Tessl is a package manager and registry specifically designed for AI agent development. It helps developers find, install, version, and evaluate 'skills' and 'context'—pre-packaged knowledge and rules—that coding agents rely on. This ensures agents behave consistently, produce higher quality code, and integrate seamlessly with organizational standards and third-party libraries across different tools and projects.
Freemium
How to use Tessl?
Developers use Tessl via its CLI (e.g., `npx tessl search`) or web registry to discover and install pre-built 'skills' for their AI coding agents. These skills, which can be for security (like CodeGuard), infrastructure (Terraform), or APIs, provide agents with specific, versioned context and rules. You can also create and evaluate your own skills based on internal APIs and conventions, then test them in real-world scenarios to ensure they improve agent performance before deploying them across your team's development environment.
Tessl 's Core Features
Centralized Registry: A searchable hub of thousands of pre-built, community, and enterprise 'skills' that provide AI agents with specific knowledge, from secure coding practices to API integrations.
Skill Evaluation & Testing: Built-in 'Task Evals' allow you to rigorously test skills in real-world scenarios to measure their actual impact on agent performance, detecting regressions and ensuring quality.
Versioning & Consistency: Manages skills like software packages, ensuring agents use the correct, version-matched context across all projects and tools to prevent drift and inconsistent behavior.
Enterprise Context Engineering: Enables teams to codify internal APIs, libraries, and development conventions into reusable skills, effectively onboarding AI agents to company-specific practices.
Cross-Agent/Model Compatibility: Skills created in Tessl are designed to be universally compatible, avoiding vendor lock-in and working across different AI agents and underlying LLM models.
Performance Metrics: Provides clear metrics like 'Agent Performance Improvement' (e.g., 1.8X) to show how much a specific skill boosts an agent's success rate on relevant tasks.
Tessl 's Use Cases
Software Engineers: Accelerate development by equipping AI coding assistants with vetted skills for specific frameworks (like React) or libraries, reducing errors and manual review cycles.
DevOps/Platform Engineers: Standardize infrastructure-as-code practices by creating Terraform or Kubernetes skills that guide agents to produce compliant and secure configurations.
Security Teams: Enforce secure coding standards across an organization by distributing evaluated CodeGuard skills that help AI agents prevent common vulnerabilities automatically.
Engineering Managers: Scale AI agent adoption safely across teams by providing a centralized, evaluated library of skills that ensure consistent and high-quality output.
AI/ML Engineers: Bridge the gap between rapid AI prototyping and production discipline by using Tessl to package and evaluate context that makes agents reliable for system-level tasks.
