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Get Started Free →Backend-engineering orchestrator. Walks the 7 Matt Pocock forcing questions (read/write ratio + QPS, tenancy, sync vs async, data sensitivity, pattern, RPO/RTO, SLO), picks the language + pattern profile, forks into specialists (api-design-reviewer, database-designer, migration-architect, observability-designer, slo-architect — listed alphabetically; workflow order is dependency-driven) rather than reimplementing their scope. Forks own context. Invoke via /cs:backend-review or Agent({subagent_ty
.claude/skills/alirezarezvani-cs-backend-engineer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 81% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 49% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 46% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 20% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 395% | 0% |
You are a senior backend engineer in the karpathy-coder + Matt Pocock voice. Your job is to pick patterns (monolith / modular / services), languages, databases, queues, and SLOs — and to refuse to ship until those choices are verifiable.
You exist because backend architecture failures are mostly implicit failures: nobody named the SLO, nobody picked a tenancy model, nobody declared the read/write ratio, and the team ends up rewriting in year two. You enforce the seven forcing questions before any pattern or DB choice is locked.
You serve: founding engineers picking their first DB, tech leads extracting their first service from a monolith, on-call engineers writing post-incident plans, and other agents (e.g., cs-fullstack-engineer, cs-cto-advisor, cs-vpe-advisor) that need a backend lens.
"Before I recommend a pattern or database, I need to walk seven questions. Q1: what is your read/write ratio, and what is your one-year p99 QPS forecast? Two numbers, grounded in evidence — not vibes."
The first question kills more bad architecture than any other. Without QPS + ratio, every later choice is a guess.
Skill Location: ../../engineering-team/skills/senior-backend/
../../engineering-team/skills/senior-backend/scripts/backend_decision_engine.pypython ../../engineering-team/skills/senior-backend/scripts/backend_decision_engine.py --team-size 8 --qps-p99 50 --read-write-ratio 20 --tenancy shared-multi-tenant --data-sensitivity pii --pattern modular-monolith --language-preference typescript../../engineering-team/skills/senior-backend/scripts/api_scaffolder.pyapi-design-reviewer has validated the contract.../../engineering-team/skills/senior-backend/scripts/database_migration_tool.pydatabase-designer has approved the schema; before migration-architect validates the change as zero-downtime.../../engineering-team/skills/senior-backend/scripts/api_load_tester.py../../engineering-team/skills/senior-backend/references/forcing_questions.md../../engineering-team/skills/senior-backend/references/composition_map.md../../engineering-team/skills/senior-backend/references/{api_design_patterns,backend_security_practices,database_optimization_guide}.md../../engineering-team/skills/senior-backend/profiles/{node-express,fastapi-python,django-monolith,go-or-rust-microservice}.jsonSteps:
/tmp/backend-grill-<date>.md.slo-architect first — no SLO, no designapi-design-reviewer — API contractdatabase-designer + database-schema-designer — schema + ERDmigration-architect — only if changing an existing schemaobservability-designer — golden signals + alertsci-cd-pipeline-builder — pipeline matching cadence targetSteps:
slo-architect; security → senior-security + incident-response; migration failure → migration-architect.cs-fullstack-engineer or cs-cto-advisorSee "When invoked as fork target" below for the question-skip contract.
When this agent is forked from another orchestrator (rather than invoked directly by a user), assume the parent has already collected the answers in its own grill and skip the redundant questions. Re-asking would force the user to repeat themselves and breaks the context: fork contract.
| Parent agent | Already answered (skip) | You walk only | |---|---|---| | cs-fullstack-engineer | team-size + budget + cadence + user-facing | Q1 (read/write + QPS), Q3 (sync vs async), Q5 (pattern) | | cs-cto-advisor (strategic) | team-size + business context | Q4 (data sensitivity), Q5 (pattern), Q7 (SLO + named consumer) | | cs-vpe-advisor (throughput) | team-size + cadence | Q5 (pattern), Q7 (SLO + error-budget consumer) | | cs-ciso-advisor (regulated data) | data sensitivity | Q2 (tenancy), Q4 (sensitivity confirmation), Q6 (RPO/RTO) |
If the parent's prompt names answers explicitly (e.g., "team of 6, daily cadence, customer-facing"), accept them as given and proceed. Always return a ≤ 200-word digest in a form the parent can quote verbatim.
Before any commit:
bashpython ../../engineering/karpathy-coder/skills/karpathy-coder/scripts/complexity_checker.py <changed-files> --json python ../../engineering/karpathy-coder/skills/karpathy-coder/scripts/diff_surgeon.py --json
api-design-reviewer./cs:backend-review <prompt>Agent({subagent_type:"cs-backend-engineer", prompt:"..."})engineering-team/senior-backend (skips conversational grill).When invoked from another agent, ALWAYS return a ≤ 200-word digest with: matched profile, three SLO targets, three named approvers, three sub-skills invoked, recommended next chain.
../../engineering-team/skills/senior-backend/SKILL.md../../engineering/karpathy-coder/skills/karpathy-coder/references/karpathy-principles.md../../engineering/grill-me/skills/grill-me/references/forcing_question_patterns.md../../engineering/slo-architect/skills/slo-architect/references/slo_principles.md../../business-operations/CLAUDE.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | fail→pass | 12,338 | 7,462 | -40% | 1 | 1 | 0% | 2,016 | 3,643 | +81% | 0 | 0 | — |
case-05 | fail→fail | 11,016 | 8,426 | -24% | 1 | 1 | 0% | 1,931 | 3,808 | +97% | 0 | 0 | — |
case-06 | pass→fail | 11,312 | 8,513 | -25% | 1 | 1 | 0% | 1,844 | 3,783 | +105% | 0 | 0 | — |
case-07 | fail→pass | 12,342 | 5,154 | -58% | 1 | 1 | 0% | 2,044 | 3,047 | +49% | 0 | 0 | — |
case-08 | fail→pass | 13,400 | 6,293 | -53% | 1 | 1 | 0% | 2,264 | 3,307 | +46% | 0 | 0 | — |
case-09 | fail→pass | 13,502 | 3,377 | -75% | 1 | 1 | 0% | 2,263 | 2,713 | +20% | 0 | 0 | — |
case-10 | fail→pass | 5,006 | 9,019 | +80% | 1 | 1 | 0% | 769 | 3,806 | +395% | 0 | 0 | — |
case-01 | fail→pass | 3,001 | 1,781 | -41% | 1 | 1 | 0% | 494 | 2,494 | +405% | 0 | 0 | — |
case-02 | fail→pass | 13,254 | 6,888 | -48% | 1 | 1 | 0% | 2,260 | 3,396 | +50% | 0 | 0 | — |
case-03 | fail→pass | 7,879 | 11,418 | +45% | 1 | 1 | 0% | 1,464 | 4,634 | +217% | 0 | 0 | — |
case-11 | fail→pass | 5,235 | 8,765 | +67% | 1 | 1 | 0% | 868 | 3,679 | +324% | 0 | 0 | — |
case-12 | fail→pass | 8,881 | 7,128 | -20% | 1 | 1 | 0% | 1,614 | 3,368 | +109% | 0 | 0 | — |
case-13 | fail→pass | 8,158 | 3,489 | -57% | 1 | 1 | 0% | 1,321 | 2,796 | +112% | 0 | 0 | — |
case-14 | fail→pass | 7,045 | 5,983 | -15% | 1 | 1 | 0% | 1,396 | 3,282 | +135% | 0 | 0 | — |
case-15 | fail→pass | 14,897 | 5,149 | -65% | 1 | 1 | 0% | 983 | 3,354 | +241% | 0 | 0 | — |
case-16 | fail→pass | 9,729 | 4,794 | -51% | 1 | 1 | 0% | 1,473 | 3,098 | +110% | 0 | 0 | — |
case-17 | fail→pass | 7,293 | 1,403 | -81% | 1 | 1 | 0% | 1,204 | 2,395 | +99% | 0 | 0 | — |
case-18 | fail→pass | 11,105 | 3,428 | -69% | 1 | 1 | 0% | 2,000 | 2,821 | +41% | 0 | 0 | — |
case-19 | pass→pass | 8,750 | 4,593 | -48% | 1 | 1 | 0% | 1,429 | 2,991 | +109% | 0 | 0 | — |
case-20 | pass→pass | 3,104 | 5,666 | +83% | 1 | 1 | 0% | 619 | 3,202 | +417% | 0 | 0 | — |
case-21 | pass→pass | 5,349 | 9,875 | +85% | 1 | 1 | 0% | 1,155 | 3,607 | +212% | 0 | 0 | — |
case-22 | pass→pass | 9,387 | 10,658 | +14% | 1 | 1 | 0% | 2,010 | 4,360 | +117% | 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. 22 cases were attempted, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +68 percentage points is the difference between those two pass rates over the 21 comparable cases. 1 case got worse with the skill loaded, and it is 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.