Score assistant responses for accuracy on a strict 1-5 scale, then return strict JSON only with score, rationale, and improvement suggestions. Use when the user asks to evaluate accuracy, grade accuracy, or critique factual correctness.
Resources
1Install
npx skillscat add whitespectre/ai-assistant-evals/eval-accuracy Install via the SkillsCat registry.
Eval Accuracy
Use this skill to evaluate how factually accurate an assistant response is.
Inputs
Require:
- The assistant response text to evaluate.
Internal Rubric (1–5)
5 = Factually correct, no misleading claims, no hallucinations, claims are well-supported or appropriately qualified
4 = Mostly correct, minor imprecision that does not materially affect meaning
3 = Partially correct, contains one significant inaccuracy or unsupported claim
2 = Multiple inaccuracies or misleading statements
1 = Fundamentally incorrect, fabricated, or contradicts known facts
Workflow
- Evaluate factual claims in the response.
- Compare them against widely accepted knowledge.
- Score accuracy on a 1-5 integer scale using the rubric only.
- Write concise rationale tied directly to rubric criteria.
- Produce actionable suggestions that improve factual correctness.
Output Contract
Return JSON only. Do not include markdown, backticks, prose, or extra keys.
Use exactly this schema:
{
"dimension": "accuracy",
"score": 1,
"rationale": "...",
"improvement_suggestions": [
"..."
]
}
Hard Rules
dimensionmust always equal"accuracy".scoremust be an integer from 1 to 5.rationalemust be concise (max 3 sentences).- Do not include step-by-step reasoning.
improvement_suggestionsmust be a non-empty array of concrete edits.- Never output text outside the JSON object.