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 convertir-reacciones-de-linkedin-en-prospecciongit 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/convertir-reacciones-de-linkedin-en-prospeccion)<a href="https://agentmods.dev/skills/gethouston/houston/convertir-reacciones-de-linkedin-en-prospeccion"><img src="https://agentmods.dev/badge/skills/gethouston/houston/convertir-reacciones-de-linkedin-en-prospeccion/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/convertir-reacciones-de-linkedin-en-prospeccion"><img src="https://agentmods.dev/badge/skills/gethouston/houston/convertir-reacciones-de-linkedin-en-prospeccion.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 | $0.00123 | $0.02425 |
| Opus 5 | $0.00062 | $0.01213 |
| Sonnet 5 | $0.00025 | $0.00485 |
| Haiku 4.5 | $0.00012 | $0.00243 |
Grade A, and why
convertir-reacciones-de-linkedin-en-prospeccion 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Convertir reacciones de LinkedIn en prospección
Orquestador de principio a fin: entra la URL de una publicación de LinkedIn, sale una campaña de Instantly en pausa. Misma cadena de cinco fases que linkedin-comment-to-outreach, pero yo extraigo reactores en vez de comentaristas.
¿Por qué reactores? Dos razones:
- Volumen - los reactores suelen superar a los comentaristas de 5 a 10 veces. Una publicación con 30 comentaristas suele tener entre 200 y 500 reactores.
- Perfiles más ricos - la extracción de reacciones devuelve el perfil completo de LinkedIn de cada persona (historial de experiencia, educación, habilidades, certificaciones, ubicación, número de contactos) directamente en una sola llamada a Apify. La extracción de comentarios solo devuelve campos superficiales. Esto eleva mucho el techo de personalización.
Compensación: reaccionar es una señal de menor esfuerzo que comentar. Estás cambiando intención por lead a cambio de volumen y profundidad de datos.
Cuándo usarlo
- "Corre el pipeline de reacciones de LinkedIn en esta publicación: ".
- "Extrae y envía un correo a todos los que reaccionaron a esta publicación".
- Una publicación está llegando ampliamente a tu perfil de cliente ideal y quieres cobertura máxima.
- Quieres datos de perfil completo de LinkedIn adjuntos a cada lead (para personalizar el cuerpo del correo, no solo el asunto).
- Prospección de audiencia de nicho: "contadores que reaccionaron a una publicación sobre planeación fiscal", "fundadores que reaccionaron a un hilo sobre levantamiento de capital".
Cuándo NO usarlo
- Solo quieres comentaristas (mayor intención por lead), usa
linkedin-comment-to-outreach. - Solo necesitas la lista de reactores, sin prospección, usa
linkedin-reaction-scraperdirectamente. - Solo necesitas enriquecer una lista existente, usa
apollo-enrichmentdirectamente. - Ya tienes una lista verificada y la copy lista, usa
instantly-campaigndirectamente.
Conexiones que necesito
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 · 130 lines · 123 tokens per session scan A 9df1954d5346
convertir-reacciones-de-linkedin-en-prospeccion is a skill published in the GitHub repository gethouston/houston (113 stars, last pushed today), licensed MIT. It adds 123 tokens to every session and 2,425 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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