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Get Started Free →Pre-production audit that scans a codebase for security, database, deployment, code quality, AI/LLM, dependency, frontend, and observability issues. Intercepts deploy commands and blocks until critical items pass. Stack-agnostic. Use for "run ship gate", "am I ready to ship", "pre-launch audit", "can I deploy", "push to production", "go live checklist", "preflight check". Not for CI/CD setup or infra provisioning.
.claude/skills/alirezarezvani-ship-gate/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-19 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 136% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 138% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -14% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 66% | 0% |
Pre-production audit that scans a codebase and reports pass/fail/manual across 8 categories before anything ships.
When the user says "push to production", "deploy", "ship it", "go live", or similar deploy-intent phrases, do NOT proceed with deployment. Instead:
ago or if code changed since, recommend re-running.
Run these checks in order to identify the project stack:
Framework detection:
package.json exists -> Node.js project
"next" in dependencies -> Next.js
"react" in dependencies -> React (if not Next.js)
"vue" in dependencies -> Vue
"svelte" in dependencies -> Svelte
"astro" in dependencies -> Astro
"express" in dependencies -> Express
"fastify" in dependencies -> Fastify
"hono" in dependencies -> Hono
requirements.txt or pyproject.toml -> Python project
"django" present -> Django
"flask" present -> Flask
"fastapi" present -> FastAPI
go.mod exists -> Go project
Cargo.toml exists -> Rust project
Database detection:
"@supabase/supabase-js" in package.json -> Supabase
supabase/ directory exists -> Supabase
"prisma" in dependencies -> Prisma (check schema for DB type)
"mongoose" in dependencies -> MongoDB
"pg" or "postgres" in dependencies -> PostgreSQL
firebase.json or .firebaserc exists -> Firebase
Deploy target detection:
vercel.json or .vercel/ exists -> Vercel
netlify.toml exists -> Netlify
Dockerfile exists -> Docker/VPS
fly.toml exists -> Fly.io
railway.json exists -> Railway
.platform/applications.yaml -> Platform.sh
Auth detection:
"@clerk" in dependencies -> Clerk
"next-auth" in dependencies -> NextAuth
"@supabase/auth-helpers" in deps -> Supabase Auth
"firebase/auth" in imports -> Firebase Auth
AI/LLM detection:
"openai" in dependencies -> OpenAI
"@anthropic-ai/sdk" in dependencies -> Claude API
"@google/generative-ai" in deps -> GeminiReport detected stack before proceeding. This determines which checks are relevant. Checks tagged with a specific stack in references/checks.md are skipped if that stack is not detected.
Run categories in this order: SEC, DB, CODE, DEP, AI, DEPLOY, FE, OBS. Security and database first because they produce the most critical findings.
For each category, run every auto-scannable check from references/checks.md using the patterns in references/patterns.md.
Report progress after each category completes:
[1/8] Security: 3 FAIL, 12 PASS, 3 SKIP
[2/8] Database: 1 FAIL, 5 PASS, 6 SKIP
...Report results as:
For checks that cannot be automated (backup restore tested, rollback plan exists, staging test passed), present them as a checklist and ask the user to confirm each one.
Classify results into three severities:
no HTTPS, SQL injection vectors, no RLS on Supabase tables)
console.logs in production, no pagination)
no analytics, no SBOM)
Final output:
SHIP GATE REPORT
================
Stack: Next.js + Supabase + Vercel
Scan time: 12s
CRITICAL (3 items, must fix)
FAIL [SEC-01] API key found in src/lib/api.ts:14
FAIL [DB-07] RLS not enabled on "profiles" table
FAIL [SEC-05] No CSRF protection on /api/checkout
HIGH (5 items, should fix)
FAIL [CODE-01] 12 console.log statements in production code
FAIL [CODE-03] Empty catch block in src/utils/auth.ts:45
FAIL [DEP-04] 3 critical npm audit vulnerabilities
FAIL [DEPLOY-05] No rollback plan documented
MANUAL [DEPLOY-06] Staging test not confirmed
ADVISORY (4 items, recommended)
FAIL [FE-01] Missing OG meta tags
FAIL [FE-03] No custom 404 page
PASS [OBS-01] Error monitoring configured
SKIP [AI-01] No AI/LLM usage detected
VERDICT: DO NOT SHIP (3 critical issues)
Fix critical items and re-run.If zero critical items remain, verdict is: CLEAR TO SHIP. If only high items remain, verdict is: SHIP WITH CAUTION (acknowledge risks).
Eight categories, each with a code prefix. Full check details in references/checks.md.
| Prefix | Category | Auto | Manual | Tool | |--------|----------|------|--------|------| | SEC | Security | 15 | 3 | 0 | | DB | Database | 7 | 5 | 0 | | DEPLOY | Deployment | 3 | 8 | 0 | | CODE | Code Quality | 11 | 0 | 1 | | AI | AI/LLM Security | 5 | 3 | 0 | | DEP | Dependencies | 5 | 0 | 1 | | FE | Frontend Quality | 7 | 3 | 0 | | OBS | Observability | 2 | 5 | 0 |
This skill audits. It does not fix. When it finds issues, it reports them with file locations and remediation guidance. The user or another skill (systematic-debugging, backend-patterns, shadcn-stack) handles the fix.
This skill does not:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-19 | fail→pass | 7,653 | 2,641 | -65% | 1 | 1 | 0% | 1,579 | 1,985 | +26% | 0 | 0 | — |
case-13 | fail→pass | 5,691 | 3,331 | -41% | 1 | 1 | 0% | 962 | 2,275 | +136% | 0 | 0 | — |
case-14 | fail→pass | 4,979 | 2,298 | -54% | 1 | 1 | 0% | 855 | 2,035 | +138% | 0 | 0 | — |
case-07 | pass→pass | 15,277 | 14,648 | -4% | 1 | 1 | 0% | 3,373 | 5,106 | +51% | 0 | 0 | — |
case-08 | fail→pass | 13,503 | 9,633 | -29% | 1 | 1 | 0% | 2,571 | 2,210 | -14% | 0 | 0 | — |
case-09 | fail→pass | 9,995 | 7,339 | -27% | 1 | 1 | 0% | 1,836 | 3,041 | +66% | 0 | 0 | — |
case-10 | fail→pass | 8,299 | 4,036 | -51% | 1 | 1 | 0% | 1,548 | 2,463 | +59% | 0 | 0 | — |
case-11 | pass→pass | 12,653 | 7,207 | -43% | 1 | 1 | 0% | 2,127 | 2,847 | +34% | 0 | 0 | — |
case-01 | fail→fail | 14,487 | 11,968 | -17% | 1 | 1 | 0% | 2,027 | 3,242 | +60% | 0 | 0 | — |
case-02 | fail→fail | 17,169 | 4,750 | -72% | 1 | 1 | 0% | 3,470 | 1,866 | -46% | 0 | 0 | — |
case-03 | fail→fail | 12,817 | 3,094 | -76% | 1 | 1 | 0% | 2,263 | 2,113 | -7% | 0 | 0 | — |
case-04 | pass→fail | 10,933 | 2,439 | -78% | 1 | 1 | 0% | 2,312 | 1,872 | -19% | 0 | 0 | — |
case-05 | fail→fail | 9,211 | 10,592 | +15% | 1 | 1 | 0% | 2,041 | 4,013 | +97% | 0 | 0 | — |
case-06 | pass→pass | 9,222 | 8,421 | -9% | 1 | 1 | 0% | 1,878 | 3,173 | +69% | 0 | 0 | — |
case-12 | fail→pass | 5,434 | 2,746 | -49% | 1 | 1 | 0% | 872 | 2,174 | +149% | 0 | 0 | — |
case-15 | pass→pass | 6,071 | 5,899 | -3% | 1 | 1 | 0% | 1,036 | 2,723 | +163% | 0 | 0 | — |
case-16 | pass→pass | 9,736 | 4,249 | -56% | 1 | 1 | 0% | 1,576 | 2,400 | +52% | 0 | 0 | — |
case-17 | pass→pass | 8,281 | 2,921 | -65% | 1 | 1 | 0% | 1,395 | 2,100 | +51% | 0 | 0 | — |
case-18 | fail→pass | 15,017 | 15,134 | +1% | 1 | 1 | 0% | 2,430 | 4,125 | +70% | 0 | 0 | — |
case-20 | fail→pass | 11,640 | 5,748 | -51% | 1 | 1 | 0% | 1,953 | 2,628 | +35% | 0 | 0 | — |
case-21 | fail→pass | 13,807 | 11,685 | -15% | 1 | 1 | 0% | 2,298 | 3,864 | +68% | 0 | 0 | — |
case-22 | fail→pass | 5,504 | 10,601 | +93% | 1 | 1 | 0% | 661 | 3,484 | +427% | 0 | 0 | — |
case-23 | fail→pass | 12,128 | 4,667 | -62% | 1 | 1 | 0% | 2,144 | 2,444 | +14% | 0 | 0 | — |
case-24 | pass→pass | 7,577 | 3,250 | -57% | 1 | 1 | 0% | 1,326 | 2,182 | +65% | 0 | 0 | — |
case-25 | fail→pass | 7,971 | 3,869 | -51% | 1 | 1 | 0% | 1,310 | 2,273 | +74% | 0 | 0 | — |
case-26 | pass→pass | 8,717 | 3,855 | -56% | 1 | 1 | 0% | 1,417 | 2,254 | +59% | 0 | 0 | — |
case-27 | fail→pass | 9,412 | 2,951 | -69% | 1 | 1 | 0% | 1,596 | 2,097 | +31% | 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. 27 cases were attempted. The headline lift of +48 percentage points is the difference between those two pass rates over the 27 comparable cases. 2 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.