business/sales/apollo-enrich-lead/SKILL.md
Instant lead enrichment. Drop a name, company, LinkedIn URL, or email and get the full contact card with email, phone, title, company intel, and next actions.
npx skillsauth add harsh040506/claude-code-unified-skill-plugin-library enrich-leadInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Turn any identifier into a full contact dossier. The user provides identifying info via "$ARGUMENTS".
/apollo:enrich-lead Tim Zheng at Apollo/apollo:enrich-lead https://www.linkedin.com/in/timzheng/apollo:enrich-lead [email protected]/apollo:enrich-lead Jane Smith, VP Engineering, Notion/apollo:enrich-lead CEO of FigmaFrom "$ARGUMENTS", extract every identifier available:
If the input is ambiguous (e.g. just "CEO of Figma"), first use mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with relevant title and domain filters to identify the person, then proceed to enrichment.
Credit warning: Tell the user enrichment consumes 1 Apollo credit before calling.
Use mcp__claude_ai_Apollo_MCP__apollo_people_match with all available identifiers:
first_name, last_name if name is knowndomain or organization_name if company is knownlinkedin_url if LinkedIn is providedemail if email is providedreveal_personal_emails to trueIf the match fails, try mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with looser filters and present the top 3 candidates. Ask the user to pick one, then re-enrich.
Use mcp__claude_ai_Apollo_MCP__apollo_organizations_enrich with the person's company domain to pull firmographic context.
Format the output exactly like this:
[Full Name] | [Title] [Company Name] · [Industry] · [Employee Count] employees
| Field | Detail | |---|---| | Email (work) | ... | | Email (personal) | ... (if revealed) | | Phone (direct) | ... | | Phone (mobile) | ... | | Phone (corporate) | ... | | Location | City, State, Country | | LinkedIn | URL | | Company Domain | ... | | Company Revenue | Range | | Company Funding | Total raised | | Company HQ | Location |
Ask the user which action to take:
mcp__claude_ai_Apollo_MCP__apollo_contacts_create with run_dedupe: truemcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with q_organization_domains_list set to this companytesting
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
tools
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
testing
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
development
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.