Analyzing Data

Organization

astronomer

analyzing-data

Queries the data warehouse with SQL and answers business questions about data. Use when answering anything that needs warehouse data - counts, metrics, trends, aggregations, joins across tables, data lookups, or ad-hoc SQL analysis (for example "who uses X", "how many Y", "show me Z", "find customers", "what is the count").

Overview

Publisher	astronomer
Repository	`agents`
Skill name	`analyzing-data`
Stars	455
Forks	71
Bundled files	25
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
25 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by astronomer on GitHub. Read the source before you install it.

Installation

Install the Analyzing Data AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/astronomer/agents/tree/main/skills/analyzing-data
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/astronomer/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/skills/analyzing-data .claude/skills/analyzing-data

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Analyzing Data in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Analyzing Data on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Analyzing Data is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Data Analysis

Answer business questions by querying the data warehouse. The kernel auto-starts on first exec call.

All CLI commands below are relative to this skill's directory. Before running any scripts/cli.py command, cd to the directory containing this file.

Workflow

Pattern lookup — Check for a cached query strategy:

bash
uv run scripts/cli.py pattern lookup "<user's question>"

If a pattern exists, follow its strategy. Record the outcome after executing:

bash
uv run scripts/cli.py pattern record <name> --success  # or --failure

Concept lookup — Find known table mappings:

bash
uv run scripts/cli.py concept lookup <concept>

Table discovery — If cache misses, search the codebase (Grep pattern="<concept>" glob="**/*.sql") or query INFORMATION_SCHEMA. See reference/discovery-warehouse.md.

Execute query:

bash
uv run scripts/cli.py exec "df = run_sql('SELECT ...')"
uv run scripts/cli.py exec "print(df)"

Cache learnings — Always cache before presenting results:

bash
# Cache concept → table mapping
uv run scripts/cli.py concept learn <concept> <TABLE> -k <KEY_COL>
# Cache query strategy (if discovery was needed)
uv run scripts/cli.py pattern learn <name> -q "question" -s "step" -t "TABLE" -g "gotcha"

Present findings to user.

Kernel Functions

Function	Returns
`run_sql(query, limit=100)`	Polars DataFrame
`run_sql_pandas(query, limit=100)`	Pandas DataFrame
`run_sql_many(queries, limit=100)`	List of Polars DataFrames (one per query)

pl (Polars) and pd (Pandas) are pre-imported.

Run independent queries together with run_sql_many — they execute concurrently (Snowflake async / connection-pool fan-out) instead of one at a time:

bash
uv run scripts/cli.py exec "dfs = run_sql_many(['SELECT ...', 'SELECT ...']); print(dfs[0])"

run_sql_many is fail-fast: if any query errors, the call raises and the results of the queries that succeeded are discarded. Use separate run_sql calls if you need partial results.

Timeouts: exec waits up to 120s by default, then interrupts the query and returns a "client stopped waiting" message (the query may still finish server-side). Raise it for known long-running queries: uv run scripts/cli.py exec "..." -t 600.

Idle kernel: the kernel self-terminates after 2h idle (preserving state until then). Override with ASTRO_KERNEL_IDLE_TIMEOUT (seconds; 0 disables).

CLI Reference

Kernel

bash
uv run scripts/cli.py warehouse list      # List warehouses
uv run scripts/cli.py start [-w name]     # Start kernel (with optional warehouse)
uv run scripts/cli.py exec "..."          # Execute Python code
uv run scripts/cli.py status              # Kernel status
uv run scripts/cli.py restart             # Restart kernel
uv run scripts/cli.py stop                # Stop kernel
uv run scripts/cli.py install <pkg>       # Install package

Concept Cache

bash
uv run scripts/cli.py concept lookup <name>                     # Look up
uv run scripts/cli.py concept learn <name> <TABLE> -k <KEY_COL> # Learn
uv run scripts/cli.py concept list                               # List all
uv run scripts/cli.py concept import -p /path/to/warehouse.md   # Bulk import

Pattern Cache

bash
uv run scripts/cli.py pattern lookup "question"                                      # Look up
uv run scripts/cli.py pattern learn <name> -q "..." -s "..." -t "TABLE" -g "gotcha"  # Learn
uv run scripts/cli.py pattern record <name> --success                                # Record outcome
uv run scripts/cli.py pattern list                                                   # List all
uv run scripts/cli.py pattern delete <name>                                          # Delete

Table Schema Cache

bash
uv run scripts/cli.py table lookup <TABLE>            # Look up schema
uv run scripts/cli.py table cache <TABLE> -c '[...]'  # Cache schema
uv run scripts/cli.py table list                       # List cached
uv run scripts/cli.py table delete <TABLE>             # Delete

Cache Management

bash
uv run scripts/cli.py cache status                # Stats
uv run scripts/cli.py cache clear [--stale-only]  # Clear

References

reference/discovery-warehouse.md — Large table handling, warehouse exploration, INFORMATION_SCHEMA queries
reference/common-patterns.md — SQL templates for trends, comparisons, top-N, distributions, cohorts

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Analyzing Data AI skill do?

Why use Analyzing Data on TypingMind?

Because you install it once and use it with any model. Analyzing Data is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Analyzing Data in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/astronomer/agents/tree/main/skills/analyzing-data. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Analyzing Data?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Analyzing Data?

As many as you like. As long as a model supports skills, you can use Analyzing Data with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Analyzing Data AI skill free?

Yes. It is published on GitHub by astronomer under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Data Analysis

Workflow

Kernel Functions

CLI Reference

Kernel

Concept Cache

Pattern Cache

Table Schema Cache

Cache Management

References

Bundled files

Frequently asked questions

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