wehub-resource-sync
73 lines
3.2 KiB
Markdown
73 lines
3.2 KiB
Markdown
---
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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.
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argument-hint: "<problem or codebase to review>"
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---
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# /cs:fullstack-review — Fullstack engineering review
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Use the `cs-fullstack-engineer` agent (which uses `context: fork` to keep the parent thread clean) to handle this inquiry:
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**$ARGUMENTS**
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## Forcing-question library
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Canonical source: `engineering-team/skills/senior-fullstack/references/forcing_questions.md` (7 questions, one-per-turn, recommendation + canon citation per question).
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1. Team size now + 12-month headcount
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2. Deployment cadence (per-PR / daily / weekly / quarterly)
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3. Customer-facing / internal tool / marketing site
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4. One-year p50 + p99 traffic forecast
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5. Hiring-against vs training-into the stack
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6. Year-one monthly cloud + SaaS budget ceiling
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7. Three verifiable success criteria with numeric targets
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## Routing protocol
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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`.
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2. **Surface kill criteria** — if any question trips one (e.g., "microservices day 1, team size 3"), STOP and resolve before proceeding.
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3. **Run the deterministic profile picker:**
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```bash
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python engineering-team/skills/senior-fullstack/scripts/fullstack_decision_engine.py \
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--team-size <N> --team-size-12mo <N12> --cadence <c> \
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--user-facing <true|false> --budget <USD/mo> \
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--traffic-p99-rps <N> --data-sensitivity <tier>
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```
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4. **Surface the matched profile + runner-up tradeoff** (if within 15%).
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5. **Fork into specialists** (one at a time, depth-first):
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- `api-design-reviewer` for API contract
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- `database-designer` for schema
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- `slo-architect` for reliability target
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- `ci-cd-pipeline-builder` for the pipeline
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- `performance-profiler` for perf baseline
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- `cs-karpathy-reviewer` before any commit
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## Output expectations (≤ 200-word digest)
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- Matched profile + reason
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- Three verifiable success criteria with numeric targets
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- Named approver chain
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- List of specialists invoked + artifact paths
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- Recommended next sub-skill (if any)
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## Anti-patterns
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- ❌ Bundling forcing questions — one per turn.
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- ❌ Skipping the kill-criteria check.
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- ❌ Reimplementing specialist scope. Fork — don't duplicate.
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- ❌ Auto-approving production changes. Always name the human approver.
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## Customization
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Profiles live at `engineering-team/skills/senior-fullstack/profiles/`. To customize for your org:
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1. Copy `saas-startup.json` (or whichever best fits) to `<your-org>.json`.
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2. Edit `constraints`, `stack_recommendations`, `success_thresholds`, `named_approver_chain`.
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3. The decision engine auto-discovers new profile JSONs.
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## Related commands
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- `/cs:frontend-review` — frontend-only deep dive
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- `/cs:backend-review` — backend-only deep dive
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- `/cs:engineer-grill` — cross-role 21-question forcing-question runner
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- `/karpathy-check` — Karpathy 4-principle review before commit
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