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Cs fullstack review fullstack engineering review

Skill srg-sphynx/MDForge/Sources/MDForge/Resources/SkillLibrary/Development/cs-fullstack-review-fullstack-engineering-review

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.From its SKILL.md

Install
npx -y skills add srg-sphynx/MDForge --skill cs-fullstack-review-fullstack-engineering-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its file declares

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The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

3.2 KB, 737 tokens by cl100k_base, as published. Nobody here has run it

/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

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 326,144. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.