alirezarezvani--claude-skills/commands/cs-fullstack-review.md

description, argument-hint

description	argument-hint
Fullstack engineering review — walks the 7 Matt Pocock forcing questions, picks the profile, forks into POWERFUL specialists (api-design-reviewer, database-designer, slo-architect). Invokes the cs-fullstack-engineer agent with context fork.	<problem or codebase to review>

/cs:fullstack-review — Fullstack engineering review

Use the cs-fullstack-engineer agent (which uses context: fork to keep the parent thread clean) to handle this inquiry:

$ARGUMENTS

Forcing-question library

Canonical source: engineering-team/skills/senior-fullstack/references/forcing_questions.md (7 questions, one-per-turn, recommendation + canon citation per question).

1. Team size now + 12-month headcount
2. Deployment cadence (per-PR / daily / weekly / quarterly)
3. Customer-facing / internal tool / marketing site
4. One-year p50 + p99 traffic forecast
5. Hiring-against vs training-into the stack
6. Year-one monthly cloud + SaaS budget ceiling
7. Three verifiable success criteria with numeric targets

Routing protocol

1. Walk the 7 forcing questions in engineering-team/skills/senior-fullstack/references/forcing_questions.md. One per turn. Recommend the answer with cited canon. Track in /tmp/fullstack-grill-<date>.md.
2. Surface kill criteria — if any question trips one (e.g., "microservices day 1, team size 3"), STOP and resolve before proceeding.
3. Run the deterministic profile picker:

   python engineering-team/skills/senior-fullstack/scripts/fullstack_decision_engine.py \
     --team-size <N> --team-size-12mo <N12> --cadence <c> \
     --user-facing <true|false> --budget <USD/mo> \
     --traffic-p99-rps <N> --data-sensitivity <tier>
   

4. Surface the matched profile + runner-up tradeoff (if within 15%).
5. Fork into specialists (one at a time, depth-first):
   • api-design-reviewer for API contract
   • database-designer for schema
   • slo-architect for reliability target
   • ci-cd-pipeline-builder for the pipeline
   • performance-profiler for perf baseline
   • cs-karpathy-reviewer before any commit

Output expectations (≤ 200-word digest)

• Matched profile + reason
• Three verifiable success criteria with numeric targets
• Named approver chain
• List of specialists invoked + artifact paths
• Recommended next sub-skill (if any)

Anti-patterns

• ❌ Bundling forcing questions — one per turn.
• ❌ Skipping the kill-criteria check.
• ❌ Reimplementing specialist scope. Fork — don't duplicate.
• ❌ Auto-approving production changes. Always name the human approver.

Customization

Profiles live at engineering-team/skills/senior-fullstack/profiles/. To customize for your org:

1. Copy saas-startup.json (or whichever best fits) to <your-org>.json.
2. Edit constraints, stack_recommendations, success_thresholds, named_approver_chain.
3. The decision engine auto-discovers new profile JSONs.

Related commands

• /cs:frontend-review — frontend-only deep dive
• /cs:backend-review — backend-only deep dive
• /cs:engineer-grill — cross-role 21-question forcing-question runner
• /karpathy-check — Karpathy 4-principle review before commit