Skillgrade Evaluation Setup

Procedures

Step 1: Install Skillgrade

1. Verify Node.js 20+ and Docker are available.
2. Run npm i -g skillgrade to install the CLI globally.

Step 2: Initialize an Eval Configuration

1. Navigate to the skill directory (must contain a SKILL.md).
2. Set the appropriate API key environment variable (GEMINI_API_KEY, ANTHROPIC_API_KEY, or OPENAI_API_KEY).
3. Run skillgrade init to generate an eval.yaml with AI-powered tasks and graders.
4. If an eval.yaml already exists, pass --force to overwrite: skillgrade init --force.
5. Without an API key, a well-commented template is generated instead.

Step 3: Configure eval.yaml

1. Read references/eval-yaml-spec.md for the full configuration schema.
2. Define one or more tasks under the tasks: key. Each task requires:

• name: unique task identifier
• instruction: what the agent should accomplish
• workspace: files to copy into the evaluation container
• graders: one or more scoring mechanisms (see the skillgrade-graders skill)

1. Optionally configure defaults: for agent, provider, trials, timeout, and threshold.

Step 4: Run Evaluations

1. Select an appropriate preset based on the evaluation goal:

• --smoke (5 trials): Quick capability check.
• --reliable (15 trials): Reliable pass rate estimate.
• --regression (30 trials): High-confidence regression detection.

1. Run the evaluation: skillgrade --smoke.
2. Run a specific eval by name: skillgrade --eval=fix-linting.
3. Run multiple evals: skillgrade --eval=fix-linting,write-tests.
4. Run only deterministic graders (skip LLM calls): skillgrade --grader=deterministic.
5. Run only LLM rubric graders: skillgrade --grader=llm_rubric.
6. The agent is auto-detected from the API key. Override with --agent=gemini|claude|codex|acp|opencode|command.
7. For ACP, pass --acp-command="gemini --acp" or set defaults.acp.command.
8. For OpenCode, pass --opencode-agent=build|plan|explore or --opencode-model=provider/model.
9. For a custom agent, pass --agent=command --command="node mycli.js" or set defaults.command. The instruction is piped to the command's stdin.
10. Override the provider with --provider=docker|local.

Step 5: Review Results

1. Run skillgrade preview for a CLI report.
2. Run skillgrade preview browser to open the web UI at http://localhost:3847.
3. Reports are saved to $TMPDIR/skillgrade/<skill-name>/results/. Override with --output=DIR.

Step 6: Integrate with CI

1. Add a GitHub Actions step that installs skillgrade, navigates to the skill directory, and runs with --regression --ci --provider=local.
2. Use --provider=local in CI — the runner is already an ephemeral sandbox, so Docker adds overhead without benefit.
3. The --ci flag causes a non-zero exit code if the pass rate falls below --threshold (default: 0.8).
4. Read references/ci-example.md for a complete workflow template.

Error Handling

• If skillgrade init fails with "No SKILL.md found," verify the current directory contains a valid SKILL.md file.
• If evaluation hangs, check Docker is running and the container has network access for API calls.
• If all trials fail with "No API key," ensure the environment variable is exported, not just set inline for a different command.