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 carregar-leads-no-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/carregar-leads-no-airtable)<a href="https://agentmods.dev/skills/gethouston/houston/carregar-leads-no-airtable"><img src="https://agentmods.dev/badge/skills/gethouston/houston/carregar-leads-no-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/carregar-leads-no-airtable"><img src="https://agentmods.dev/badge/skills/gethouston/houston/carregar-leads-no-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 | $0.00112 | $0.02082 |
| Opus 5 | $0.00056 | $0.01041 |
| Sonnet 5 | $0.00022 | $0.00416 |
| Haiku 4.5 | $0.00011 | $0.00208 |
Grade A, and why
carregar-leads-no-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 9d 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.
Carregador de Leads no Airtable
Crio uma tabela nova no Airtable para uma lista de leads, com todas as colunas que o resto do pipeline precisa já em vigor, e depois carrego cada registro em lote. Uso agentes em paralelo porque o Airtable impõe um limite de um registro por chamada de criação, carregar 500 registros de forma serial levaria de 8 a 10 minutos; 4 agentes em paralelo reduzem isso para 2 a 3 minutos.
Quando usar
- "Carregue esses leads no Airtable: ".
- "Crie uma nova tabela no Airtable para esse scraping".
- Fase 2 de qualquer um dos dois pipelines do LinkedIn (chamada pelo orquestrador).
- Você tem uma lista de leads em JSON de qualquer origem e quer colocá-los no Airtable com o esquema padrão do pipeline.
Conexões de que preciso
- Airtable (banco de dados) - Obrigatória. Listo as bases, crio a tabela e carrego os registros pela API REST do Airtable via Composio.
Se o Airtable não estiver conectado, eu paro e peço para você conectá-lo na aba Integrações.
Informações de que preciso
- O arquivo de origem com os leads - Obrigatório. Array JSON de objetos. No mínimo, cada linha precisa de
profileUrlefullName. Opcional:headline,commentText,reactionCount,location,connectionsCount,experience,education,skills. Se estiver faltando, eu pergunto: "Onde está a lista de leads? Me passe o caminho de um arquivo JSON ou cole o array." - A base do Airtable - Obrigatória. Se você tiver só uma base, eu a uso. Se tiver várias, eu listo e pergunto qual delas. Se estiver faltando, eu pergunto: "Em qual base do Airtable devo criar a nova tabela?"
- Um nome para a tabela - Opcional. O padrão é
LinkedIn {sourceType} - {author} - {YYYY-MM-DD}, ondesourceTypeé "Commenters" ou "Reactors". Substitua por chamada se você tiver uma convenção de nomes própria.
O esquema da tabela
Crio a tabela com estes campos. Os tipos de campo seguem as convenções da API REST do Airtable.
Identificação do lead (sempre preenchido no carregamento):
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.
- 9d ago First seen · 113 lines · 112 tokens per session scan A 261f604d4092
carregar-leads-no-airtable is a skill published in the GitHub repository gethouston/houston (113 stars, last pushed today), licensed MIT. It adds 112 tokens to every session and 2,082 once invoked, about $0.0006 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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