Workspace Data Analyst
Loads and inspects CSV files in your workspace, producing quick statistical summaries, insights, and recommended next steps.
An example workspace skill bundled with the VoltAgent framework (CSV analysis), a very short doc (402 characters) demonstrating framework capability, not an independent production skill.
What does this skill do, and when should you use it?
This is a minimal data-analysis skill shipped inside the VoltAgent repository's example workspace (examples/with-workspace). Its entire instruction set is three steps: load the CSV file and inspect headers, summarize totals, averages, and outliers, then provide a short insight summary with recommended next steps. It contains no executable scripts and declares no runtime dependencies — it is a pure instruction-based skill. It bundles a references/schema.md doc and a sample CSV to illustrate expected behavior. It suits users who need a lightweight CSV overview rather than rigorously validated analysis.
Reads CSV files placed in the workspace; inspects and confirms the header structure; computes totals and averages and identifies outliers; produces a short insight summary plus recommended next steps. The skill ships no scripts — the actual reading and computation are performed by whatever agent hosts the skill.
- An analyst receiving a fresh CSV export who wants a quick read on column structure and rough distributions
- An operations person working in Claude Code with business reports who needs an auto-generated summary write-up
- A developer exploring VoltAgent's workspace skill mechanism who wants a simple, real example to test with
- A data beginner wanting a natural-language rough check of totals, averages, and outliers on a small dataset
What are this skill's strengths and limitations?
- Extremely simple: a three-step instruction set that is easy to understand and modify
- Pure instructions with no script dependencies — runs in any Agent Skills-compliant client as-is
- Ships with a schema reference and a sample CSV, lowering the entry barrier
- MIT-licensed, free to reuse and customize
- No executable scripts — all numbers are model-computed with no accuracy guarantee
- No documented limits on input size; behavior on large CSV files is unknown
- No tests, evals, or validation mechanism, so output quality is unevidenced
- The skill is example-grade; the repo documentation offers no usage commitments or support for it specifically
How do you install this skill?
The skill lives at examples/with-workspace/workspace/skills/data-analysis/ in the VoltAgent repo. No dedicated install command for this skill is documented. The general approach is copying the whole data-analysis folder (SKILL.md, references/schema.md, assets/sample.csv) into your Agent Skills-compatible client's skills directory. To obtain the repo, use npm create voltagent-app@latest (from the README quick start) or clone the GitHub repository directly.
How do you use this skill?
Place your target CSV in the workspace and trigger it with natural language, e.g.: "Analyze data.csv in my workspace — give me column summaries and outliers." The agent follows the three-step flow defined in SKILL.md. The source does not document any file-path parameter handling or command substitution syntax.