Builds targeted company and contact lists using Common Room Prospector for net-new prospects or existing accounts with signals. Clarifies object types and refines iteratively.
From common-roomnpx claudepluginhub 8gg-git/knowledge-work-plugins --plugin common-roomThis skill uses the workspace's default tool permissions.
references/prospect-guide.mdGuides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Designs KPI dashboards with metrics selection (MRR, churn, LTV/CAC), visualization best practices, real-time monitoring, and hierarchy for executives, operations, and product teams.
Transforms raw data into narratives with story structures, visuals, and frameworks for executive presentations, analytics reports, and stakeholder communications.
Build targeted account and contact lists using Common Room's Prospector. Supports iterative refinement through natural conversation, intent-based discovery, and both net-new prospecting and signal-based queries against existing accounts.
Common Room's Prospector operates against two fundamentally different object types. Always clarify which one is in play before running a query:
ProspectorOrganization — Companies not yet in Common Room
Organization (in Common Room) — Companies already in your CR workspace
When a user's request could apply to both (e.g., "Show companies hiring AI engineers this month"), clarify:
"Are you looking for net-new companies not yet in Common Room, or filtering accounts already in your workspace?"
The catalog should make this distinction explicit so the LLM can select the right Prospector endpoint.
Fetch the Me object to get the user's segments. When prospecting against Organization records (accounts already in CR), default to filtering within "My Segments" unless the user asks for a broader search.
If criteria are already provided, proceed. Otherwise ask:
"What kind of accounts or contacts are you looking for? For example: company size, industry, job titles, signals like recent product activity or community engagement, geographic region, or specific intent signals like recent funding or job postings."
Use the Common Room object catalog to see available filters for each object type. The key distinction:
Lookalike search: If the user asks to "find companies like [X]", first look up the reference company in Common Room (or via web search if not in CR). Extract its key attributes — industry, employee range, tech stack, funding stage, geography — and propose those as filter criteria. Present the derived criteria to the user for confirmation before running the search, since lookalike targeting works best when the user can refine which attributes matter most.
Prospecting is conversational. Support multi-turn refinement naturally:
Example flow:
Execute the Prospector query with confirmed criteria. Sort by signal strength or fit score where available (not alphabetically).
For ProspectorOrganization (net-new) results:
| Company | Domain | Industry | Size | Capital Raised | Revenue | Location |
|---|
For Organization (in CR) results:
| Company | Industry | Size | Top Signal | Signal Date | Score | CRM Stage |
|---|
Flag any results where data is thin or the most recent signal is older than 90 days.
For ProspectorOrganization results (net-new companies not in CR), run a quick web search on the top 3–5 companies to add context beyond firmographics. CR has no behavioral signals for these companies, so web search fills the gap — look for recent funding, product launches, leadership changes, or news coverage. Include findings as brief annotations next to each company in the results.
references/prospect-guide.md — filter types, signal-based sorting, object type distinctions, and list-building strategies