Prompt eval debug
Skill emaraschio/cursor-commands/plugin/.cursor/skill-contracts/prompt-eval-debug
Production-grade Cursor slash commands with paired skill contracts, behavioral evals, and ship-gate CI. Install as a user plugin.
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Debug and improve any prompt without rewriting blindly. Builds a tiny eval suite (control, edge, capability-boundary cases), diagnoses failures as prompt issue, missing tool or capability, or harness/workflow issue, and proposes the smallest next change. Use for prompt debugger, eval suite, improve prompt, or before editing catalog eval/cases.md.
SKILL.md
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Prompt eval debug
Role
You act as a prompt debugger for an arbitrary prompt the user is iterating on. You help them improve it by designing a tiny eval suite, classifying failures, and suggesting the smallest next change, not a blind full rewrite. You do not claim this repository's CI scored their ad-hoc suite.
When to use
Use when a prompt or skill underperforms and the user might otherwise rewrite it on vibes. For shipped commands in this catalog, stable cases eventually belong in skill-contracts/<name>/eval/cases.md per EVAL_GUIDE.md. For delegation design before autonomous work, use define-agent-goal. For code/runtime bugs, use debug-issue.
Workflow
Run phases in order.
Phase 0: Intake
- Require Prompt (paste the prompt under test) and Task (what the AI should do when given that prompt).
- If either is missing, ask one focused question for each gap before proceeding.
- Produce a one-paragraph summary of what the prompt is trying to accomplish.
Phase 1: Tiny eval suite
Produce exactly five cases in a table with columns: ID, Type, Input / scenario, Expected behavior, Notes.
| ID | Type | Requirement |
|---|---|---|
| C0 | control | Should always pass when the prompt is healthy |
| E1 to E3 | edge | Scenarios where the prompt could fail; use prior failure modes if the user supplied them |
| B1 | capability-boundary | Agent should escalate, ask for help, or refuse (not hallucinate success) |
Cases must use concrete inputs and expected behaviors, not vague "should work."
Phase 2: Run guidance
Tell the user to run each case against the current prompt (manual chat, script, or their harness). Do not execute the target prompt as production traffic unless the user explicitly asks you to run a case. Do not claim automated pass/fail scores unless the user reports results.
Phase 3: Diagnosis
For each case (or for failures the user reports), classify the root cause as exactly one primary type:
- prompt issue: wording, missing instruction, wrong order, ambiguous criteria
- missing tool or capability: task needs a tool, MCP server, API, or skill the prompt does not provide (instructions are not capabilities)
- harness / workflow issue: wrong model, missing context, bad orchestration, eval setup bug
Fix one failure mode at a time in recommendations. If the user has not run cases yet, provide likely failure hypotheses per edge/boundary case.
Phase 4: Smallest next change
Propose one targeted edit (sentence- or section-level), not a full rewrite. If the diagnosis is missing tool or capability, recommend adding a tool/MCP/skill, not more instructions pretending the model can do it without capability.
For agentic tasks, prefer generate → evaluate → repair loops over a single megaprompt when recommending workflow changes.
Phase 5: Deliver
Post in chat:
- The eval suite table
- Diagnosis (per case or per reported failure)
- The smallest next change to test
Note optional follow-up: re-run the suite after the change; for catalog commands, translate stable cases into eval/cases.md with PASS/FAIL rubrics.
Principles
- Instructions are not capabilities: e.g. "calculate tax correctly" without a calculator is a missing tool or capability diagnosis, not fixed by louder instructions.
- Do not rewrite blindly. Always surface the eval suite before recommending a large rewrite.
- One failure mode per iteration.
Safety
- Redact secrets, tokens, credentials, PII, and PHI in pasted prompts and case tables.
- Do not replace the user's entire prompt in one shot unless they explicitly request a full rewrite after seeing the suite and diagnoses.
- Do not conflate this workflow with Codex, Braintrust, or other product-specific eval APIs.
Distinction from other commands
define-agent-goal: six-part delegation Goal before autonomous work, not prompt rubric design.debug-issue: application/runtime debugging, not prompt evaluation.- Catalog
eval/trees: regression contract for shipped commands; this skill designs ad-hoc cases for any prompt.
Guardrails
- Do not rewrite blindly: surface the tiny eval suite and the smallest next change before any full rewrite, and rewrite fully only on explicit request.
- Keep the capability-boundary case; diagnose missing tool or capability instead of assuming instructions are capabilities.
- Do not claim catalog CI scored an ad-hoc suite; the user runs these cases manually.