Tech Marketing Framework

Product Introduction

Definition: The Tech Marketing Framework is an open-source, MIT-licensed Go-To-Market (GTM) system and terminal-based marketing workbench designed specifically for use with Claude Code. It functions as a "config-as-code" infrastructure that transforms a local git repository into a specialized AI marketing engine, leveraging 11+ Claude Code skills to automate technical content generation.

Core Value Proposition: The framework exists to solve the "distribution gap" for technical founders and early-stage startups. By utilizing a grounded AI approach, it enables developers to build a high-velocity marketing engine without the generic "AI slop" typical of standard LLM wrappers. Its primary objective is to align marketing output with engineering-level technical depth, utilizing keywords such as developer-led marketing, open-source GTM, Claude Code automation, and grounded AI content generation.

Main Features

1. Context-Grounded Skill Architecture: The framework operates through 11+ specialized slash commands (skills) within the Claude Code environment. Unlike generic chatbots, every skill—such as /social-posts, /blog, or /sales-deck—is hard-coded to read from a local directory (docs/inputs/). This directory contains six core markdown files: product brief, target personas, messaging/positioning, competitor intel, testimonials, and brand guidelines. This ensures the AI utilizes actual product specs and verified documentation as the primary source of truth, eliminating hallucinated features and generic marketing jargon.

2. Autonomous Agentic Workflows: The system includes three specialized agents that operate on a "fire and forget" basis. The Asset-Reviewer acts as a QA layer, scanning generated content against brand guidelines to flag banned words or structural anti-patterns. The How-They-Market agent performs parallel web searches across Hacker News, Reddit, and LinkedIn to reverse-engineer competitor flywheels. The Ads-Auditor provides technical analysis of campaign metrics, generating a health score and optimization checklists based on industry benchmarks.

3. Autoresearch Prompt Optimization: Implementing a methodology inspired by Andrej Karpathy, the framework includes an /autoresearch skill. This feature runs autonomous evaluation loops on the system's own prompts. It tests skills against binary evals, mutates prompt instructions one change at a time, and saves the most effective version. This programmatic approach to prompt engineering ensures the marketing output quality improves over time through iterative, data-driven optimization.

Problems Solved

Pain Point: Generic AI tools often produce "AI slop"—content filled with buzzwords like "seamless" and "cutting-edge" that alienates technical audiences. The Tech Marketing Framework solves this by forcing the AI to adopt the voice of the user's actual engineering team. It also addresses the high overhead of GTM execution for small teams who lack a dedicated product marketing manager (PMM).

Target Audience: The primary users are technical founders, AI-driven marketing VPs, Developer Relations (DevRel) engineers, and early-stage startup teams who prefer working in a terminal environment rather than fragmented SaaS dashboards. It is specifically designed for those who manage their documentation in Markdown and use Git for version control.

Use Cases:

• Feature Launch Automation: Generating a coordinated blast across Slack, LinkedIn, and corporate blogs simultaneously using the /launch-roundup command.
• B2B Sales Narrative Construction: Building structured slide decks based on the April Dunford positioning methodology.
• Programmatic SEO/AEO: Creating search-engine and AI-engine optimized blog variants that address specific technical queries identified in competitor research.

Unique Advantages

Differentiation: Unlike traditional marketing SaaS platforms that require cloud-based dashboards and subscription fees, this framework is "Config-as-code." It lives in your local development environment. It respects privacy through a docs/inputs-local/ directory that is gitignored by default, allowing for private data overrides without leaking sensitive company intel to a public repo.

Key Innovation: The core innovation is the integration of Model Context Protocol (MCP) and Claude Code’s filesystem access to enforce "Source of Truth" marketing. By treating marketing assets like software configuration, it ensures that every piece of copy is grounded in real product docs. This shifts marketing from a creative-first process to a data-first engineering process.

Frequently Asked Questions (FAQ)

1. How does the Tech Marketing Framework prevent AI hallucinations? The framework prevents hallucinations by using a "grounded prompt" architecture. Every Claude Code skill is programmatically instructed to read local markdown files in docs/inputs/ before generating content. If a feature or claim is not present in your product_brief.md, the system is designed to flag it or ask for more info rather than fabricating details.

2. Is this tool compatible with OpenAI’s GPT-4 or only Claude? This framework is specifically optimized for Claude Code and the Claude 3.5 Sonnet model. It utilizes Claude-specific slash commands and agentic behaviors tailored to the Claude ecosystem's ability to handle deep context and technical reasoning.

3. Does this require a subscription or SaaS account? No. The Tech Marketing Framework is an open-source project licensed under the MIT License. You simply clone the repository from GitHub and run it locally. The only cost associated is the API usage for Claude Code.

4. Can I customize the marketing agents for my specific industry? Yes. Through the /skill-builder command, users can interactively design and create new skills or agents. Because the entire framework is a git repo of markdown and configuration files, you can easily modify the prompt templates to suit specific niche industries or unique brand voices.