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Get Started Free →Applies Code Complete's QA process design: selects defect-detection techniques by phase, sizes the test suite, and designs review and inspection processes. For QA planning and process design, not active bug investigation (use cc-debugging).
.claude/skills/ryanthedev-cc-quality-practices/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 0% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -19% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -41% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 18% | 0% |
Improving quality reduces development cost. No single defect-detection technique exceeds ~75% effectiveness, so combine techniques — combining nearly doubles detection rates. Mature suites run about 5 dirty tests (error paths, bad data, edge cases) for every 1 clean test (happy path).
For active bug diagnosis (an actual failing test or repro to chase down), hand off: Skill(code-foundations:cc-debugging). This skill is for QA planning and process design.
Shared CC vocabulary and thresholds (cohesion spectrum, coupling, key metrics): Read(${CLAUDE_PLUGIN_ROOT}/references/cc-foundations.md).
Internal quality enables external quality: poor maintainability blocks fixing defects, which degrades reliability.
| Technique | Rate | Notes | |---|---|---| | Formal inspection | 45–70% | Roles, checklists, preparation. Preparation finds ~90% of the defects; the meeting adds ~10% Votta 1991]. | | Pair programming | 40–60% | Real-time review during development. | | Walk-through | 20–40% | Author-led, less structured. | | Code reading | 20–35% | Individual review emphasizing preparation. | | Unit testing | 15–50% | Developer tests of individual components. |
Execute the quality, review, and testing checklists against the code or process. Checklists:
Read(${CLAUDE_SKILL_DIR}/checklists/qa-and-testing.md) — QA plan, inspections, test cases, data-flow.Read(${CLAUDE_SKILL_DIR}/checklists/debugging.md) — finding, fixing, brute-force, general approach.Output one row per item: | Item | Status | Evidence | Location |, status ∈ VIOLATION (fails item) / WARNING (partial) / PASS.
Produce test cases, review procedures, and quality plans.
Test-case generation (output: test-case list):
1 + count(if/while/for/and/or) + case branches (add 1 if no default case).| Need | Method | |---|---| | Highest defect detection (45–70%) | Formal inspection (default) | | Team geographically dispersed | Code reading (individual prep, async-friendly) | | Schedule pressure + quality | Pair programming | | Diverse viewpoints, larger group | Walk-through |
Inspection roles and meeting mechanics apply when designing a review process for a human team (not for solo or agent work):
| After | Next | |---|---| | Defect to fix found | Skill(code-foundations:cc-refactoring-guidance) | | Design issues found | Skill(code-foundations:cc-routine-and-class-design) | | Active bug to diagnose | Skill(code-foundations:cc-debugging) |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 22,119 | 19,976 | -10% | 1 | 1 | 0% | 3,939 | 4,969 | +26% | 0 | 0 | — |
case-02 | fail→pass | 17,655 | 13,607 | -23% | 1 | 1 | 0% | 2,950 | 2,948 | -0% | 0 | 0 | — |
case-03 | pass→pass | 11,173 | 5,032 | -55% | 1 | 1 | 0% | 1,476 | 1,319 | -11% | 0 | 0 | — |
case-04 | pass→pass | 17,295 | 7,293 | -58% | 1 | 1 | 0% | 2,858 | 2,026 | -29% | 0 | 0 | — |
case-05 | pass→fail | 18,434 | 16,927 | -8% | 1 | 1 | 0% | 3,069 | 3,752 | +22% | 0 | 0 | — |
case-06 | pass→pass | 14,280 | 7,549 | -47% | 1 | 1 | 0% | 2,181 | 2,048 | -6% | 0 | 0 | — |
case-07 | pass→pass | 16,161 | 8,823 | -45% | 1 | 1 | 0% | 2,412 | 2,224 | -8% | 0 | 0 | — |
case-08 | pass→pass | 12,395 | 8,369 | -32% | 1 | 1 | 0% | 1,958 | 2,153 | +10% | 0 | 0 | — |
case-09 | pass→pass | 7,490 | 6,060 | -19% | 1 | 1 | 0% | 1,161 | 1,914 | +65% | 0 | 0 | — |
case-10 | fail→fail | 9,935 | 9,089 | -9% | 1 | 1 | 0% | 1,515 | 2,350 | +55% | 0 | 0 | — |
case-11 | fail→pass | 12,336 | 4,299 | -65% | 1 | 1 | 0% | 1,911 | 1,557 | -19% | 0 | 0 | — |
case-12 | fail→pass | 17,161 | 4,680 | -73% | 1 | 1 | 0% | 2,995 | 1,780 | -41% | 0 | 0 | — |
case-13 | pass→pass | 5,452 | 4,334 | -21% | 1 | 1 | 0% | 851 | 1,590 | +87% | 0 | 0 | — |
case-18 | pass→pass | 4,102 | 1,532 | -63% | 1 | 1 | 0% | 572 | 1,164 | +103% | 0 | 0 | — |
case-14 | fail→fail | 20,844 | 11,328 | -46% | 1 | 1 | 0% | 3,271 | 2,493 | -24% | 0 | 0 | — |
case-15 | fail→pass | 15,866 | 15,941 | +0% | 1 | 1 | 0% | 2,352 | 2,786 | +18% | 0 | 0 | — |
case-16 | fail→pass | 16,202 | 6,199 | -62% | 1 | 1 | 0% | 2,390 | 1,903 | -20% | 0 | 0 | — |
case-17 | pass→pass | 15,780 | 8,816 | -44% | 1 | 1 | 0% | 2,424 | 2,321 | -4% | 0 | 0 | — |
case-19 | fail→pass | 9,934 | 4,807 | -52% | 1 | 1 | 0% | 1,641 | 1,732 | +6% | 0 | 0 | — |
case-20 | fail→pass | 11,399 | 4,364 | -62% | 1 | 1 | 0% | 1,675 | 1,676 | +0% | 0 | 0 | — |
case-21 | fail→pass | 19,392 | 7,645 | -61% | 1 | 1 | 0% | 1,482 | 2,130 | +44% | 0 | 0 | — |
case-22 | fail→pass | 7,085 | 3,608 | -49% | 1 | 1 | 0% | 1,012 | 1,509 | +49% | 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. 22 cases were attempted. The headline lift of +41 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is 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.