quality

name	quality
description	AI code quality checks. The Big 5: input validation, edge cases, error handling, duplication, complexity. Triggers: quality, big 5, ai code, review, validate.
allowed-tools	Read, Grep, Bash

AI code is 1.7x buggier. These 5 checks catch 80% of issues. Load this skill for any review, validation, or implementation work. Verbose research: skills/quality/reference/quality-research.md Every endpoint has Zod/Pydantic schema. Parameterized queries only. Handle: null, empty array, zero-length string, timeout, unicode. No empty catch. Errors logged with context. User messages generic. Same logic in 3+ places = extract to utility. Functions under 30 lines. No nested ternaries > 2 levels.

<quick_checks>

# 1. Missing validation
grep -r "req\.body" --include="*.ts" --include="*.js" | grep -v "parse\|validate\|z\." | head -5

# 2. Empty catch blocks
grep -r "catch.*{}" --include="*.ts" --include="*.js" | head -5

# 3. String concat in queries (SQL injection)
grep -rE "SELECT.*\$\{|INSERT.*\$\{" --include="*.ts" --include="*.js" | head -5

</quick_checks>

Any Big 5 violation = NOT READY Fix before commit. No exceptions.

<r_factor> Composite quality score replacing binary pass/fail.

R = (0.20 * test_pass_rate) + (0.20 * acceptance_rate) + (0.15 * scope_accuracy) + (0.15 * security_clean_rate) + (0.15 * budget_compliance) + (0.15 * first_try_rate)

Range: 0.0 to 1.0

thresholds: >= 0.85: production-ready >= 0.70: good (ship with monitoring) >= 0.50: acceptable (ship with caveats) < 0.50: not ready (fix before shipping)

rule: R-factor is informational, not a hard gate. Use thresholds as guidelines. rule: Track R-factor over time to measure improvement, not as a one-time score. reference: skills/quality/reference/quality-research.md (measurement definitions, usage notes) </r_factor> Proactive deviation detection. Don't wait for bugs — detect behavioral anomalies.

cycle: anomaly: deviation from baseline (token spike, unusual files, scope creep) detection: automated on every checkpoint (compare to historical averages) suppression: block progression, alert human, quarantine work recovery: critical = human review required. medium = auto-retry after fix

thresholds: warning: > 1.5x baseline anomaly: > 2x baseline critical: > 3x baseline

rule: anomaly = pause and ask, not auto-abort. rule: build baselines from at least 10 historical data points before enforcing. reference: skills/quality/reference/quality-research.md (baselines, integration points)

<on_complete> agentdb write-end '{"skill":"quality","big5_checked":true,"violations":N,"r_factor":0.00}' </on_complete>