Skillgrade Grader Authoring

Procedures

Step 1: Identify the Grading Strategy

1. Determine whether the task requires objective verification (deterministic) or qualitative assessment (LLM rubric).
2. For most tasks, combine both: deterministic graders verify outcomes (weight 0.7), LLM rubrics assess approach quality (weight 0.3).

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:

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

1. score (0.0–1.0) and details are required. checks is optional but recommended.
2. Read references/grader-output-schema.md for the full output specification.
3. Use awk for arithmetic in bash scripts — bc is not available in node:20-slim.
4. Reference the grader in eval.yaml:

``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? ``

1. Reference the rubric in eval.yaml:

``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) ``

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

Step 4: Combine Multiple Graders

1. Assign weights to each grader based on importance. Weights are normalized automatically.
2. Final reward is calculated as: Σ (grader_score × weight) / Σ weight.
3. Example configuration:

``yaml graders: - type: deterministic run: bash graders/check.sh weight: 0.7 - type: llm_rubric rubric: rubrics/quality.md weight: 0.3 ``

Step 5: Validate Graders

1. Create a reference solution script that produces the expected output.
2. Run skillgrade --validate to verify graders score the reference solution correctly.
3. Test only deterministic graders: skillgrade --grader=deterministic (skips LLM calls, faster iteration).
4. Test only LLM rubric graders: skillgrade --grader=llm_rubric.
5. Run a specific eval with a specific grader type: skillgrade --eval=my-eval --grader=deterministic.
6. If a grader returns unexpected scores, inspect the script output and adjust scoring logic.

Error Handling

• If a deterministic grader outputs non-JSON, ensure all echo/console.log statements except the final JSON result are redirected to stderr.
• If an LLM rubric grader returns 0.00 with a missing API key message, set the appropriate key for your provider: GEMINIAPIKEY (provider: gemini), ANTHROPICAPIKEY (provider: anthropic), or OPENAIAPIKEY (provider: openai).
• To use a custom/self-hosted LLM endpoint, set ANTHROPICBASEURL (for provider: anthropic) or OPENAIBASEURL (for provider: openai) — e.g. for Ollama or vLLM.
• If scores are inconsistent across trials, reduce rubric ambiguity by adding concrete examples of passing and failing behavior.