ocean-io（海洋生物）

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.

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

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.

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.

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.

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.

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?"

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?"

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.

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"] }.

1. Present results as a table (name, domain, size, industry, country).
2. Confirm credit cost (N × 0.2) before calling export_companies.

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"].

1. Display results as a table.
2. Offer to export — confirm credit cost before calling export_people.

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.

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.

1. Cross-reference with industry or tech stack filters to narrow to ICP.

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.

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.

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.