Get Smarter

The description states that Get Smarter is a zero-dependency TypeScript CLI (Node 24+). It installs small, marked, preview-first pointers into conventional instruction files for AI agents like Codex, Claude, and Gemini.

It uses:

• Structured Markdown records
• A shared scanner to validate those records
• A command-line interface (npx get-smarter init .)
• Preview-first messaging
• Schema-valid outbound messages
• A doctor command to report malformed records
• A send command for generating valid outbound messages

The tool is said to preserve existing project instructions while enabling new agents to access only relevant context.

Inference It appears to be a lightweight coordination layer for AI agents working within codebases, designed to reduce friction and risk in cross-agent collaboration.

The author claims that Get Smarter addresses the problem of introducing a new AI agent into an established project where another AI has already built part of it. The core positioning is:

• Avoiding “me as the courier” — removing human involvement in passing context between agents.
• Enabling safe, structured communication between agents without modifying the original codebase.
• Providing a way for agents to read and resolve messages without needing full project history.

Inference The product positions itself as a context coordination tool, not a general-purpose AI agent or coding assistant. It is framed as solving a workflow issue rather than offering a new capability.

The description does not explicitly name target customers or personas. However, it implies usage by:

• Developers working with AI agents in codebases
• Teams using multiple AI tools to build software (e.g., Claude + Codex)
• Individuals who have built projects with one AI and want to introduce another

Inference The ICP likely includes developers or engineering teams who use AI coding assistants, particularly those managing complex or long-running projects where agent handoffs are frequent.

No evidence of pricing, monetization strategy, or business model is provided in the description. The project appears to be a hackathon submission and not yet commercialized.

Inference There is no indication that Get Smarter has a defined revenue model or pricing structure at this stage.

The tool is built using:

• Node.js
• TypeScript
• npm
• Markdown
• Claude, Codex, Gemini, GPT-5.6 (as tools used in development)

It includes:

• A CLI with fourteen focused tests
• Automated test coverage for audience filtering, status lifecycle, malformed records, preview safety, path containment, and resolved-message disappearance
• A demo that runs deterministically in a temp directory
• Zero dependency installation

Inference The tool is technically sound and designed with testability and safety in mind. It uses asymmetric context exchange to avoid shared chat windows.

There is no evidence of traction, customers, or adoption beyond the author’s own use case. The project was submitted as a hackathon entry and has not been published for public use outside of its demo.

Inference No measurable user engagement or product-market fit data exists. It remains an experimental tool developed by one person.

The description does not mention competitors or similar tools in the market. The author frames this as a solution to a specific problem — introducing new AI agents into existing projects — but does not reference prior art or analogous solutions.

Inference There is no evidence of competitive landscape analysis or awareness of existing tools addressing agent coordination in codebases.

• Unproven adoption: No evidence of real-world usage beyond the author’s own workflow.
• Limited scope: The tool is described as a hackathon submission, not a scalable product.
• No commercial viability: No pricing, monetization or business model mentioned.
• Self-reported only: All claims are unverified and based on personal experience.
• Single-person team: The project was built by one individual (Maryann Clark), suggesting limited resources for scaling.

1. What is the actual risk of using this tool in production environments?
2. How does it handle edge cases or failures during message resolution?
3. Has anyone else used this beyond your own workflow?
4. Are there plans to support more AI agents or platforms beyond Claude, Codex, and Gemini?
5. What are the long-term goals for the project — is it intended to evolve into a commercial product?