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DataTypeScript

Data Analysis

by Prism-Shadow

Data Analysis is a Data skill for Claude Code, published by Prism-Shadow in penguin-harness.

1.5K stars150 forkson Prism-Shadow/penguin-harnessAdded 2026/08/19+21% in starsRepository updated 2026/08/19

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Install in seconds

Install Data Analysis

Copy Data Analysis into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/Prism-Shadow/penguin-harness/tree/main/packages/skills/skills/data-analysis ~/.claude/skills/data-analysis

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/Prism-Shadow/penguin-harness.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: packages/skills/skills/data-analysis/SKILL.md in Prism-Shadow/penguin-harness
Installs to: ~/.claude/skills/data-analysis
Collection: One of 21 skills cataloged from this repository
Category: Data — 668 skills

What Data Analysis does

Data Analysis completes data-analysis tasks with bounded inspection, correct data semantics, native artifact handling, complete delivery, and risk-based verification. Use it when an agent must deliver a requested dataset or report artifact at an exact path with correct grain, units, and format.

Data Analysis is cataloged under Data on DirSkills. Data Analysis comes from a repository tagged agent, agentic-ai, ai, build-tool and claude-code.

Documentation

README

Excerpt

Data Analysis

Deliver the requested result and artifacts. Do not turn the task into a proof exercise or add evidence, reports, explanations, or intermediate files that were not requested.

Before you start

Require a concrete data-analysis task, its available inputs, and the requested deliverable, location, and format. Ask only when missing information prevents a defensible result and would materially change the deliverable; otherwise proceed.

Contract

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Data Analysis

What else does Prism-Shadow publish alongside Data Analysis?
Data Analysis is one of 21 skills that DirSkills catalogs from Prism-Shadow/penguin-harness, the repository it ships in. Its siblings there include Agent Creation, Agent Evaluation and Agent Optimization. Each one is a separate skill with its own page in this directory, installs the same way Data Analysis does, and is maintained by Prism-Shadow in that same repository. The rest of the collection is listed on the Prism-Shadow/penguin-harness page.
How does Data Analysis compare to other Data skills?
Data Analysis ranks #389 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Data Analysis against them. Open each page to compare what they document and how they install.

More from Prism-Shadow/penguin-harness

Data Analysis is one of 21 skills cataloged on DirSkills from Prism-Shadow/penguin-harness.

See all 21 skills →

Agent Creation

Agent Creation turns a user requirement into a working agent configuration by writing AGENTS.md, setting identity metadata, and installing only needed skills. Use it to create or configure an Agent State under the agents directory.

Agent Evaluation

Agent Evaluation runs one specified test agent on one benchmark case exactly once, privately scores the execution, and returns one protocol result. Use it when a run_subagent caller needs a single isolated agent evaluation without managing loops or scoreboards.

Agent Optimization

Agent Optimization improves one Test Agent through an evidence → hypothesis → Candidate → evaluation → accept or rollback loop. It uses a frozen Benchmark, Scoreboard, and public Test Traces as black-box feedback while delegating all evaluation to an agent-evaluation subagent.

AgentHub Models

AgentHub Models calls model APIs through @prismshadow/agenthub for streaming text, image generation, speech synthesis, embeddings, and the supported-model registry with one client. Use it when building AI apps that need a single TypeScript client for multiple model providers.

Benchmark Design

Benchmark Design builds and calibrates a multi-Case capability benchmark for one Test Agent and establishes a traceable Formal Baseline. Use it when you need to design, pilot, and freeze a benchmark without changing the test agent.

Bento Slides

Bento Slides creates and edits Bento presentations — self-contained .bento.html decks whose document is JSON. Use it when the user wants a slide deck or presentation, whether from scratch, from source material, or by improving an existing file.

More Data skills

Data Analysis is one of 668 Data skills cataloged on DirSkills, ranked #389 by stars among them.

Browse all Data skills →

Benchmark Methodology

Benchmark Methodology scores competitors across nine weighted dimensions using explicit 1–5 rubrics and a tension plot. Use it after competitive-platform-analysis to produce comparable, evidence-anchored profile cards for a competitive report.

Jupyter Notebook

Jupyter Notebook provides a stateful Python REPL via a live Jupyter kernel (hamelnb) so variables persist across executions. Use it for iterative data exploration, API inspection, and complex code development.

Solana

Solana queries wallets, token holdings, transactions, NFTs, whale transfers, and network stats on the Solana blockchain with USD pricing via CoinGecko. Use it when you need to inspect an address, verify a transaction, check token metadata or prices, or monitor network health.