| name | cs-fullstack-review |
| description | 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. |
| argument-hint | <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).
- Team size now + 12-month headcount
- Deployment cadence (per-PR / daily / weekly / quarterly)
- Customer-facing / internal tool / marketing site
- One-year p50 + p99 traffic forecast
- Hiring-against vs training-into the stack
- Year-one monthly cloud + SaaS budget ceiling
- Three verifiable success criteria with numeric targets
Routing protocol
- 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.
- Surface kill criteria — if any question trips one (e.g., "microservices day 1, team size 3"), STOP and resolve before proceeding.
- 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>
- Surface the matched profile + runner-up tradeoff (if within 15%).
- Fork into specialists (one at a time, depth-first):
api-design-reviewer for API contractdatabase-designer for schemaslo-architect for reliability targetci-cd-pipeline-builder for the pipelineperformance-profiler for perf baselinecs-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:
- Copy
saas-startup.json (or whichever best fits) to <your-org>.json.
- Edit
constraints, stack_recommendations, success_thresholds, named_approver_chain.
- 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
