The package manager for agent skills and context
Versioned, evaluated skills and context for agentic software development.
npx tessl search
Used by agentic developers at
Context can drive up to 3.3X improvement in agents use of over 300 libraries
How good is your skill? Put it to the test
Evaluate any skill against structured best practices for descriptions and content.
EVALUATE YOUR SKILLMake agents successful in your environment
Agents don’t know how to develop in your organization, they need to be onboarded. Turn your APIs, libraries, and conventions into agent-usable skills, docs and rules, so agents stop guessing and start behaving like experienced team members.
- Version-matched OSS and internal APIs
- Correct imports, calls, and constraints
- Fewer retries and review cycles
Evaluate what works in real-world scenarios
Not all context is equal, and mistakes can mislead agents or overwhelm context windows. Evaluate and optimize your skills by running agents through real world scenarios, and testing changes to avoid regressions over time.
- Repeatable task evaluations
- Regression detection as skills, agents and models evolve
- Learn if your context helps or hurts agent performance
Create skills once. Use them across all agents and models
Tessl gives you a single source of truth for skills and context, reusable across agents, models, and development environments without duplication or drift.
- Avoid lock-in with universally compatible context
- Consistent behavior across agents
- Collaborate on context with your team and agents
Keep your agent on the rails with better context. Discover thousands of evaluated skills in the Tessl Registry.
Don’t take our word for it
Latest Articles
Explore our guides and resources to understand key concepts relating to Al agents.
AI Native DevCon’26: The London conference for developers building with AI
AI Native DevCon'26 in London focuses on challenges of deploying AI agents in production, featuring four tracks on engineering, orchestration, enablement, and governance.
Read more
OpenAI is shutting down self-serve fine-tuning – what this signals for enterprise AI
OpenAI is phasing out self-serve fine-tuning, citing advanced models reducing its necessity, signaling a shift in enterprise AI towards infrastructure challenges.
Read more
What 1,281 agent runs reveal about coding agent failure in large codebases
Sourcegraph's study of 1,281 agent runs in large codebases identifies infrastructure, not model capability, as the main bottleneck, revealing five common failure patterns.
Read more
How enterprises are scaling AI: 5 patterns from OpenAI
OpenAI identifies five patterns for scaling AI in enterprises, focusing on operational integration, governance, and engineering ownership over model capabilities.
Read more
Why multi-agent AI breaks in production — and how Yugabyte's Meko is trying to fix it
Yugabyte's Meko addresses multi-agent AI production issues by providing a shared memory and coordination layer, tackling state synchronization challenges in complex workflows.
Read more
Your benchmarks are lying to you, and your judge is to blame!
Benchmarking AI models with single LLM judges can skew results due to judge bias. Multiple judges reveal score variations, suggesting a need for diverse evaluation methods.
Read more
Get started in seconds
curl -fsSL https://get.tessl.io | shOr explore skills and context in the Registry.
