code-review
Review code for quality, bugs, security issues, and suggest improvements. Use when asked to review, audit, or improve code.
git clone --depth 1 https://github.com/skrun-dev/skrun /tmp/code-review && cp -r /tmp/code-review/agents/code-review ~/.claude/skills/code-reviewSKILL.md
# Code Review You are an expert code reviewer. Analyze the provided code and produce a structured review. ## Instructions 1. Read the code carefully 2. Identify bugs, security issues, and code smells 3. Evaluate code quality on a scale of 0-100 4. Provide specific, actionable suggestions for improvement 5. Be constructive — explain WHY something is an issue, not just WHAT ## Output Format Return a JSON object with: - `review`: A summary of the code quality (2-3 sentences) - `issues`: An array of objects, each with `severity` (critical/warning/info), `line` (if applicable), and `description` - `score`: A number 0-100 representing overall code quality ## Guidelines - Score 90-100: Production-ready, minor style suggestions only - Score 70-89: Good code with some improvements needed - Score 50-69: Functional but has significant issues - Score below 50: Needs major rework ## Examples Input: A function with no error handling Output: Score ~60, issues include "No error handling for edge cases", "Missing input validation"
Generate a numbered Architecture Decision Record (ADR) following the standard nygard/MADR convention. Reads the target ADR directory to compute the next number and to surface candidates for cross-linking. Use when asked to document an architectural decision, draft an ADR, or capture a technical choice with its rationale.
Generate a polished CHANGELOG.md and release-notes.md from a local git repository (or a captured `.git-log.txt` dump). Groups commits by Conventional Commit type, writes both artifacts to the run output directory. Use when asked to draft release notes, summarize commits between tags, or produce a human-readable changelog.
Turn a CSV of operational data (sales, usage, signups, support tickets) into a multi-page styled PDF executive report with narrative + matplotlib charts. The LLM analyzes the data, picks what's interesting, writes the prose, and emits a structured render request that becomes a polished PDF. Use when given a CSV and asked for a report, summary, or analysis.
Analyze structured data (CSV/JSON), find patterns, generate insights, and suggest visualizations. Use for data analysis tasks.
Draft professional emails based on context, tone, and recipient. Use for composing business emails.
Turn a folder of Markdown notes (Obsidian vault, Notion export, plain repo docs) into a navigable static HTML knowledge base bundled as a single .zip file. Maintains a persistent concept graph across runs — concepts that appear in multiple runs gain prominence, and the index becomes denser over time. Use when given a Markdown vault and asked to publish, share, or render it as a browsable site.
Listen to a meeting recording and extract structured action items, decisions, and open questions. Maintains a persistent ledger across runs — previously-open actions are auto-resolved when mentioned as done in subsequent meetings. Outputs `actions.csv` (importable to Linear/Asana/Notion) + `recap.md` (paste into Slack). Use when given a meeting recording and asked for a recap or action items.
Read a PDF directly with vision and extract text, summarize, or analyze its structure. Use when the user passes a PDF file.