skills/ship-faster/skills/_archive/review-merge-readiness/SKILL.md
Request/execute structured code review: use after completing important tasks, at end of each execution batch, or before merge. Based on git diff range, compare against plan and requirements, output issue list by Critical/Important/Minor severity, and provide clear verdict on merge readiness. Trigger words: request code review, PR review, merge readiness, production readiness.
npx skillsauth add enuno/claude-command-and-control review-merge-readinessInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Goal: Make code review a repeatable process, not random comments.
Core principle: Review early, review often.
Mandatory:
workflow-execute-plans ends (default 3 tasks per batch)Optional but valuable:
Prefer using "baseline before plan/task started" as BASE_SHA:
# Common approach: use main as baseline
BASE_SHA=$(git merge-base HEAD main 2>/dev/null || git merge-base HEAD master)
HEAD_SHA=$(git rev-parse HEAD)
If you want to review just the most recent commit within a small task:
BASE_SHA=$(git rev-parse HEAD~1)
HEAD_SHA=$(git rev-parse HEAD)
Review must provide:
run_dir/03-plans/features/<feature_slug>-plan.md)git diff --stat "$BASE_SHA..$HEAD_SHA"
git diff "$BASE_SHA..$HEAD_SHA"
Use template: review-merge-readiness/code-reviewer.md, fill in placeholders and execute.
Check each item per template checklist and output same structured result (Strengths + Issues by severity + Verdict).
Output must include:
Each Issue must include:
file:linereview-clean-code: More focused on "maintainability/cleanliness", suitable for deep code smell investigationreview-react-best-practices: More focused on React/Next.js performance patterns (waterfalls/bundle/re-renders). Use when the diff touches React UI, data fetching, or performance-sensitive areas.If you just need a "can we merge?" verdict: use this skill.
If you want a deep health check: additionally invoke review-clean-code.
tools
MemPalace local-first AI memory system. Use when setting up persistent memory for Claude Code sessions, mining project files or conversation transcripts, querying past context, configuring MCP tools, managing the knowledge graph, or troubleshooting palace operations.
tools
LangSmith Python SDK — trace, evaluate, and monitor LLM applications. Covers @traceable decorator, trace context manager, Client API, evaluate() / aevaluate(), comparative evaluation, custom evaluators, dataset management, prompt caching, ASGI middleware, and pytest plugin.
development
LangGraph (Python) — build stateful, controllable agent graphs with checkpointing, streaming, persistence, interrupts, fault tolerance, and durable execution. Covers both Graph API (StateGraph) and Functional API (@entrypoint/@task).
development
LangGraph Graph API (Python) — build explicit DAG agent workflows with StateGraph, typed state, nodes, edges, Command routing, Send fan-out, checkpointers, interrupts, and streaming. Use when you need explicit control flow and graph topology.