Tessl

Tessl is an AI developer platform and agent orchestration harness built to develop, evaluate, and optimize agent skills and coding workflows. It provides a structured CLI environment for managing repository skills, generating test scenarios, and systematically benchmarking agent performance against standardized developer tasks. In daily use, Tessl operates through the `tessl` CLI and integrates directly with agent runtimes such as Claude Code. Users can install skill packages, run static reviews of instruction files (`SKILL.md`), automatically generate multi-scenario evaluation suites, and execute differential evaluations with and without the skill active using models like Claude 3.7 Sonnet, Opus, or Haiku. Unlike traditional CLI agents that focus solely on executing single-turn code generation tasks, Tessl functions as an agent evaluation harness. It tests whether an agent correctly routes to a skill via activation evals, scores output deltas using LLM-as-a-judge rubrics, and categorizes findings into a four-bucket framework to pinpoint performance gaps, redundant context, and instruction-induced regressions.

Tags: ai agent, cli, code assistant, coding agent, developer tools, Generative AI

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Key features

  • Static Skill Review — Validates SKILL.md files against completeness, actionability, conciseness, workflow clarity, and trigger term conflict risks. - Automated Scenario Generation — Generates realistic, scoped real-world task scenarios from skill specifications with tessl scenario generate. - Differential Baseline Evals — Runs tasks with and without skill context to measure precise score deltas and isolate net performance improvements. - Skill Activation Routing Testing — Identifies whether agents route to the intended skill using --solver=activation and suggests description rewrites for routing failures. - Four-Bucket Diagnostic Framework — Categorizes eval criteria into Working Well, Plugin Gap, Redundant, and Regression to streamline prompt and instruction optimization. - Multi-Model Agent Execution — Evaluates skill performance across specific target LLM backends including Claude Sonnet, Opus, and Haiku.

Use cases

Use Case: Benchmarking AI Skill Effectiveness Developers can measure whether custom framework rules and best practices actually improve agent code quality by running judge-scored differential evaluations against baseline model behavior.

Use Case: Fixing Agent Skill Routing Teams maintaining multi-skill repositories can run activation evaluations to detect when an agent fails to invoke a skill, generating refined description triggers to ensure accurate routing.

Use Case: Eliminating Context Bloat and Regressions Maintainers can identify redundant instructions that consume context without improving performance, as well as isolate contradictory examples in documentation that degrade model execution.

Who it is for

Software engineers, platform teams, and AI tool builders developing and distributing skills, plugins, and custom instructions for AI coding agents. It is not intended for developers looking for a basic interactive terminal chat tool without evaluation and skill management needs.

Install guide

Installation Install the skill-optimizer package into your skills project using the Tessl CLI: bash tessl i tessl-labs/skill-optimizer

First Run Ensure your Tessl CLI is authenticated and configured with your project environment: bash tessl login

Example Session Statically analyze a skill file, test activation routing, generate test scenarios, and run an evaluation against Claude: bash # Run static analysis on skill instructions tessl skill review skills/fastify/SKILL.md # Test if the agent routes to the correct skill tessl eval run . --solver=activation # Generate 5 test scenarios and benchmark with Claude Sonnet tessl scenario generate . --count=5 tessl eval run . --agent=claude:claude-sonnet-4-6

Part of CLI Coding Agents

Categories: AI Coding Agents, Code Assistants, Developer Tools

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