
| Haiku 4.5 | $0.00011 | $0.00208 |
Grade A, and why
cargar-leads-en-airtable 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 8d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cargador de leads en Airtable
Creo una tabla nueva en Airtable para una lista de leads, con todas las columnas que el resto del pipeline necesita ya listas, y luego cargo por lotes cada registro. Uso agentes en paralelo porque Airtable exige un límite de un registro por llamada de creación, la carga en serie de 500 registros tomaría 8 a 10 minutos; con 4 agentes en paralelo eso baja a 2 o 3 minutos.
Cuándo usarme
- "Carga estos leads en Airtable: ".
- "Crea una tabla nueva en Airtable para este scraping".
- Fase 2 de cualquiera de los dos pipelines de LinkedIn (invocada por el orquestador).
- Tienes una lista de leads en JSON de cualquier origen y quieres tenerlos en Airtable con el esquema estándar del pipeline.
Conexiones que necesito
- Airtable (base de datos), obligatoria. Listo las bases, creo la tabla y cargo los registros usando la API REST de Airtable a través de Composio.
Si Airtable no está conectado, me detengo y te pido que la conectes desde la pestaña de Integraciones.
Información que necesito
- El archivo de origen con los leads, obligatorio. Array JSON de objetos. Como mínimo cada fila necesita
profileUrlyfullName. Opcionales:headline,commentText,reactionCount,location,connectionsCount,experience,education,skills. Si falta, pregunto: "¿Dónde está la lista de leads? Pásame una ruta a un archivo JSON o pega el array." - La base de Airtable, obligatoria. Si tienes una sola base, la uso. Si tienes varias, te las listo y te pregunto cuál. Si falta, pregunto: "¿En qué base de Airtable creo la tabla nueva?"
- Un nombre para la tabla, opcional. Por defecto es
LinkedIn {sourceType} - {author} - {YYYY-MM-DD}dondesourceTypees "Commenters" o "Reactors". Puedes indicar otro por llamada si tienes una convención de nombres propia.
El esquema de la tabla
Creo la tabla con estos campos. Los tipos de campo siguen las convenciones de la API REST de Airtable.
Identificación del lead (siempre poblados por la carga):
Full Name(singleLineText)Profile URL(url)Headline(singleLineText)Source Type(singleSelect: "comment", "reaction")Source Post URL(url)Source Author(singleLineText)Scraped At(dateTime)
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.
- 8d ago First seen · 113 lines · 106 tokens per session scan A 09b8c66a111e
cargar-leads-en-airtable is a skill published in the GitHub repository gethouston/houston (113 stars, last pushed today), licensed MIT. It adds 106 tokens to every session and 2,079 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 2026-09-03.
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