/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).

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