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 extraer-reacciones-de-linkedingit 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/extraer-reacciones-de-linkedin)<a href="https://agentmods.dev/skills/gethouston/houston/extraer-reacciones-de-linkedin"><img src="https://agentmods.dev/badge/skills/gethouston/houston/extraer-reacciones-de-linkedin/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/extraer-reacciones-de-linkedin"><img src="https://agentmods.dev/badge/skills/gethouston/houston/extraer-reacciones-de-linkedin.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 | $0.00115 | $0.01685 |
| Opus 5 | $0.00057 | $0.00843 |
| Sonnet 5 | $0.00023 | $0.00337 |
| Haiku 4.5 | $0.00012 | $0.00169 |
Grade A, and why
extraer-reacciones-de-linkedin 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extraer reacciones de LinkedIn
Extraigo a todas las personas que reaccionaron a una publicación de LinkedIn y armo una lista limpia y sin duplicados, con el perfil completo de LinkedIn adjunto a cada fila en un solo paso. Es la fase 1 del pipeline de reacciones a prospección, y también se puede ejecutar de forma independiente si solo necesitas la lista.
La gran ventaja frente a la extracción de comentarios: profileScraperMode: "main" hace que el actor devuelva directamente el historial de experiencia, la educación, las habilidades, las certificaciones, la ubicación y el número de contactos de quien reaccionó. No hace falta un segundo paso de enriquecimiento para los datos de perfil (el enriquecimiento de Apollo sigue siendo necesario para los correos verificados).
Cuándo usarlo
- "Extrae a los que reaccionaron a esta publicación de LinkedIn: ".
- "Sácame una lista de quién reaccionó a esta publicación, con sus perfiles".
- Quieres una lista limpia y sin duplicados de quienes reaccionaron, con datos de perfil ricos para cualquier uso posterior.
Cuándo NO usarlo
- Quieres comentaristas (menor volumen, mayor intención por lead), usa
linkedin-comment-scraper. - Quieres el pipeline completo de principio a fin hasta Instantly, usa
linkedin-reaction-to-outreach.
Conexiones que necesito
- Apify (extracción) - Requerida. Uso el actor
harvestapi/linkedin-post-reactionsconprofileScraperMode: "main".
Si Apify no está conectado, me detengo y te pido que la conectes desde la pestaña de Integraciones.
Información que necesito
- La URL de la publicación de LinkedIn - Requerida.
- Una cantidad objetivo de elementos - Opcional. Por defecto usa
defaultMaxItemsde tu contexto de prospección (500). Las extracciones de reacciones suelen llegar a 500+ en una publicación popular; súbelo si quieres cobertura completa de una publicación viral.
Pasos
- Validar la URL. Mismas reglas que en la extracción de comentarios: debe ser una URL de una publicación de LinkedIn. Rechazo URLs de perfil, artículo o empresa. Resuelvo enlaces cortos una sola vez.
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 · 100 lines · 115 tokens per session scan A ffdb91740523
extraer-reacciones-de-linkedin is a skill published in the GitHub repository gethouston/houston (113 stars, last pushed today), licensed MIT. It adds 115 tokens to every session and 1,685 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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