skillgrade-graders

Skillgrade Grader Authoring

Procedures

Step 1: Identify the Grading Strategy

1. Determine which grader types the task needs:
   • Deterministic: objective verification — file checks, output comparison, test execution.
   • LLM rubric: qualitative assessment — workflow compliance, code quality, reasoning.
   • Human: subjective judgment — design fidelity, UX, documentation clarity.
2. For most tasks, combine deterministic (weight 0.7) and LLM rubric (weight 0.3).
3. Add a human grader when automated checks can't reliably measure quality.

Step 2: Write a Deterministic Grader

1. Create a script in the skill's graders/ directory (bash or TypeScript).
2. The script must output a JSON object to stdout with the following structure:

   {"score": 0.67, "details": "2/3 checks passed", "checks": [{"name": "check-name", "passed": true, "message": "Description"}]}
   

3. score (0.0–1.0) and details are required. checks is optional but recommended.
4. Read references/grader-output-schema.md for the full output specification.
5. Use awk for arithmetic in bash scripts — bc is not available in node:20-slim.
6. Access task context via SKILLGRADE_INPUT env var (JSON with task, trial, expected, metadata).
7. Reference the grader in eval.yaml:

   - type: deterministic
     run: bash graders/check.sh
     weight: 0.7
   

Step 3: Write an LLM Rubric Grader

1. Draft a rubric with explicit scoring criteria and point allocations.
2. Structure the rubric into weighted sections that sum to 1.0:

   Workflow Compliance (0-0.5):
   - Did the agent follow the mandatory workflow steps?
   Efficiency (0-0.5):
   - Completed in ≤5 commands without trial-and-error?
   

3. Reference the rubric in eval.yaml:

   - type: llm_rubric
     rubric: |
       [rubric text or file path]
     weight: 0.3
     provider: gemini               # optional: gemini (default) | anthropic | openai
     model: gemini-3.5-flash        # optional model override (defaults to the latest dynamically resolved flash model)
   

4. For long rubrics, store in a separate file and reference by path: rubric: rubrics/quality.md.