The runnable library - every mission with the prompt, the steps and what comes back. 131 posts, page 1 of 6.
Call the similar-companies tool over MCP from your own agent: a seed domain in, 25 lookalikes out as JSON with headcount, industry and a similarity score, schema included.
A nightly job that reads new customers from your warehouse CSV, adds them to suppression list <name> and refreshes the LinkedIn and Meta audiences with them excluded.
A script against the REST API that scores every domain in a CSV against ICP definition <name>, appends score and reasons, and writes the ranked file back, retries included.
One CLI command that verifies every email in <file.csv>, writes <out.csv> with status and confidence columns, and exits non-zero when more than 5% of the rows are invalid.
Connect Cursor to the MCP server so your coding agent can resolve any company by domain, find a person by name and company, verify emails and add rows to list <name>.
An hourly polling loop in TypeScript that reads new intent signals on <topic> for list <name> through the REST API and returns only the rows changed since the last cursor.
A REST recipe your app calls on signup: send an email, get back company domain, headcount, industry, tech stack, fit score and a verified flag as JSON, with the credit cost shown.
A cron-driven CLI job that refreshes list <name> against its saved criteria, verifies every new email and posts the added, removed and changed rows to #sales.
Give a Claude Code session live access to list <name> over MCP: read rows, filter by fit score and trigger enrichment, with the server URL, a scoped key and an example call.
The 20 highest-scoring accounts on list <name> as a JSON array your outbound agent can consume, each with reasons, the latest signal and a verified primary contact.
Every Friday, the week's LinkedIn leads and inbound signups scored against your ICP, the quality mix and source campaigns charted, and the scorecard emailed with the CSV attached.
The 40 most relevant companies in a category with headcount, funding, HQ and core technologies, charted and summarized for a board in Google Docs with the full table in Sheets.
Target accounts checked for a verified contact, a full buying committee, a live signal and a HubSpot owner, charted by territory and posted to #sales-leadership with Asana tasks.
Your site and won accounts read, the market split into six to eight scored segments with evidence, the ranking charted, and a deck on where next quarter's pipeline comes from.
Every Monday, the week's GTM data - list growth, signals by type, verification rates, audience sizes and match rates - charted in a Google Doc with the summary posted to #revops.
A thousand verified contacts built in batches of 100, each batch posted into the Slack thread for a spot-check and corrections, the final list saved with a CSV.
Send a name and company to your WhatsApp agent and get back a contact card with a verified work email, a direct number where found, and a one-line note on the person's role.
Missions projected over a credit threshold ask first in the channel they started from, with the estimate and deliverable; your 'yes' runs it, silence leaves it queued.
Forward a prospect's email to your agent address and get back the research on the sender and their company, a fit score, and a reply drafted in your voice.
Message a Telegram bot 'check <company.com>' and get the compressed read within a minute - what they do, size, stack, recent moves - with the full profile saved to Notion.
The ten companies with the strongest new intent posted to #sales each morning; a reply like 'take 3 and 7' enriches those, finds verified contacts and drafts the first touch.
A prospect list built from criteria in a Slack thread, ten sample rows shown first for correction, then the full 500 built, saved as a list and pushed to HubSpot.
Message a domain in Slack and get the two-minute read in the thread: what they do, size, stack, hiring, funding, the buyer verified and the question that opens the call.
A sourced comparison brief on how a company positions itself, the rivals it names, the claims it makes against each, and how its three closest competitors answer.
Open a workspace and the playbook library is waiting - describe the outcome and the agents carry it end to end, on your plan's monthly credits.