GTM Signal Aggregator

    1

    Aggregates hiring, tech stack, and intent signals into one score per account.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+17 more

    See it in action

    You say

    Aggregate hiring and tech stack signals for these 50 domains. Weight tech stack 2x higher than hiring. Branch the messages so tech-heavy accounts get a 'migration' angle.

    Your agent does

    Account: acme.com | Total Score: 85 | Dominant Signal: tech_stack | Branch: Migration Angle. Account: stark.co | Total Score: 40 | Dominant Signal: hiring | Branch: Growth Angle. Cut Analysis: Removed 12 accounts below score 30, estimated 2 lost replies.

    What you get

    Collapse multiple intent sources into a single weighted priority score.Route accounts into different messaging flows based on signal dominance.Preserve signal context so first lines remain relevant to the account trigger.Calculate the opportunity cost of lead filtering thresholds.

    About this skill

    The problem

    Sales teams struggle to prioritize accounts when multiple fragmented signals like hiring, funding, and tech stack changes arrive as separate lists. Without a unified scoring system, you cannot determine which account to work first or which specific signal should trigger your outreach messaging.

    What it does

    • Aggregates hiring, tech stack, and funding data into a single, weighted priority score per account.
    • Maintains individual component scores so the specific reason for the high priority is preserved for messaging.
    • Assigns accounts to specific messaging branches based on their dominant buying signal.
    • Calculates a score threshold cut and estimates the opportunity cost of accounts removed from the sequence.
    • Establishes a re-scoring cadence to prevent outreach based on stale signals.

    Frameworks & tools

    Works with B2B intent data, Clay, and Apify. Integrates with ICP.md and VOICE.md project files.

    Why this beats prompting it yourself

    Manual prompting often results in a single flattened score that loses the context of why an account was flagged. This skill ensures that ties in signal strength are handled as distinct branches and requires the model to justify the weights used based on historic closed-won data or explicit assumptions.

    Use cases

    • Prioritize a massive account list for a new outbound campaign.
    • Route accounts to different email sequences based on dominant intent signals.
    • Determine the opportunity cost of narrowing your sales focus via threshold cutting.
    • Automate signal-based messaging triggers for SDR teams.

    Most premium skills are on a weekly review cycle. Fixes and improvements ship as updates, and buyers receive release notes by email. Part of the Mambabuilt catalog. Packs and the full bundle are available at mambabuilt.com.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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