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explain-codebase
Scan and explain an entire codebase — structure, entry points, integrations, patterns, and data flow
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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Scan and explain an entire codebase — structure, entry points, integrations, patterns, and data flow
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
PR-focused code review with structured checklist, severity ratings, and actionable feedback
Use when a code review finding needs proof — write a focused test in JavaScript or Go that either confirms the issue is real or exposes it as over-engineering hyperbole. Trigger after code-review or code-review-skill findings are presented and evidence is requested.
Use when reviewing Dependabot alerts, npm audit findings, govulncheck output, or CVE reports on a JavaScript/Node.js or Go project — especially when triaging multiple alerts across direct and transitive dependencies to assess real-world risk and produce a remediation plan.
Produce data-driven software delivery estimates by analyzing historical JIRA tickets, git activity, and engineer track records, then matching the new work against the most similar past tickets. Use this skill whenever the user asks "how long will this take", wants to estimate a piece of work, scope an epic, plan a sprint, or estimate delivery for JIRA stories or a Figma design. Also use whenever the user wants developer-to-work assignment recommendations based on history, wants to optimize an estimate by adding or reallocating engineers, or asks "what's the fastest way to ship this" or "who should work on this". Especially trigger when the user provides JIRA ticket IDs, JIRA story links, or Figma designs together with any indication of a team that will execute the work.
Use when auditing an existing test suite for quality and coverage gaps, evaluating Playwright migration readiness, scoring automation against a world-class e-commerce standard, or guiding the creation of new tests. Applicable to Selenium, WebdriverIO, and Playwright suites.
AI-powered observability with Grafana MCP — translates natural language to metrics, logs, and trace queries to diagnose issues like a senior SRE
| name | explain-codebase |
| description | Scan and explain an entire codebase — structure, entry points, integrations, patterns, and data flow |
Scan and explain an entire codebase so you can understand how it functions, how everything is connected, what it integrates with, and what patterns to be aware of.
Analyze the codebase at $ARGUMENTS (or the current working directory if not specified) and produce a structured overview.
main() functions, index.ts/js, server startup files, CLI entry pointsPresent the findings as a well-organized markdown document with these sections:
## Codebase Overview: [Project Name]
### Summary
[1-3 sentences: what this project does and the core technology]
### Tech Stack
- **Language:** [e.g., TypeScript 5.x]
- **Framework:** [e.g., Next.js 14]
- **Database:** [e.g., PostgreSQL via Prisma]
- **Testing:** [e.g., Jest + Playwright]
- **Build/Deploy:** [e.g., Docker + GitHub Actions]
### Directory Structure
[Annotated top-level tree with purpose of each directory]
### Entry Points
[List of entry points and what each one starts]
### Request/Data Flow
[Step-by-step trace of a typical operation through the system]
### External Integrations
[Table or list of all external services, where they connect, and how]
### Key Patterns
[List of architectural and code patterns with brief explanations]
### Module Dependencies
[Description or diagram of how internal modules relate to each other]
### Things to Watch Out For
[Non-obvious behaviors, gotchas, tech debt, or areas of complexity]