Ocean Io

Overview#

This skill connects your OpenClaw agent to Ocean.io's B2B intelligence platform. Use it to build prospect lists, enrich accounts, find lookalike companies, and surface the right contacts — all from natural language commands.

Ocean.io covers 60M+ companies and 200M+ people. Credits are consumed at 0.2 per record for both searches and exports.

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When to use this skill#

Activate Ocean.io when the user wants to:

• Find companies similar to existing customers ("lookalike" search)
• Build a prospect list filtered by industry, size, location, or tech stack
• Identify decision-makers at target accounts by title, department, or seniority
• Export a contact or company list to CSV for use in outreach tools
• Check what fields are available before building a search

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Tools#

search_companies#

Find companies matching ICP criteria or similar to a set of seed domains. Use lookalike_domains when the user wants to replicate their best customers.

Key filters available:

• lookalikeDomains — up to 10 seed domains to find similar companies
• companyMatchingMode — "precise" (same product/service) or "broad" (same industry)
• industries, industryCategories — firmographic segmentation
• companySizes — e.g. ["51-200", "201-500"]
• primaryLocations / otherLocations — HQ country or office presence
• technologies.apps / technologies.categories — tech stack filters
• headcountGrowth — growth signal over 3, 6, or 12 months
• fundingRound — type, amount, and date of last funding
• revenues — revenue band filter
• webTraffic — monthly visits/views range
• excludeDomains — exclude known customers or competitors

Always check list_company_fields if the user asks what data is available.

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search_people#

Find people by job title, seniority, department, or company criteria. The people_fields parameter is required — always specify what to return.

Key filters available:

• jobTitleKeywords — keyword match on title (allOf / anyOf / noneOf)
• seniorities — Owner, Founder, C-Level, VP, Head, Director, Manager, Other
• departments — Sales, Marketing and Advertising, Engineering, Product, etc.
• countries / cities / states — geographic filters
• company_filters — nest any search_companies filters to target people at specific accounts
• changedPositionAfter — recently promoted/hired contacts (format: "YYYY-MM")
• lookalikeLinkedinHandles — find people similar to a given LinkedIn profile
• skills — filter by LinkedIn-listed skills
• connections — filter by LinkedIn connection count

Always check list_people_fields if the user asks what data can be returned.

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export_companies#

Export company records to a CSV file. Returns a download URL for the generated file.

⚠️ Costs credits: 0.2 credits per successfully exported company. Always confirm the number of records and estimated credit cost with the user before calling this tool.

Input: array of company domains. Accepts up to 10,000 domains per request.

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export_people#

Export people records to a CSV file. Returns a download URL for the generated file.

⚠️ Costs credits: 0.2 credits per successfully exported person. Always confirm the number of records and estimated credit cost with the user before calling this tool.

Input: array of LinkedIn handles or URLs (from search_people results). Accepts up to 10,000 per request.

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list_company_fields#

Returns all available fields for company records. Output is static — call once per session and cache the result. Do not call again if already retrieved. Use when the user asks "what data does Ocean.io have on companies?"

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list_people_fields#

Returns all available fields for people records. Output is static — call once per session and cache the result. Use when the user asks "what fields can I get for contacts?"

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list_industries / list_linkedin_industries#

Returns valid industry values for filters. Output is static — call once per session and cache the result. Always validate industry names against this list before passing them to search filters.

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Example workflows#

ICP lookalike prospecting#

"Find 20 companies similar to stripe.com and shopify.com, B2B SaaS, 50-500 employees, US-based."

1. Call search_companies with lookalikeDomains: ["stripe.com", "shopify.com"], companySizes: ["51-200", "201-500"], primaryLocations: { includeCountries: ["us"] }.
2. Present results as a table (name, domain, size, industry, country).
3. Confirm credit cost (N × 0.2) before calling export_companies.

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Finding decision-makers at target accounts#

"Find VP of Sales or Head of Revenue at these 10 companies."

1. Call search_people with jobTitleKeywords: { anyOf: ["VP of Sales", "Head of Revenue"] }, seniorities: ["VP", "Head"], company_filters: { includeDomains: [...] }, people_fields: ["name", "jobTitle", "linkedinUrl", "country", "departments"].
2. Display results as a table.
3. Offer to export — confirm credit cost before calling export_people.

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Trigger-based prospecting (recently funded)#

"Find Series A or B SaaS companies that raised funding in the last 6 months."

1. Call search_companies with fundingRound: { types: ["Series A", "Series B"], date: { from: "<6 months ago>" } }.
2. Optionally layer in industry/size filters if the user specifies an ICP.
3. Suggest following up with search_people to find the relevant contacts at those accounts.

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Hiring signal prospecting#

"Find companies in the sales tech space that are rapidly growing their Sales department."

1. Call search_companies with departmentHeadcountGrowth targeting the Sales department with positive growth over the last 6 months.
2. Cross-reference with industry or tech stack filters to narrow to ICP.

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Full account + contact workflow with export#

"Find Chicago-headquartered companies similar to pandadoc.com and export them to CSV."

1. Call search_companies with lookalikeDomains: ["pandadoc.com"], primaryLocations: { includeCountries: ["us"] }, and a city filter for Chicago.
2. Present results as a table (name, domain, description).
3. State: "This export will consume X credits (N companies × 0.2). Confirm?"
4. On confirmation, call export_companies and return the download URL to the user.

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Credit awareness#

All operations consume credits at 0.2 credits per record — this applies to both search results and exports. Before any search, state the estimated credit cost (num_results × 0.2). Before any export, confirm: "This will export N records at 0.2 credits each = X credits total. Confirm?"

Never call export_companies or export_people without explicit user confirmation.

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Tips#

• Use companyMatchingMode: "precise" for tight lookalikes (same product category). Use "broad" when the user wants a wider net (same industry vertical).
• Combine search_companies + search_people for full account + contact workflows: find the companies first, then pass their domains into company_filters.includeDomains on search_people.
• changedPositionAfter is powerful for timing outreach — a newly hired VP of Sales is actively building their stack.
• Both search_companies and search_people return paginated results. After presenting the first page, offer to load more. Pass the searchAfter token from the last result into the next call to fetch the next page.
• For pagination, pass the searchAfter value from previous results back into the next call.