Skill: Prompt Test

Objective

Provide before/after evidence that a change to prompts/default.md (or a custom prompt file) actually improves review quality. The skill runs the reviewer twice against the same PR — once with the OLD prompt, once with the NEW — and produces a comparison the user can paste into a PR description.

Without this evidence, prompt changes are opinion, not engineering.

Non-goals

• Does NOT modify the prompt for the user. The user owns the prompt edit; this skill evaluates it.
• Does NOT post comments on the test PR — runs are configured with AIPRRTRACKINGCOMMENT=false and AIPRRCOLLAPSEPREVIOUS=false to avoid disturbing the target PR's conversation.

Inputs

• target_prs — list of PR numbers to test against. For substantive changes use 3–5 covering different change types (feature, bugfix, refactor, docs). For targeted changes one PR is enough.
• repo — <owner>/<name> of the target repo. Defaults to the repo the skill is invoked from.

Pre-flight

# API keys present?
test -n "$ANTHROPIC_API_KEY" || { echo "Set ANTHROPIC_API_KEY"; exit 1; }
test -n "$GITHUB_TOKEN"      || { echo "Set GITHUB_TOKEN"; exit 1; }

# On a branch with the prompt change?
git diff main...HEAD -- prompts/default.md | head -1

If the prompt isn't actually changed in the working tree, ask the user whether they intended to test a different file.

Steps

For each target PR N in target_prs:

1. Capture HEAD SHA of target PR

HEAD_SHA=$(gh api "repos/${REPO}/pulls/${N}" --jq .head.sha)

2. Run with OLD prompt

git stash                       # set aside the new prompt

export AIPRR_PROVIDER=anthropic
export AIPRR_API_KEY=$ANTHROPIC_API_KEY
export AIPRR_GH_TOKEN=$GITHUB_TOKEN
export AIPRR_REPO=$REPO
export AIPRR_PR_NUMBER=$N
export AIPRR_HEAD_SHA=$HEAD_SHA
export AIPRR_BASE_REF=main
export AIPRR_ACTION_PATH=$PWD
export AIPRR_STRICTNESS=lenient
export AIPRR_TRACKING_COMMENT=false       # capture, don't post
export AIPRR_COLLAPSE_PREVIOUS=false       # don't disturb the PR

python3 scripts/reviewer.py 2>&1 | tee "/tmp/old-${N}.log"

Capture from the log:

• The summary (the model's submit_review argument).
• The list of inline comments with severities.
• The number of tool calls.
• The number of turns the loop ran.

3. Run with NEW prompt

git stash pop                  # restore the new prompt

python3 scripts/reviewer.py 2>&1 | tee "/tmp/new-${N}.log"

Same captures.

4. Compare

Produce a per-PR comparison:

Plus a qualitative bullet list:

• "New flags X that old missed." (better)
• "New misses Y that old caught." (regression)
• "Severity of Z shifted from info → warning." (calibration change)

Aggregation across multiple PRs

After running each PR, aggregate:

## Prompt smoke-test summary

Tested against N PRs covering <types>.

### Net findings
- **New catches old missed:** <list with PR links>
- **New misses old caught:** <list with PR links>
- **Calibration shifts:** <summary>

### Verdict
<one sentence: ship / refine / abandon>

The summary belongs in the PR description for the prompt change.

Output

Print the summary to stdout. Optionally write it to tmp/prompt-test-<branch>.md if the user wants to attach a file.

Common failure modes

• Anthropic rate limits. Add time.sleep(5) between PRs if you're testing >5 in a row.
• Stash conflict. If git stash pop fails, the user has uncommitted changes elsewhere; stop and ask.
• Target PR closed. PRs in closed/merged state still work for read-only review; skip if the diff is gone (rare).
• Provider cost. Each run is one full review at the configured model. For 5 PRs × 2 runs = 10 reviews. Estimate cost up front; if that's a problem use a cheaper model via AIPRR_MODEL for the smoke test.