Resume Bullet Writer
Transform weak resume bullets into achievement-focused statements with metrics and impact. Use when the user wants to rewrite bullets, strengthen accomplishments, or add action verbs and impact to their resume.
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.
--- name: enrich-lead description: "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." user-invocable: true argument-hint: "[name, company, LinkedIn URL, or email]" --- # Enrich Lead Turn any identifier into a full contact dossier. The user provides identifying info via "$ARGUMENTS". ## Examples - `/apollo:enrich-lead Tim Zheng at Apollo` - `/apollo:enrich-lead https://www.linkedin.com/in/timzheng` - `/apollo:enrich-lead sarah@stripe.com` - `/apollo:enrich-lead Jane Smith, VP Engineering, Notion` - `/apollo:enrich-lead CEO of Figma` ## Step 1 — Parse Input From "$ARGUMENTS", extract every identifier available: - First name, last name - Company name or domain - LinkedIn URL - Email address - Job title (use as a matching hint) 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. ## Step 2 — Enrich the Person > **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 known - `domain` or `organization_name` if company is known - `linkedin_url` if LinkedIn is provided - `email` if email is provided - Set `reveal_personal_emails` to `true` If 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. ## Step 3 — Enrich Their Company Use `mcp__claude_ai_Apollo_MCP__apollo_organizations_enrich` with the person's company domain to pull firmographic context. ## Step 4 — Present the Contact Card 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 | --- ## Step 5 — Offer Next Actions Ask the user which action to take: 1. **Save to Apollo** — Create this person as a contact via `mcp__claude_ai_Apollo_MCP__apollo_contacts_create` with `run_dedupe: true` 2. **Add to a sequence** — Ask which sequence, then run the sequence-load flow 3. **Find colleagues** — Search for more people at the same company using `mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search` with `q_organization_domains_list` set to this company 4. **Find similar people** — Search for people with the same title/seniority at other companies--- name: enrich-lead description: "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." user-invocable: true argument-hint: "[name, company, LinkedIn URL, or email]" --- # Enrich Lead Turn any identifier into a full contact dossier. The user provides identifying info via "$ARGUMENTS". ## Examples - `/apollo:enrich-lead Tim Zheng at Apollo` - `/apollo:enrich-lead https://www.linkedin.com/in/timzheng` - `/apollo:enrich-lead sarah@stripe.com` - `/apollo:enrich-lead Jane Smith, VP Engineering, Notion` - `/apollo:enrich-lead CEO of Figma` ## Step 1 — Parse Input From "$ARGUMENTS", extract every identifier available: - First name, last name - Company name or domain - LinkedIn URL - Email address - Job title (use as a matching hint) 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. ## Step 2 — Enrich the Person > **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 known - `domain` or `organization_name` if company is known - `linkedin_url` if LinkedIn is provided - `email` if email is provided - Set `reveal_personal_emails` to `true` If 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. ## Step 3 — Enrich Their Company Use `mcp__claude_ai_Apollo_MCP__apollo_organizations_enrich` with the person's company domain to pull firmographic context. ## Step 4 — Present the Contact Card 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 | --- ## Step 5 — Offer Next Actions Ask the user which action to take: 1. **Save to Apollo** — Create this person as a contact via `mcp__claude_ai_Apollo_MCP__apollo_contacts_create` with `run_dedupe: true` 2. **Add to a sequence** — Ask which sequence, then run the sequence-load flow 3. **Find colleagues** — Search for more people at the same company using `mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search` with `q_organization_domains_list` set to this company 4. **Find similar people** — Search for people with the same title/seniority at other companies
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.