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Get Started Free →Design and prioritize a regression test suite so changes don't break what worked. Use when asked to plan regression testing, build a regression suite, decide what to re-test after a change, or trim a bloated regression pack. Produces a risk-based regression plan — what to re-test and why, prioritised tiers (smoke → full), automation candidates, and a run strategy per release — so coverage matches risk and the suite stays fast.
.claude/skills/mohitagw15856-regression-test-plan/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -38% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 74% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -4% | 0% |
Regression testing protects what already works — but re-running everything every time is slow and wasteful, and testing too little ships breakage. The answer is risk-based: re-test what changed, what it touches, and what hurts most if it breaks. This skill builds that prioritised plan and a run strategy, so coverage tracks risk and the suite doesn't balloon.
Given "we're shipping a checkout change, what should we regression-test?", produce the plan anyway — infer the impacted areas and a sensible prioritisation, labelling assumptions. Tie scope to change-impact and risk. Never hand back a question instead of a plan.
Ask for these only if they aren't already provided (else infer and label):
1. Impact analysis — what changed, the areas directly and indirectly affected, and the high-risk zones (shared components, recent bugs, complex logic).
2. Prioritised scope — what to re-test, in tiers:
| Tier | When to run | Scope | Why | |---|---|---|---| | Smoke / sanity | every build | critical paths only (login, checkout, save) | fast fail | | Targeted | this change | the changed area + its direct dependencies | change-impact | | Full regression | major release / risky change | broad core coverage | safety net |
3. What to skip (and the risk) — explicitly de-scope low-risk, unchanged areas, and name the residual risk.
4. Automation candidates — which cases are stable, high-value, and repetitive enough to automate first (and which to keep manual).
5. Run strategy — when each tier runs (per-commit / per-release), order (critical first), and the entry/exit criteria for sign-off.
Risk-based regression practice — change-impact analysis, tiered smoke/targeted/full suites, automation prioritisation, and release-fit run strategy.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 50,106 | 28,233 | -44% | 1 | 1 | 0% | 8,183 | 5,108 | -38% | 0 | 0 | — |
case-02 | fail→pass | 37,301 | 31,625 | -15% | 1 | 1 | 0% | 5,594 | 4,319 | -23% | 0 | 0 | — |
case-03 | pass→pass | 34,726 | 27,556 | -21% | 1 | 1 | 0% | 4,757 | 4,595 | -3% | 0 | 0 | — |
case-04 | fail→pass | 15,608 | 20,371 | +31% | 1 | 1 | 0% | 1,847 | 3,211 | +74% | 0 | 0 | — |
case-05 | pass→pass | 21,157 | 25,639 | +21% | 1 | 1 | 0% | 2,470 | 4,005 | +62% | 0 | 0 | — |
case-06 | fail→pass | 26,034 | 21,775 | -16% | 1 | 1 | 0% | 2,825 | 3,299 | +17% | 0 | 0 | — |
case-07 | pass→pass | 14,140 | 15,180 | +7% | 1 | 1 | 0% | 1,443 | 3,222 | +123% | 0 | 0 | — |
case-08 | pass→pass | 24,359 | 21,610 | -11% | 1 | 1 | 0% | 2,850 | 3,284 | +15% | 0 | 0 | — |
case-09 | pass→pass | 18,059 | 24,392 | +35% | 1 | 1 | 0% | 2,387 | 3,302 | +38% | 0 | 0 | — |
case-10 | fail→pass | 21,239 | 20,254 | -5% | 1 | 1 | 0% | 3,068 | 2,955 | -4% | 0 | 0 | — |
case-11 | fail→fail | 16,646 | 17,977 | +8% | 1 | 1 | 0% | 1,803 | 3,446 | +91% | 0 | 0 | — |
case-12 | pass→pass | 24,610 | 18,859 | -23% | 1 | 1 | 0% | 2,693 | 3,664 | +36% | 0 | 0 | — |
case-13 | fail→pass | 23,447 | 21,735 | -7% | 1 | 1 | 0% | 2,301 | 3,267 | +42% | 0 | 0 | — |
case-14 | fail→pass | 24,206 | 25,302 | +5% | 1 | 1 | 0% | 2,568 | 3,815 | +49% | 0 | 0 | — |
case-15 | pass→pass | 23,806 | 19,348 | -19% | 1 | 1 | 0% | 2,700 | 3,457 | +28% | 0 | 0 | — |
case-16 | fail→pass | 28,910 | 23,433 | -19% | 1 | 1 | 0% | 3,151 | 3,362 | +7% | 0 | 0 | — |
case-17 | fail→fail | 20,794 | 25,838 | +24% | 1 | 1 | 0% | 2,367 | 3,909 | +65% | 0 | 0 | — |
case-18 | pass→pass | 28,078 | 26,427 | -6% | 1 | 1 | 0% | 3,230 | 3,616 | +12% | 0 | 0 | — |
case-19 | pass→pass | 20,579 | 25,455 | +24% | 1 | 1 | 0% | 2,461 | 3,558 | +45% | 0 | 0 | — |
case-20 | pass→fail | 17,685 | 22,869 | +29% | 1 | 1 | 0% | 2,688 | 3,957 | +47% | 0 | 0 | — |
case-21 | pass→fail | 23,666 | 29,395 | +24% | 1 | 1 | 0% | 2,638 | 4,638 | +76% | 0 | 0 | — |
case-22 | pass→pass | 56,530 | 38,155 | -33% | 1 | 1 | 0% | 8,244 | 5,721 | -31% | 0 | 0 | — |
case-23 | pass→fail | 33,560 | 33,492 | -0% | 1 | 1 | 0% | 3,743 | 5,196 | +39% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +22 percentage points is the difference between those two pass rates over the 23 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.