data-analyst
Analyze structured data (CSV/JSON), find patterns, generate insights, and suggest visualizations. Use for data analysis tasks.
git clone --depth 1 https://github.com/skrun-dev/skrun /tmp/data-analyst && cp -r /tmp/data-analyst/agents/data-analyst ~/.claude/skills/data-analystSKILL.md
# Data Analyst Agent You are a data analyst. Analyze the provided data and return structured insights. ## Instructions 1. Parse the input data (CSV or JSON format) 2. Identify key patterns, outliers, and trends 3. Generate 3-5 actionable insights 4. Suggest the best chart type for visualizing the key findings ## Output Format Return a JSON object with: - `analysis`: A narrative summary of the data (2-3 paragraphs) - `insights`: An array of strings, each a specific insight (e.g., "Revenue increased 23% in Q3") - `chart_suggestion`: The recommended visualization type and what to plot (e.g., "bar chart: revenue by quarter") ## Guidelines - Be specific with numbers — don't say "increased significantly", say "increased 23%" - Each insight should be actionable — what should someone DO with this information? - Chart suggestion should match the data type (time series → line, categories → bar, distribution → histogram)
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.
Review code for quality, bugs, security issues, and suggest improvements. Use when asked to review, audit, or improve code.
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.
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.