explorium-cli

B2B data enrichment CLI. Match companies/prospects to IDs, enrich with firmographics, contacts, profiles, tech stack, funding, and more. Includes AI-powered company research with web search.

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Install skill "explorium-cli" with this command: npx skills add haroexplorium/explorium-cli/haroexplorium-explorium-cli-explorium-cli

Explorium CLI

B2B data enrichment CLI. Match companies/prospects to IDs, enrich with firmographics, contacts, profiles, tech stack, funding, and more. Includes AI-powered company research with web search.

Setup (run once per session if needed)

Cowork VM (recommended)

If the repo is already cloned, run the setup script. It pulls latest code, installs the CLI, syncs this skill, and configures the API key:

cd /path/to/explorium-cli && ./setup-cowork.sh

If the repo is not yet cloned:

git clone https://github.com/haroExplorium/explorium-cli.git cd explorium-cli && ./setup-cowork.sh

Standalone binary install

which explorium 2>/dev/null || ls ~/.local/bin/explorium 2>/dev/null

If not found, install with the one-liner:

curl -fsSL https://raw.githubusercontent.com/haroExplorium/explorium-cli/main/install.sh | bash export PATH="$HOME/.local/bin:$PATH"

Configure API key

explorium config show

If output shows api_key: NOT SET (exit code 1), ask the user for their Explorium API key using AskUserQuestion, then:

explorium config init -k <API_KEY>

Global Options

Place BEFORE the subcommand:

-o, --output {json|table|csv} Output format (default: json) --output-file PATH Write to file (clean output, no formatting)

Commands Reference

See commands-reference.md in this directory for the full command reference with all options.

Businesses

Command Purpose Key Options

businesses match

Match companies to IDs --name , --domain , --linkedin , -f FILE , --summary , --ids-only

businesses search

Search/filter businesses --country , --size , --revenue , --industry , --tech , --total N

businesses enrich

Firmographics (single) --id , --name , --domain , --min-confidence

businesses enrich-tech

Technology stack Same ID resolution options

businesses enrich-financial

Financial indicators Same ID resolution options

businesses enrich-funding

Funding & acquisitions Same ID resolution options

businesses enrich-workforce

Workforce trends Same ID resolution options

businesses enrich-traffic

Website traffic Same ID resolution options

businesses enrich-social

LinkedIn posts Same ID resolution options

businesses enrich-ratings

Employee ratings Same ID resolution options

businesses enrich-keywords

Website keywords Same ID resolution options + --keywords

businesses enrich-challenges

10-K challenges Same ID resolution options

businesses enrich-competitive

Competitive landscape Same ID resolution options

businesses enrich-strategic

Strategic insights Same ID resolution options

businesses enrich-website-changes

Website changes Same ID resolution options

businesses enrich-webstack

Web technologies Same ID resolution options

businesses enrich-hierarchy

Company hierarchy Same ID resolution options

businesses enrich-intent

Bombora intent signals Same ID resolution options

businesses bulk-enrich

Bulk firmographics --ids , -f FILE , --match-file , --summary

businesses enrich-file

Match + enrich in one -f FILE , --types , --summary

businesses lookalike

Similar companies --id , --name , --domain

businesses autocomplete

Name/industry/tech suggestions --query , --field {name,industry,tech}

businesses events list

List event types --ids , --events

businesses events enroll

Subscribe to events --ids , --events , --key

businesses events enrollments

List subscriptions

Prospects

Command Purpose Key Options

prospects match

Match people to IDs --first-name , --last-name , --company-name , --email , --linkedin , -f FILE , --summary , --ids-only

prospects search

Search prospects --business-id , --company-name , -f FILE , --job-level , --department , --job-title , --country , --has-email , --has-phone , --total N , --max-per-company N , --summary

prospects enrich contacts

Emails & phones (single) --id , --first-name , --last-name , --company-name , --email , --linkedin

prospects enrich social

LinkedIn posts Same ID resolution options

prospects enrich profile

Professional profile Same ID resolution options

prospects bulk-enrich

Bulk enrich (with -f FILE : preserves input columns with input_ prefix; with --ids : enrichment fields only) --ids , -f FILE , --match-file , --types {contacts,profile,all} , --summary

prospects enrich-file

Match + enrich in one -f FILE , --types {contacts,profile,all} , --summary

prospects autocomplete

Name/title/dept suggestions --query , --field {name,job-title,department}

prospects statistics

Aggregated insights --business-id , --group-by

prospects events list

List event types --ids , --events

prospects events enroll

Subscribe to events --ids , --events , --key

prospects events enrollments

List subscriptions

Research

AI-powered company research using Claude + web search. Requires ANTHROPIC_API_KEY environment variable.

Command Purpose Key Options

research run

Research companies with AI + web search -f FILE , --prompt , --threads , --max-searches , --no-polish , --verbose

research run

Reads a CSV/JSON file, asks a question about each company using AI with web search, and outputs the original data with 3 new columns: research_answer , research_reasoning , research_confidence .

-f, --file FILENAME Input CSV or JSON file with company records [required] -p, --prompt TEXT Research question to answer for each company [required] -t, --threads INTEGER Max concurrent research tasks (default: 10) --max-searches INTEGER Max web searches per company (default: 5) --no-polish Skip prompt polishing with Sonnet -v, --verbose Show detailed progress and polished prompt

How it works:

  • Reads input file and auto-detects company name and domain columns

  • Polishes the raw question into a precise research prompt using Claude Sonnet (skip with --no-polish )

  • Fans out research across all companies concurrently (controlled by --threads )

  • Each company is researched using Claude Haiku with web search tool

  • Results are merged back into the original records

Example:

explorium research run -f companies.csv --prompt "Is this a B2B company?" -o csv --output-file researched.csv

Config & Webhooks

Command Purpose

config init -k KEY

Set API key

config show

Display config

config set KEY VALUE

Set config value

webhooks create --partner-id ID --url URL

Create webhook

webhooks get --partner-id ID

Get webhook

webhooks update --partner-id ID --url URL

Update webhook

webhooks delete --partner-id ID

Delete webhook

CSV Column Mapping

The CLI auto-maps CSV columns (case-insensitive):

Businesses: name /company_name /company , domain /website /url , linkedin_url /linkedin

Prospects: full_name /name , first_name , last_name , email /email_address , linkedin /linkedin_url , company_name /company

Research: company_name /company /name /business_name (for company), domain /website /url (for domain)

LinkedIn URLs without https:// are auto-fixed.

Note: All -f /--file options accept CSVs with any number of columns. The CLI reads only the columns it needs and ignores the rest. You can pass the output of one command directly as input to the next without stripping columns.

Stdin Piping

All -f /--file options accept - to read from stdin, enabling command pipelines:

Pipe match output into search, then into enrich

bulk-enrich -f preserves input columns with input_ prefix

explorium businesses match -f companies.csv -o csv 2>/dev/null
| explorium prospects search -f - --job-level cxo --total 10 -o csv 2>/dev/null
| explorium prospects bulk-enrich -f - --types contacts -o csv \

final_results.csv

Format (CSV vs JSON) is auto-detected from content. --summary output goes to stderr and won't corrupt piped data.

Workflows

Single company lookup

explorium businesses enrich --name "Acme Corp" -o table

Single prospect with contacts

explorium prospects enrich contacts --first-name John --last-name Doe --company-name "Acme Corp"

Discover valid filter values

Find valid industry categories for --industry

explorium businesses autocomplete --query "software" --field industry

Find valid technologies for --tech

explorium businesses autocomplete --query "React" --field tech

Find valid job titles

explorium prospects autocomplete --query "founder" --field job-title

Search prospects by company name

No need to resolve business_id manually -- --company-name does it internally

explorium prospects search --company-name "Salesforce" --job-level cxo --country US --total 50 --summary -o csv --output-file results.csv

Bulk: CSV in, enriched CSV out (recommended for files)

One command: match + enrich, flat CSV output

explorium prospects enrich-file -f leads.csv --types all -o csv --summary --output-file enriched.csv explorium businesses enrich-file -f companies.csv --types firmographics -o csv --summary --output-file enriched.csv

Pipeline: match then enrich separately

Match first

explorium prospects match -f leads.csv -o csv --output-file matched.csv --summary

Then enrich the matched file directly (reads prospect_id column)

explorium prospects bulk-enrich -f matched.csv --types all -o csv --output-file enriched.csv

Search and collect

Get 200 SaaS companies in the US

explorium businesses search --country US --tech "Salesforce" --total 200 -o csv --output-file results.csv

Balanced search across companies

Get up to 5 prospects per company (searches each company in parallel)

explorium prospects search -f biz_ids.csv --job-level cxo,vp --max-per-company 5 -o csv --output-file prospects.csv

Full pipeline: companies -> filter -> prospects -> contacts

1. Find target companies

explorium businesses search --country US --tech "Salesforce" --total 100 -o csv --output-file companies.csv

2. Match to get business IDs

explorium businesses match -f companies.csv --ids-only --output-file biz_ids.csv

3. Search prospects across those companies

explorium prospects search -f biz_ids.csv --job-level cxo,vp --has-email --total 200 -o csv --output-file prospects.csv

4. Enrich with contacts

explorium prospects bulk-enrich -f prospects.csv --types all -o csv --output-file enriched.csv

AI Research: answer questions about companies

Research a list of companies with a custom question

explorium research run -f companies.csv --prompt "Does this company use Kubernetes?" -o csv --output-file researched.csv

With more control

explorium research run -f companies.csv
--prompt "What is this company's main product?"
--threads 20
--max-searches 3
--verbose
-o csv --output-file researched.csv

Event-Driven Marketing Leader Discovery

Goal: Find marketing leadership at companies actively posting about events

Step 1: Match and enrich prospects (gets business_id)

explorium prospects enrich-file
-f prospects.csv
--types firmographics
--summary
-o csv --output-file matched_prospects.csv

Step 2: Enrich companies with social posts

explorium businesses enrich-file
-f matched_prospects.csv
--types all
--summary
-o json --output-file companies_with_social.json

Step 3: Filter for event posts

jq -r 'select(.social_posts != null) | select(.social_posts | tostring | test("(?i)(conference|webinar|event|summit)")) | .business_id' companies_with_social.json > event_companies.txt echo "business_id" > event_companies.csv cat event_companies.txt >> event_companies.csv

Step 4: Find marketing leaders

explorium prospects search
-f event_companies.csv
--department "Marketing"
--job-level "cxo,vp"
--has-email
--max-per-company 3
-o csv --output-file marketing_leaders.csv --summary

Step 5: Enrich with contacts

explorium prospects enrich-file
-f marketing_leaders.csv
--types contacts
--summary
-o csv --output-file final_marketing_leaders.csv

Important Notes

  • Match-based enrichment: All enrich commands accept --name /--domain /--linkedin instead of --id -- the CLI resolves the ID automatically

  • --min-confidence (default 0.8): Lower to 0.5-0.7 for fuzzy matches

  • enrich-file is the fastest path for CSV workflows -- combines match + enrich in one command

  • CSV output flattens nested JSON automatically for spreadsheet use

  • --summary shows matched/not-found/error counts on stderr

  • --company-name on prospects search : resolves company names to business IDs automatically (accepts comma-separated names)

  • prospects search --summary : prints aggregate stats (countries, job levels, companies, email/phone counts) to stderr

  • --field on autocomplete: discover valid values for --industry , --tech , --job-title , --department

  • -f - reads from stdin on all file-accepting commands (auto-detects CSV vs JSON)

  • All batch operations retry on transient errors (429, 500-504, ConnectionError, Timeout) with exponential backoff. Failed batches are skipped and partial results are returned.

  • research run requires ANTHROPIC_API_KEY env var. Uses Sonnet for prompt polishing and Haiku + web search for per-company research.

Constraints

  • Use ONLY Explorium CLI for all data operations

  • DO NOT use Vibe Prospecting MCP for operations the CLI can handle

  • Use jq , cut , sort , echo for post-processing (system tools, allowed)

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