enriquecer-leads-con-apollo

enriquecer-leads-con-apollo is a skill for Claude Code, Codex from gethouston/houston. It costs 110 tokens per session (2,164 once invoked), scanned A, original, MIT.

A lead-enrichment workflow that looks for verified email addresses in Apollo and adds contact details to Airtable. It can also create labelled contacts in Apollo.

In plain words
What is it for?
It is for matching LinkedIn profile URLs to emails, companies, job titles, and locations in batches, then preparing those contacts for later sales workflows.
Why use it?
It fills in missing contact information so a lead list can be used for outreach. It keeps the enriched data in the Airtable table where the leads are tracked.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

Good fit It is for matching LinkedIn profile URLs to emails, companies, job titles, and locations in batches, then preparing those contacts for later sales workflows.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gethouston/houston/enriquecer-leads-con-apollo
Install

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.

Any agent
npx skills add gethouston/houston --skill enriquecer-leads-con-apollo
Clone the repo
git clone --depth 1 https://github.com/gethouston/houston

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for enriquecer-leads-con-apollo

README.md
[![agentmods](https://agentmods.dev/badge/skills/gethouston/houston/enriquecer-leads-con-apollo/github.svg)](https://agentmods.dev/skills/gethouston/houston/enriquecer-leads-con-apollo)
Your own site
<a href="https://agentmods.dev/skills/gethouston/houston/enriquecer-leads-con-apollo"><img src="https://agentmods.dev/badge/skills/gethouston/houston/enriquecer-leads-con-apollo/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.

agentmods 80×15 button for enriquecer-leads-con-apollo

Your own site · 80×15
<a href="https://agentmods.dev/skills/gethouston/houston/enriquecer-leads-con-apollo"><img src="https://agentmods.dev/badge/skills/gethouston/houston/enriquecer-leads-con-apollo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 110 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,164 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00110 $0.02164
Opus 5 $0.00055 $0.01082
Sonnet 5 $0.00022 $0.00433
Haiku 4.5 $0.00011 $0.00216

Measured 9d ago against content hash d52acabebd1d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

enriquecer-leads-con-apollo 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.

store/agents-i18n/es/outbound/.agents/skills/enriquecer-leads-con-apollo/SKILL.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Enriquecimiento con Apollo

Tomo una lista de leads en una tabla de Airtable y busco emails verificados para la mayor cantidad posible usando el endpoint de bulk match de Apollo. Actualizo las filas de Airtable directamente con email, empresa, cargo y ubicación, y creo contactos en Apollo bajo una etiqueta con nombre para que los leads lleguen a tus flujos de trabajo del CRM de Apollo. La tasa de coincidencia depende mucho de la audiencia, espera entre 50% y 70% en audiencias de fundadores/operadores en Estados Unidos, y menos en audiencias de consumo o fuera de Estados Unidos.

Cuándo usarme

  • "Enriquece estos leads con Apollo: ".
  • "Busca emails para las filas de esta tabla".
  • Fase 3 de cualquiera de los dos pipelines de LinkedIn (invocada por el orquestador).
  • Tienes una tabla de Airtable con Profile URLs cargados y quieres agregarles emails.

Cuándo NO usarme

  • Los leads todavía no están en Airtable, cárgalos primero con airtable-lead-loader.
  • Solo quieres leer datos de Apollo, no modificar Airtable, esta habilidad escribe de vuelta en Airtable como parte de su contrato; si solo necesitas una búsqueda puntual en Apollo, hazla manualmente.

Conexiones que necesito

  • Airtable (base de datos), obligatoria. Leo las filas y luego escribo de vuelta los campos de enriquecimiento.
  • Apollo (enriquecimiento), obligatoria. Uso el endpoint apollo_people_bulk_match y el endpoint apollo_contacts_create a través de Composio.

Si falta cualquiera de las dos, me detengo y te pido que la conectes.

Información que necesito

  • El ID de la base de Airtable + el ID de la tabla, obligatorio. Si se invoca desde un orquestador, ambos se pasan directamente. Si se invoca de forma independiente, listo las bases y tablas y pregunto cuál si hay alguna ambigüedad.
  • Una etiqueta de contacto de Apollo, opcional. Por defecto es LinkedIn {sourceType} - {sourceAuthor} Post, derivada de los campos Source Type y Source Author de la tabla (toda fila de una tabla dada tiene el mismo origen). Puedes indicar otra por llamada.

Read the full file on GitHub · 112 lines

Changes

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.

  1. 9d ago First seen · 112 lines · 110 tokens per session scan A d52acabebd1d

Subscribe to this mod's changes

enriquecer-leads-con-apollo is a skill published in the GitHub repository gethouston/houston (113 stars, last pushed today), licensed MIT. It adds 110 tokens to every session and 2,164 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.

Related

Other skills, from other repositories

amazon-reviews-api-skill

This skill helps users automatically extract Amazon product reviews via the Amazon Reviews API. Agent should proactively apply this skill when users express needs like getting reviews for Amazon product with ASIN B07TS6R1SF, analyzing customer feedback for a specific Amazon item, getting ratings and comments for a…

browser-act/skills · 124 tokens

amazon-competitor-analyzer

Scrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.

browser-act/skills · 48 tokens

asc-subscription-localization

Bulk-localize subscription, subscription-group, and in-app purchase display names across App Store locales using asc, including API 4.4.1 version-scoped v2 resources. Use when filling or updating subscription/IAP names and descriptions without App Store Connect UI work.

rorkai/app-store-connect-cli-skills · 60 tokens

food-order

Reorder previous Foodora orders, preview cart contents, and track delivery ETA/status with ordercli. Use when the user wants to reorder food, check delivery status, or browse recent Foodora order history. Never confirm an order without explicit user approval.

Bitterbot-AI/bitterbot-desktop · 53 tokens

product-description-generator

E-commerce product description generator for any platform. Generates optimized titles, bullet points, descriptions, and backend keywords using competitor research + keyword scoring + FABE copywriting. Two modes: (A) Create — generate listing from product specs with optional competitor analysis, (B) Optimize — improve…

nexscope-ai/eCommerce-Skills · 126 tokens

amazon-price-tracker

Amazon price monitoring and competitive pricing intelligence. Real-time price tracking, Buy Box analysis, promotion detection, and dynamic pricing strategy optimization. Use when the user asks about price monitoring, competitor pricing, Buy Box tracking, or pricing strategy.

nexscope-ai/Amazon-Skills · 51 tokens