Docker workbench and Agent Skill for running deterministic evals against agent skills.

Use this repo in two ways:

• Install the skill-optimizer skill/plugin into your agent so it can author and debug eval suites.
• Run the local CLI to execute cases and suites in Docker against OpenRouter models.

Installation differs by agent. The canonical skill is skills/skill-optimizer/SKILL.md; every plugin manifest points at that same file.

Register this repository as a Claude Code plugin marketplace:

/plugin marketplace add fastxyz/skill-optimizer

Then install the plugin:

/plugin install skill-optimizer@skill-optimizer

Register this repository as a Codex plugin marketplace:

codex plugin marketplace add fastxyz/skill-optimizer

Then open the plugin search interface:

/plugins

Select skill-optimizer and install it.

In the Codex app, open Plugins from the sidebar, search for skill-optimizer, and install it from the Coding section.

If it is not listed, install it from Codex CLI first:

codex plugin marketplace add fastxyz/skill-optimizer

Install the skill with the open skills CLI:

npx skills add fastxyz/skill-optimizer --skill skill-optimizer -a cursor -y

Cursor can also import the skill from GitHub via Settings -> Rules -> Project Rules -> Add Rule -> Remote Rule (Github). The Cursor plugin metadata lives at .cursor-plugin/plugin.json.

Tell OpenCode:

Fetch and follow instructions from https://raw.githubusercontent.com/fastxyz/skill-optimizer/refs/heads/main/.opencode/INSTALL.md

Or add the plugin to opencode.json at user or project scope:

{
  "plugin": ["skill-optimizer@git+https://github.com/fastxyz/skill-optimizer.git"]
}

Restart OpenCode. See docs/README.opencode.md for details.

Install the Gemini extension from GitHub:

gemini extensions install https://github.com/fastxyz/skill-optimizer

To update:

gemini extensions update skill-optimizer

If you only want the skill files without plugin metadata, use the open skills CLI:

npx skills add fastxyz/skill-optimizer --skill skill-optimizer -a claude-code -a opencode -a codex -a cursor -y

Requirements:

• Node.js 20+
• Docker
• OPENROUTER_API_KEY for real model runs

Install and build:

npm install
npm run build

Only openrouter/... model refs are supported.

Run the suite against the models listed in suite.yml:

npx tsx src/cli.ts run-suite examples/workbench/pdf/suite.yml --trials 1

Run one case directly:

npx tsx src/cli.ts run-case ./case.yml --model openrouter/google/gemini-2.5-flash

CLI help:

npx tsx src/cli.ts --help
npx tsx src/cli.ts run-case --help
npx tsx src/cli.ts run-suite --help

The workbench gives an agent a skill/reference folder, an isolated /work directory, and deterministic graders. It is designed for evals where success can be verified from files, command logs, SQL, generated artifacts, or other local state.

Core concepts:

• A case is one user-like task plus one or more graders.
• A suite is a matrix of cases and OpenRouter models.
• references/ is copied into /work; this is where the skill under test lives.
• The agent phase sees only /work, not graders, hidden answers, /case, or /results.
• Graders run after the agent with $CASE, $WORK, and $RESULTS available.
• Graders are the acceptance contract. They can inspect workspace files and artifacts, answer.json, trace.jsonl, and result state under $RESULTS.

Read docs/workbench.md for the full model: directory layout, Docker phases, graders, outputs, and debugging.

Tracked examples live under examples/workbench/. The PDF example includes positive PDF extraction/splitting/creation cases and a negative case that checks the agent did not read the PDF skill file for a non-PDF task. The MCP example shows a local calculator server started as a hidden Docker service and exposed through the workbench mcp command.

npx tsx src/cli.ts run-suite examples/workbench/pdf/suite.yml --trials 1
npx tsx src/cli.ts run-suite examples/workbench/mcp/suite.yml --trials 1

npm run typecheck
npm test
npm run build
npx tsx src/cli.ts --help

For Docker runner or image changes:

docker build -t skill-optimizer-workbench:local -f docker/workbench-runner.Dockerfile .

Do not commit .skill-eval/, .results/, .env, or credentials.