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Get Started Free →Run a comprehensive code review with 4 specialized reviewers (security, correctness, performance, consistency) in parallel.
.claude/skills/tetherto-review/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-18 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 63% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 34% | 0% |
| case-20 | ✗→✓ | ▲ Improved | -38% | 0% |
| case-21 | ✗→✓ | ▲ Improved | 5% | 0% |
Run a comprehensive code review using 4 specialized review agents: security, correctness, performance, and consistency.
/review # review current branch changes vs main
/review #1561 # review a PR by number
/review branch-name # review a specific branch vs main
/review --only security,correctness # run only specific reviewersmain#<number> or <number>: review a GitHub PR<branch-name>: review a specific branch against main--only <list>: comma-separated list of reviewers to run (security, correctness, performance, consistency). Default: all 4.Parse $ARGUMENTS to determine the review target:
# or is a number → PR review modeExtract --only flag if present to filter which reviewers to launch.
PR review mode:
bashgh pr diff <number> --repo tetherto/qvac gh pr view <number> --repo tetherto/qvac --json title,body,commits
Branch review mode:
bashgit diff main...<branch> git log main..<branch> --oneline
Current branch mode:
bashgit diff main...HEAD git log main..HEAD --oneline
If the diff is empty, report "No changes to review" and stop.
Launch the selected review agents in parallel as sub-agents.
For each agent, set:
subagent_type to the reviewer namemodel: "sonnet" to keep review costs down (Claude Code only — Cursor CLI inherits the parent model)readonly: true (reviewers report only — they must not modify files)prompt with enough context for the sub-agent to work independently (see template below)Agents to launch (all 4 unless --only filters):
Prompt template — adapt [target], [diff-command], and [domain] for each reviewer:
Review the code changes on [target] in repo tetherto/qvac.
To get the diff, run: [diff-command]
Focus only on [domain] issues.
Report each finding with: severity, file path and line, description, impact, and fix recommendation.
If no issues found, report: "No [domain] issues identified."
Do NOT fix code — report findings only.Where [diff-command] is:
gh pr diff <number> --repo tetherto/qvacgit diff main...<branch>git diff main...HEADWhile reviewers run, do a quick check:
.npmrc, .env, or credential files must NOT be in the diffCollect results from all reviewers and present a unified report:
## Code Review: [target]
### Security
[findings or "No issues"]
### Correctness
[findings or "No issues"]
### Performance
[findings or "No issues"]
### Consistency
[findings or "No issues"]
### Summary
- Total findings: X (Y critical, Z warnings)
- Recommendation: [ready to merge / needs fixes / needs discussion]After presenting the report, ask the user:
Found X issues. Want me to fix the actionable ones? (y/n)If the user says yes:
fix: [description]Do NOT fix:
model: "sonnet" on each Agent tool call to keep costs down| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-18 | fail→pass | 7,780 | 8,248 | +6% | 1 | 1 | 0% | 1,178 | 1,545 | +31% | 0 | 0 | — |
case-01 | fail→fail | 9,987 | 10,614 | +6% | 1 | 1 | 0% | 1,050 | 2,372 | +126% | 0 | 0 | — |
case-02 | fail→fail | 6,166 | 5,946 | -4% | 1 | 1 | 0% | 1,015 | 1,495 | +47% | 0 | 0 | — |
case-03 | fail→fail | 8,939 | 3,845 | -57% | 1 | 1 | 0% | 1,407 | 1,327 | -6% | 0 | 0 | — |
case-04 | pass→fail | 5,227 | 3,967 | -24% | 1 | 1 | 0% | 879 | 1,252 | +42% | 0 | 0 | — |
case-05 | pass→pass | 10,864 | 7,240 | -33% | 1 | 1 | 0% | 1,567 | 2,227 | +42% | 0 | 0 | — |
case-06 | pass→fail | 4,888 | 4,771 | -2% | 1 | 1 | 0% | 882 | 1,368 | +55% | 0 | 0 | — |
case-07 | fail→fail | 4,038 | 12,157 | +201% | 1 | 1 | 0% | 592 | 1,477 | +149% | 0 | 0 | — |
case-08 | fail→fail | 6,404 | 2,974 | -54% | 1 | 1 | 0% | 1,060 | 1,528 | +44% | 0 | 0 | — |
case-09 | pass→pass | 5,306 | 2,848 | -46% | 1 | 1 | 0% | 881 | 1,618 | +84% | 0 | 0 | — |
case-10 | fail→pass | 4,946 | 13,604 | +175% | 1 | 1 | 0% | 881 | 1,440 | +63% | 0 | 0 | — |
case-11 | pass→pass | 4,478 | 2,109 | -53% | 1 | 1 | 0% | 750 | 1,435 | +91% | 0 | 0 | — |
case-12 | fail→pass | 7,139 | 4,216 | -41% | 1 | 1 | 0% | 1,109 | 1,482 | +34% | 0 | 0 | — |
case-13 | pass→pass | 10,136 | 4,492 | -56% | 1 | 1 | 0% | 1,601 | 1,765 | +10% | 0 | 0 | — |
case-14 | pass→pass | 8,166 | 2,371 | -71% | 1 | 1 | 0% | 1,311 | 1,462 | +12% | 0 | 0 | — |
case-15 | pass→fail | 5,259 | 1,541 | -71% | 1 | 1 | 0% | 836 | 1,277 | +53% | 0 | 0 | — |
case-16 | pass→pass | 6,413 | 2,869 | -55% | 1 | 1 | 0% | 976 | 1,530 | +57% | 0 | 0 | — |
case-17 | pass→pass | 11,384 | 7,394 | -35% | 1 | 1 | 0% | 1,930 | 2,323 | +20% | 0 | 0 | — |
case-19 | pass→pass | 7,699 | 4,356 | -43% | 1 | 1 | 0% | 1,146 | 1,819 | +59% | 0 | 0 | — |
case-20 | fail→pass | 14,097 | 2,311 | -84% | 1 | 1 | 0% | 2,409 | 1,489 | -38% | 0 | 0 | — |
case-21 | fail→pass | 9,920 | 3,366 | -66% | 1 | 1 | 0% | 1,561 | 1,645 | +5% | 0 | 0 | — |
case-22 | pass→pass | 5,158 | 1,735 | -66% | 1 | 1 | 0% | 771 | 1,386 | +80% | 0 | 0 | — |
case-23 | fail→pass | 15,723 | 8,500 | -46% | 1 | 1 | 0% | 2,709 | 2,096 | -23% | 0 | 0 | — |
case-24 | fail→pass | 6,059 | 1,375 | -77% | 1 | 1 | 0% | 905 | 1,315 | +45% | 0 | 0 | — |
case-25 | pass→pass | 4,350 | 9,335 | +115% | 1 | 1 | 0% | 647 | 1,531 | +137% | 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. 25 cases were attempted, and 20 counted toward the lift figure. The other 5 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +16 percentage points is the difference between those two pass rates over the 20 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.