Prompt Regression Tester

A prompt edit that "looks fine on a couple of examples" is the single most common way teams ship a quality regression. The fix is not heroic — it's a fixed eval set, assertions a machine can check, and a baseline you diff against. This skill builds that harness so any prompt change or model migration produces a pass/fail + regression report instead of a vibe.

When to use this skill

• You're about to edit a production prompt and want proof the edit doesn't regress existing behavior.
• You're migrating models (e.g. claude-opus-4-7 → claude-opus-4-8, or onto a new provider) and need to confirm output quality holds.
• A prompt regressed in the past and you want a committed test that would have caught it.
• You're standing up a new prompt and want defensible coverage before it goes live.
• Someone "improved" a prompt and you need to confirm it actually improved rather than traded one failure for another.

Instructions

1. Find the prompt and its call site first. Grep for the system/user prompt text, the model ID (claude-, gpt-, model=, model:), and the SDK call (messages.create, chat.completions, etc.). Pin down exactly what's under test: the prompt string, the model ID, and every generation parameter (effort/temperature/max_tokens/tools). The harness must reproduce that call exactly — a regression test that uses different params than production tests the wrong thing.

2. Assemble a FIXED eval set — and freeze it. Collect 15–40 representative inputs into a version-controlled file (evals/cases.jsonl, one JSON object per line: {id, input, ...}). Include, deliberately: the happy-path cases, the boundary cases, and — most importantly — every input that has previously failed (past bug reports, support tickets, the example that motivated the last prompt edit). The set is an asset; it only grows by deliberate commit, never shrinks or mutates per run.

   [!WARNING] An eval set that changes between runs is not a regression test — it's a fresh experiment each time, and the diff against baseline is meaningless. Do not generate inputs on the fly, sample randomly, or pull "the latest N production logs" at runtime. Commit the inputs.

3. Write checkable assertions per case — prefer deterministic over judged. For each input, specify what "correct" means as machine-checkable expectations, picking the tightest check that fits:

   • exact — output equals a string (classification labels, enum values).
   • contains / not_contains — a required substring is present / a forbidden phrase (a leaked secret, a banned disclaimer, "As an AI") is absent.
   • regex — a format pattern (an order ID shape, a date, a currency string).
   • json_schema — output parses as JSON and validates against a schema (the highest-value check for structured-output prompts; catches missing fields, wrong types, extra keys).
   • structural — list length, required keys present, sorted order, no duplicates.

   Store assertions alongside each case. A single case may carry several. Reserve an LLM-as-judge only for qualities no code can decide — tone, helpfulness, faithfulness to a source, "did it refuse appropriately" — and even then express the judge as a rubric with a discrete verdict (pass/fail or a 1–5 score with a threshold), not a freeform "rate this."

4. Default to the latest, most capable model for both the system-under-test and the judge. Use claude-opus-4-8 (current most-capable Opus; 1M context, adaptive thinking) unless the prompt's production config pins a different model — in which case test that model and add claude-opus-4-8 as a comparison column. For the judge, also default to claude-opus-4-8: a weak judge is a noisy judge. Use adaptive thinking (thinking: {type: "adaptive"}) for the judge; do not send temperature/top_p/budget_tokens (removed on Opus 4.7/4.8 — they 400). Keep the judge model separate and named in the report so a judge swap is never confused with a quality change.

5. Run the set across each variant under test and score every case. A "variant" is a (prompt, model, params) tuple — typically baseline (current production) vs candidate (your edit). Iterate the frozen cases, call the real SDK with the exact production config, run each assertion, and record a per-case result: pass/fail, which assertion failed, and the raw output. Run candidate and baseline in the same invocation so they see identical inputs. Cache or store raw outputs so a re-score (e.g. after fixing an assertion) doesn't require re-generating.

6. Diff against the committed baseline and flag regressions. Snapshot the baseline results to evals/baseline.json (committed). On each run, compare candidate-vs-baseline per case and classify:

   • Regression — passed in baseline, fails now. (The line that blocks the PR.)
   • Fix — failed in baseline, passes now. (The intended win — confirm it's real, not luck.)
   • Unchanged pass / unchanged fail — note but don't alarm.

   [!WARNING] Don't treat "candidate pass-rate ≥ baseline pass-rate" as green. Aggregate rate hides swaps — a candidate can fix two cases and break two others for the same headline number. The per-case regression list is the signal; the aggregate is a summary, not the gate.

7. Make the baseline a deliberate, reviewed artifact. Update evals/baseline.json only via an explicit "accept" step (a --update-baseline flag or a separate command), and commit it in the same PR as the prompt change so a reviewer sees both the new prompt and exactly which case results moved. Never let the harness silently rewrite the baseline on every run — that's how a regression gets absorbed into "the new normal" unnoticed.

   [!NOTE] LLM outputs vary run-to-run even at fixed settings, so a single judged case flipping isn't necessarily a real regression. For judge-scored cases, run N=3 and require a majority verdict before flagging; for deterministic assertions, one failure is one failure — no repetition needed.

Output

The skill produces a committed, runnable harness:

• Layout —

  evals/
    cases.jsonl        # frozen inputs + per-case assertions (version-controlled)
    baseline.json      # accepted baseline results (version-controlled)
    run.(py|ts)        # loads cases, runs each variant, scores, diffs
    schema/*.json      # json_schema assertions, if any
  

• The assertion set — each case lists its checks, e.g.

  {"id": "refund-001", "input": "...", "assert": [
    {"type": "json_schema", "schema": "schema/refund.json"},
    {"type": "not_contains", "value": "As an AI"},
    {"type": "judge", "rubric": "Output politely declines without admitting fault.", "threshold": "pass"}
  ]}
  

• A pass/fail + regression report — printed and written to evals/report.md:

  Variant: candidate (prompt@HEAD, claude-opus-4-8)  vs  baseline (prompt@main, claude-opus-4-8)
  Judge: claude-opus-4-8
  
  42 cases | 39 pass / 3 fail   (baseline: 40 pass / 2 fail)
  
  REGRESSIONS (2)  ← blocks merge
    refund-001   json_schema: missing field "reason"
    tone-014     judge(2/3): newly apologetic where baseline was neutral
  
  FIXES (1)
    parse-007    regex: now matches order-id format
  
  Exit code: 1 (regressions present)
  

A non-zero exit on any regression makes the harness drop straight into CI. Report file paths back as absolute paths so the user can wire it up.