oneshot-agent/oneshot-gtm

GTM agent for technical founders. Pay-per-result. Signed receipts. Two surfaces: terminal CLI + local web dashboard.

What it solves

oneshot-gtm is an open-source Go-To-Market (GTM) agent designed for technical founders who are in the pre-Product-Market Fit (PMF) stage. It prevents founders from scaling broken sales motions by encoding founder-led sales discipline (like the Mom Test and Predictable Revenue) directly into the software. Unlike traditional GTM tools that optimize for volume, this tool focuses on low-volume, high-quality, founder-to-founder outreach that is strictly gated by signals and human approval.

How it works

The system operates as a strategy wrapper around the OneShot API toolbox, which handles the actual execution of emails, SMS, and research. It uses a local SQLite ledger to track every action and its associated cost in USDC on the Base network, providing cryptographically signed receipts for every call.

Key components include:

  • Finders: 15 different triggers that monitor public signals (e.g., Show HN posts, funding announcements, GitHub stars, or job changes) to discover prospects that match an Ideal Customer Profile (ICP).
  • Plays: Pre-defined outreach strategies (e.g., "podcast-guest" or "hiring-signal") that draft and send messages based on a founder's profile and a specific offer.
  • Linting: An AI-writing filter that blocks common AI-sounding phrases and sycophantic openers to ensure messages sound human.
  • Cadence Engine: A system for managing follow-ups and tracking outcomes (replies, deals) to calculate actual Customer Acquisition Cost (CAC).

Who it’s for

Technical founders building new products who need to find early adopters and validate their value proposition without relying on expensive, seat-based SaaS subscriptions or opaque, high-volume spam tools.

Highlights

  • Pay-per-result pricing: No monthly subscriptions; costs are settled per call via the OneShot SDK.
  • Founder-led discipline: Built-in soft-gates that prevent scaling moves (like hiring an AE) until specific signals are earned.
  • Local-first state: All data is stored in a local SQLite database and a chmod-600 dotfile for privacy and control.
  • BYO LLM: Supports OpenRouter, OpenAI, and Anthropic via user-provided keys.
  • Signal-based discovery: Uses a wide array of finders to identify prospects based on real-time events rather than static lists.
  • Outcome-attributed CAC: Every spend is linked to a signed receipt, allowing for precise tracking of acquisition costs against actual outcomes.

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