Agent skill

Analysis Artifacts

Generate reproducible analysis artifacts — SQL queries, Python visualizations, and summary tables — as you work through a BigQuery data analysis.

MITAuto-check passedDatabases

Install Analysis Artifacts

skills CLI

$ npx skills add warpdotdev/oz-skills --skill analysis-artifacts -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install warpdotdev/oz-skills analysis-artifacts --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/warpdotdev/oz-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/analysis-artifacts .claude/skills/analysis-artifacts && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "analysis-artifacts" agent skill from https://github.com/warpdotdev/oz-skills/tree/main/.agents/skills/analysis-artifacts into .claude/skills/analysis-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-artifacts", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/warpdotdev/oz-skills/tree/main/.agents/skills/analysis-artifacts

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add warpdotdev/oz-skills --skill analysis-artifacts -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install warpdotdev/oz-skills analysis-artifacts --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/warpdotdev/oz-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/analysis-artifacts .agents/skills/analysis-artifacts && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "analysis-artifacts" agent skill from https://github.com/warpdotdev/oz-skills/tree/main/.agents/skills/analysis-artifacts into .agents/skills/analysis-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-artifacts", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add warpdotdev/oz-skills --skill analysis-artifacts -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install warpdotdev/oz-skills analysis-artifacts --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/warpdotdev/oz-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/analysis-artifacts .cursor/skills/analysis-artifacts && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "analysis-artifacts" agent skill from https://github.com/warpdotdev/oz-skills/tree/main/.agents/skills/analysis-artifacts into .cursor/skills/analysis-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-artifacts", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/warpdotdev/oz-skills.git --path .agents/skills/analysis-artifacts

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add warpdotdev/oz-skills --skill analysis-artifacts -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install warpdotdev/oz-skills analysis-artifacts --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/warpdotdev/oz-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/analysis-artifacts .gemini/skills/analysis-artifacts && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "analysis-artifacts" agent skill from https://github.com/warpdotdev/oz-skills/tree/main/.agents/skills/analysis-artifacts into .gemini/skills/analysis-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-artifacts", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install warpdotdev/oz-skills analysis-artifacts

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add warpdotdev/oz-skills --skill analysis-artifacts -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/warpdotdev/oz-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/analysis-artifacts .github/skills/analysis-artifacts && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "analysis-artifacts" agent skill from https://github.com/warpdotdev/oz-skills/tree/main/.agents/skills/analysis-artifacts into .github/skills/analysis-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-artifacts", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add warpdotdev/oz-skills --skill analysis-artifacts -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install warpdotdev/oz-skills analysis-artifacts --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/warpdotdev/oz-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/analysis-artifacts .opencode/skills/analysis-artifacts && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "analysis-artifacts" agent skill from https://github.com/warpdotdev/oz-skills/tree/main/.agents/skills/analysis-artifacts into .opencode/skills/analysis-artifacts/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-artifacts", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: analysis-artifacts
GitHub stars: 825
Token cost: ~1.1k tokens
SKILL.md length: 534 words
Files: 1
Skills in repo: 13
Repo updated: Aug 15, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Generate reproducible analysis artifacts — SQL queries, Python visualizations, and summary tables — as you work through a BigQuery data analysis.

Works in 6 steps: Scaffold the analysis directory → Plan the analysis → Set up the README → …
Asked to conduct a deep dive
SKILL.md covers When to Use, Workflow and Examples
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Analysis Artifacts is an agent skill from warpdotdev/oz-skills. Generate reproducible analysis artifacts — SQL queries, Python visualizations, and summary tables — as you work through a BigQuery data analysis. Use when asked to conduct a deep dive, exploratory analysis, or investigation that goes beyond a simple data lookup.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Databases, covering Data warehousing, SQL and Data analysis. It works with SQL, Google BigQuery and Python. The licence is MIT.

When your agent uses it

Asked to conduct a deep dive
Exploratory analysis
Investigation that goes beyond a simple data lookup

Example prompts

“/analysis-artifacts”

Requirements

Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

Scaffold the analysis directory
Plan the analysis
Set up the README
Create artifacts as you go
Overwriting artifacts
Summarize the analysis

What it can do on your machine

Read from SKILL.md and the folder at commit 6c08c49. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Analysis Artifacts loads about 1.1k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 534 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~70
When it runs · the whole SKILL.md, loaded when a task matches: ~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from warpdotdev/oz-skills at commit 6c08c49, republished under its MIT licence (© warpdotdev). 534 words, ~1,051 tokens.

Download SKILL.mdSave it as .claude/skills/analysis-artifacts/SKILL.md (or your agent's skills folder).

name: analysis-artifacts
description: Generate reproducible analysis artifacts — SQL queries, Python visualizations, and summary tables — as you work through a BigQuery data analysis. Use when asked to conduct a deep dive, exploratory analysis, or investigation that goes beyond a simple data lookup.

Analysis Artifacts

When to Use

When asked to do a "deep dive" or "analysis" on a question with a non-obvious answer
When the analysis requires exploratory querying in BigQuery
When the output should be reproducible and shareable (not just a one-off answer)

Workflow

1. Scaffold the analysis directory

At the start of every analysis:

Create a new directory in the analyses folder, named according to the existing pattern there
Create subdirectories: /assets/queries and /assets/visualizations
Create a README.md at the root of the new directory — this is the main readable document for the analysis

2. Plan the analysis

Always create a plan before starting, whether or not the user asked for one. Steps in the plan should map to the logical sub-questions or sub-areas you've deemed important to explore. Present the plan and wait for a go-ahead before proceeding.

3. Set up the README

Once the plan is approved:

Add a title, author, and date to the top of the README
Add a Problem Statement section summarizing the analysis question and the sub-pieces you'll explore
Add a Cohorts Definition section. This must be extremely explicit about the groups being compared. If comparing two groups (e.g., free vs. paid, new vs. old, before vs. after a milestone), define cohorts in a way that controls for confounding factors. Consider:
- Signup/activation time (as defined by your product — e.g., first login, first meaningful action); this relates to user tenure
- Plan type or subscription tier (e.g., free vs. paid)
- Controlling for observation time window length across cohorts
- Product-specific usage propensity metrics relevant to the analysis question
Once defined, respect these cohort definitions in all queries throughout the analysis.

Show full SKILL.md (262 more words)Show less

4. Create artifacts as you go

For every material step in the analysis:

SQL query artifact: For any BigQuery query that powers a visualization, summary, or key insight, save a .sql file in /assets/queries/ with a descriptive name and a comment block explaining the query's purpose. Only create the file after you're satisfied with the results. Skip trivial or one-off lookup queries.
Visualization or table artifact: For each key insight, assess whether it's best conveyed through a chart or a table. Lean toward visualizations. If a visualization, write a Python script to generate it and save both the script and the output image to /assets/visualizations/ with descriptive names. If a table, save it as a .csv in /assets/visualizations/.

5. Overwriting artifacts

If you need to redo part of the analysis (due to a methodology correction or user feedback), overwrite all associated artifacts:

Replace the .sql query file
Replace the visualization script and regenerate the image
Replace the .csv table file

Note the change to the user when you do this.

6. Summarize the analysis

When the analysis is complete (either at the end of the plan or when the user asks), write the full README:

Summarize each step and sub-question in logical document sections
Be crisp and concise — avoid unnecessary verbosity
Embed saved viz images from /assets/visualizations/ where appropriate
Generate markdown tables from .csv files in /assets/visualizations/
Include a small reference hyperlink to the associated query file in each section
Add a TL;DR section near the top (after Problem Statement, before Cohorts Definition)
Add a Key Takeaways section at the end

Examples

bash

analyses/
└── 2024-01-user-retention/
    ├── README.md
    └── assets/
        ├── queries/
        │   ├── cohort_retention_by_week.sql
        │   └── retention_by_plan_type.sql
        └── visualizations/
            ├── retention_curve.py
            ├── retention_curve.png
            └── plan_type_summary.csv

Files

Just SKILL.md in .agents/skills/analysis-artifacts of warpdotdev/oz-skills.

Open the folder on GitHubat commit 6c08c49

Compare with similar skills

Analysis Artifacts next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Analysis Artifacts compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Analysis Artifacts this skillwarpdotdev/oz-skills	825	—	~1.1k	Automated safety check: Pass	MIT	1 mo ago
Semantic Analystsidequery/sidemantic	129	—	~982	Automated safety check: Pass	AGPL-3.0	yesterday
Google BigqueryLeoYeAI/openclaw-master-skills	2.2k	—	~4.1k	Automated safety check: Pass	MIT	2 mo ago
Chdb SQLvemetric/vemetric	394	1 repos	~1.2k	Automated safety check: Pass	Apache-2.0	yesterday
Analyzing Dataastronomer/agents	451	—	~1.3k	Automated safety check: Pass	Apache-2.0	today
Modelersidequery/sidemantic	129	—	~4.2k	Automated safety check: Pass	Apache-2.0	yesterday

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Questions about Analysis Artifacts

What does Analysis Artifacts do?

Generate reproducible analysis artifacts — SQL queries, Python visualizations, and summary tables — as you work through a BigQuery data analysis. Analysis Artifacts is an agent skill from warpdotdev/oz-skills. Generate reproducible analysis artifacts — SQL queries, Python visualizations, and summary tables — as you work through a BigQuery data analysis.

When should I use Analysis Artifacts?

Analysis Artifacts fits situations like: asked to conduct a deep dive; exploratory analysis; investigation that goes beyond a simple data lookup.

How do I install Analysis Artifacts in Claude Code?

Run `npx skills add warpdotdev/oz-skills --skill analysis-artifacts -a claude-code`. Or copy the skill folder (.agents/skills/analysis-artifacts in warpdotdev/oz-skills) into .claude/skills/analysis-artifacts in your project. Claude Code loads it when a task matches its description.

How do I install Analysis Artifacts in Codex?

Run `npx skills add warpdotdev/oz-skills --skill analysis-artifacts -a codex`. Or copy the skill folder (.agents/skills/analysis-artifacts in warpdotdev/oz-skills) into .agents/skills/analysis-artifacts in your project. Codex loads it when a task matches its description.

Can I use Analysis Artifacts in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add warpdotdev/oz-skills --skill analysis-artifacts -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analysis-artifacts, .gemini/skills/analysis-artifacts, .github/skills/analysis-artifacts and .opencode/skills/analysis-artifacts in your project.

What does Analysis Artifacts need to run?

SKILL.md names no scripts, command-line tools or credentials: Analysis Artifacts is instructions for the agent only. Our summary lists: Python 3.

Does Analysis Artifacts access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Analysis Artifacts safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Analysis Artifacts use?

Analysis Artifacts is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Analysis Artifacts use?

About 1.1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Analysis Artifacts?

Skills that share tags, products or a category with Analysis Artifacts: Semantic Analyst (sidequery/sidemantic, 129 stars), Google Bigquery (LeoYeAI/openclaw-master-skills, 2.2k stars), Chdb SQL (vemetric/vemetric, 394 stars) and Analyzing Data (astronomer/agents, 451 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analysis Artifacts?

warpdotdev (a GitHub organization) maintains it in warpdotdev/oz-skills, which has 825 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on August 15, 2026.

Source: warpdotdev/oz-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Analysis Artifacts

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Analysis Artifacts

When to Use

Workflow

1. Scaffold the analysis directory

2. Plan the analysis

3. Set up the README

4. Create artifacts as you go

5. Overwriting artifacts

6. Summarize the analysis

Examples

Files

Compare with similar skills

Similar skills

More from warpdotdev/oz-skills

Related

Questions about Analysis Artifacts