How do I install the Cs Fullstack Review skill?

Run npx skillmds add nous-hermeshub/cs-fullstack-review in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Cs Fullstack Review safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Cs Fullstack Review?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Cs Fullstack Review free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Cs Fullstack Review?

nous-hermeshub (@nous-hermeshub) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Cs Fullstack Review by nous-hermeshub

Name: Cs Fullstack Review
Rating: 4.666666666666667 (3 reviews)
Author: nous-hermeshub

Cs Fullstack 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.

nous-hermeshub e34ddfd 3.1 KB Updated Sep 8, 2026 1 repo stars

File contents

/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 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:

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

---name: cs-fullstack-reviewdescription: 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.license: MIT---# /cs:fullstack-review — Fullstack engineering reviewUse the `cs-fullstack-engineer` agent (which uses `context: fork` to keep the parent thread clean) to handle this inquiry:**$ARGUMENTS**## Forcing-question libraryCanonical 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 headcount2. Deployment cadence (per-PR / daily / weekly / quarterly)3. Customer-facing / internal tool / marketing site4. One-year p50 + p99 traffic forecast5. Hiring-against vs training-into the stack6. Year-one monthly cloud + SaaS budget ceiling7. Three verifiable success criteria with numeric targets## Routing protocol1. **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:** ```bash 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.## CustomizationProfiles 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

Frequently asked questions