What is Tessl?

AI agents are now writing real production code. But as libraries change, APIs evolve, and conventions drift, agents struggle to stay correct. The issue isn't the underlying models, but how context is created, updated, assessed and distributed to make agent effective for you.

Tessl is a platform for managing context for coding agents, treating agent skills and context as software with a complete lifecycle: build, evaluate, distribute, and optimize.

It provides:

• A package manager for installing, updating and managing versioned, agent-agnostic skills and context

• A registry for discovering and distributing evaluated context bundles

• Evaluations to measure and optimize how well context works with agents, preventing regressions as systems change

Skills are software, not static files

Skills are a powerful new unit of software development. But most teams treat agent skills as static artifacts: markdown files, copied prompts, or repos you clone and hope stay relevant. That approach works briefly, then breaks.

Skills need professional developer tooling: versioning, quality checks, dependency management, and continuous validation. Without this lifecycle, agent behavior becomes unpredictable and fixes don't compound.

Tessl's registry indexes over 1,000 skills and hosts documentation for over 10,000+ OSS packages, keeping agent context version-matched to your code and dependencies. Tessl evaluates the quality of skills and context so you know how effective they are for your agents. Skills and context can be found on the Tessl Registry and installed for your agent of choice via the Tessl CLI.

Context for the Open-Source ecosystem

Each context bundle captures how agents should interact with a technology, covering imports, examples, conventions, and common pitfalls. Teams using Tessl saw up to 3.3× improvement in correct API usage across open-source libraries.

Developers can use this context directly, while open-source maintainers and vendors can publish official skills and context so agents use their packages correctly by default. It's like npm or pypi for agent context, a shared layer of structured knowledge that helps agents code safely with the world's software.

Private workspaces: context for your own codebase

Teams can also create private context that describes how their internal systems should be used.

Private context can define:

• Internal proprietary or organisation specific agent skills

• Internal APIs and platform services

• Security and compliance rules

• Naming, data-handling, and architecture conventions

It gives every AI agent in your organization a reliable understanding of your environment, keeping them on-pattern and on-policy. Private context turns your internal knowledge into a reusable layer for agents.

Reliable agents depend on reliable context

As agents become permanent contributors to the codebase, teams need a way to operate, manage, and improve them, just like any other part of the engineering system.

The Tessl Registry makes that context discoverable, standardized, and reusable, giving developers and organizations a way to:

• Start with trusted, evaluated open-source context

• Version and track updates as dependencies change

• Continuously validate that agent behavior stays correct

• Enforce context automatically across agents and repos

Skills are agent and model-agnostic, so teams can maintain consistent behavior across tools like Claude Code, Cursor, Gemini, Codex, and similar environments without locking into a single ecosystem.

You write the context once. Tessl handles distributing it across every agent, every repo, and every tool, so you never have to maintain parallel versions or manually keep guidance in sync.

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