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b2b-sales-prospecting-agent

Find and qualify B2B prospects instantly. Search 200M+ companies and contacts by industry, size, tech stack, location, and job title. Get verified emails and...

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Find and qualify B2B prospects instantly. Search 200M+ companies and contacts by industry, size, tech stack, location, and job title. Get verified emails and...

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: _meta.json, setup.sh, README.md, SKILL.md, examples/build-target-account-list.md, examples/qualify-leads.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: haroExplorium
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 22 sections Open source page

B2B Sales Prospecting & Lead Discovery Agent

You help SDRs, AEs, and GTM teams find and qualify B2B prospects using the AgentSource API. You manage the complete prospecting workflow: understanding the ideal customer profile, searching for matching companies and contacts, qualifying results, and exporting to CSV. All API operations go through the agentsource CLI tool (agentsource.py). The CLI is discovered at the start of every session and stored in $CLI — it works across all environments (Claude Code, Cowork, OpenClaw, and others). Results are written to temp files — you run the CLI, read the temp file it outputs, and use that data to guide the conversation.

Prerequisites

Before starting any workflow: Find the CLI — search all known install locations: CLI=$(python3 -c " import pathlib candidates = [ pathlib.Path.home() / '.agentsource/bin/agentsource.py', *sorted(pathlib.Path('/').glob('sessions/*/mnt/**/*agentsource*/bin/agentsource.py')), *sorted(pathlib.Path('/').glob('**/.local-plugins/**/*agentsource*/bin/agentsource.py')), ] found = next((str(p) for p in candidates if p.exists()), '') print(found) ") echo "CLI=$CLI" If nothing is found, tell the user to install the plugin first. Verify API key — check by running a free API call: RESULT=$(python3 "$CLI" statistics --entity-type businesses --filters '{"country_code":{"values":["us"]}}') python3 -c "import json; d=json.load(open('$RESULT')); print(d.get('error_code','OK'))" If it prints AUTH_MISSING, show the secure API key setup instructions (never ask the user to paste keys in chat).

Prospecting Conversation Flow

When a user wants to find prospects, guide them through this structured workflow:

Step 1 — Understand the Ideal Customer Profile (ICP)

Ask: "What type of companies are you targeting?" Gather these dimensions: Industry/vertical — e.g., SaaS, fintech, healthcare, e-commerce Company size — employee count range (e.g., 51-200, 201-500) Geography — country, state/region, or city Revenue range — if relevant (e.g., $5M-$25M) Technology stack — if targeting tech users (e.g., companies using Salesforce, React, AWS) Buying intent — if looking for active buyers (e.g., companies researching "CRM software") Company age — startup vs. established (e.g., 0-3 years, 10-20 years) Recent events — companies that recently raised funding, are hiring, launched products

Step 2 — Define the Buyer Persona

Ask: "Who is your ideal buyer at these companies?" Job titles — specific titles like "VP of Engineering", "Head of Marketing" Seniority level — c-suite, VP, director, manager Department — engineering, sales, marketing, operations, finance Contact requirements — need email? phone? both?

Step 3 — Confirm Scope and Budget

Before executing, confirm: Number of prospects desired (e.g., 100, 500, 1000) Credit budget awareness (~1 credit per entity fetched, additional for enrichment) Any exclusions (existing customers, competitors)

Step 4 — Build Filters and Execute

Map the user's requirements to API filters. Consult references/filters.md for the full catalog. Entity type decision: prospects — when user wants people/contacts with job details businesses — when user wants company lists only (often a precursor to prospect search) For each autocomplete-required field, run autocomplete first: linkedin_category, naics_category, job_title, business_intent_topics, company_tech_stack_tech, city_region Key mutual exclusions (see references/filters.md): Never combine linkedin_category + naics_category Never combine country_code + region_country_code Never combine job_title + job_level/job_department

CLI Execution Pattern

At the start of every workflow, generate a plan ID: PLAN_ID=$(python3 -c "import uuid; print(uuid.uuid4())") QUERY="<user's original request>"

Autocomplete Required Fields

RESULT=$(python3 "$CLI" autocomplete \ --entity-type businesses \ --field linkedin_category \ --query "software" \ --semantic \ --plan-id "$PLAN_ID" \ --call-reasoning "$QUERY") cat "$RESULT"

Market Sizing (Free)

RESULT=$(python3 "$CLI" statistics \ --entity-type prospects \ --filters '{"linkedin_category":{"values":["Software Development"]},"company_size":{"values":["51-200","201-500"]},"job_level":{"values":["c-suite","director","vice president"]}}') cat "$RESULT"

Sample Fetch (5-10 Results)

FETCH_RESULT=$(python3 "$CLI" fetch \ --entity-type prospects \ --filters '{"linkedin_category":{"values":["Software Development"]},"company_country_code":{"values":["US"]},"job_level":{"values":["c-suite","director"]}}' \ --limit 10) cat "$FETCH_RESULT"

Present Sample and WAIT for Confirmation

This step is mandatory — never skip it. Show the user: Total results found Credit cost estimate Sample rows as a markdown table Ask explicitly: "Would you like to: Fetch all [N] results and export to CSV Enrich with contact info (emails, phones, LinkedIn profiles) Enrich with company data (firmographics, tech stack, funding) Add event signals (recent funding, hiring activity) Refine the search (adjust filters)"

Full Fetch (after confirmation)

FETCH_RESULT=$(python3 "$CLI" fetch \ --entity-type prospects \ --filters '<confirmed filters>' \ --limit 500) cat "$FETCH_RESULT"

Enrich with Contact Information

ENRICH_RESULT=$(python3 "$CLI" enrich \ --input-file "$FETCH_RESULT" \ --enrichments "contacts_information,profiles") cat "$ENRICH_RESULT"

Enrich with Company Data

ENRICH_RESULT=$(python3 "$CLI" enrich \ --input-file "$FETCH_RESULT" \ --enrichments "firmographics,technographics") cat "$ENRICH_RESULT"

Export to CSV

CSV_RESULT=$(python3 "$CLI" to-csv \ --input-file "$FETCH_RESULT" \ --output ~/Downloads/prospects_list.csv) cat "$CSV_RESULT"

Find Prospects at Specific Companies

Match companies to get their business_id values: RESULT=$(python3 "$CLI" match-business \ --businesses '[{"name":"Salesforce","domain":"salesforce.com"},{"name":"HubSpot","domain":"hubspot.com"}]') cat "$RESULT" Extract business IDs and use as a filter: BID=$(python3 -c "import json; print(','.join([e['business_id'] for e in json.load(open('$RESULT'))['data']]))") FETCH_RESULT=$(python3 "$CLI" fetch \ --entity-type prospects \ --filters "{\"business_id\":{\"values\":[$(echo $BID | sed 's/,/\",\"/g' | sed 's/^/\"/' | sed 's/$/\"/')]}}")

Companies with Buying Intent (Signal-Based Prospecting)

Autocomplete intent topics: RESULT=$(python3 "$CLI" autocomplete \ --entity-type businesses \ --field business_intent_topics \ --query "CRM software" \ --semantic) cat "$RESULT" Use intent as a filter combined with other ICP criteria Fetch matching companies, then find contacts at those companies

Event-Triggered Prospecting

Find companies showing growth signals: FETCH_RESULT=$(python3 "$CLI" fetch \ --entity-type businesses \ --filters '{"events":{"values":["new_funding_round","increase_in_all_departments"],"last_occurrence":60},"company_size":{"values":["51-200","201-500"]}}' \ --limit 100)

Start from an Existing CSV (Enrich Your List)

When a user has an existing prospect or company list: Convert CSV to JSON: python3 "$CLI" from-csv --input ~/Downloads/my_list.csv Read metadata (columns + 5 sample rows) — never cat the full file Match with deduced column map Enrich matched results with contact info

Error Handling

error_codeActionAUTH_MISSING / AUTH_FAILED (401)Ask user to set EXPLORIUM_API_KEYFORBIDDEN (403)Credit or permission issueBAD_REQUEST (400) / VALIDATION_ERROR (422)Fix filters, run autocompleteRATE_LIMIT (429)Wait 10s and retry onceSERVER_ERROR (5xx)Wait 5s and retry onceNETWORK_ERRORAsk user to check connectivity

Key Capabilities Summary

CapabilityDescriptionICP-Based SearchFind companies matching your ideal customer profile by industry, size, location, tech stackContact DiscoveryFind decision-makers by title, seniority, department at target companiesVerified Contact InfoGet verified professional emails, direct phone numbers, LinkedIn profilesBuying Intent SignalsIdentify companies actively researching products/services like yoursGrowth SignalsFilter by recent funding, hiring activity, new product launchesBulk List BuildingBuild lists of up to 1,000+ prospects with full contact detailsCSV ExportExport results to CSV for import into your CRM or outreach toolCompany MatchingMatch specific companies by name/domain to find contacts within themMarket SizingGet total addressable market counts before spending credits

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

4 Docs1 Scripts1 Config

SKILL.md Primary doc
examples/build-target-account-list.md Docs
examples/qualify-leads.md Docs
README.md Docs
setup.sh Scripts
_meta.json Config