What is analyzing-data?

A tool for answering business questions by querying a data warehouse, a central database that stores information from across a company. It uses SQL, the language commonly used to retrieve and combine database data.

Use it to look up data, calculate metrics, aggregate results, join information from multiple tables, and answer ad-hoc business questions.

It helps avoid manually searching tables or writing repeated queries when you need counts, trends, comparisons, or specific records.

analyzing-data — Skill by astronomer

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add skills/astronomer/agents/analyzing-data

Any agent

npx skills add astronomer/agents --skill analyzing-data

Clone the repo

git clone --depth 1 https://github.com/astronomer/agents

Made for: Claude Code, Codex.

Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,228 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00078	$0.01228
Opus 5	$0.00039	$0.00614
Sonnet 5	$0.00016	$0.00246
Haiku 4.5	$0.00008	$0.00123

Measured 2d ago against content hash 27643c365be0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

analyzing-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 21 executable files (scripts/cache.py, scripts/cli.py, scripts/config.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/analyzing-data/SKILL.md · 121 lines

How it starts

The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

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

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

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

Concept lookup — Find known table mappings:

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:

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:

# 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:

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

Read the full file on GitHub · 121 lines

Files

What ships with it

26 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2d ago First seen · 121 lines · 78 tokens per session scan A 27643c365be0

Subscribe to this mod's changes

analyzing-data is a skill published in the GitHub repository astronomer/agents (432 stars, last pushed 15d ago), licensed Apache-2.0. It adds 78 tokens to every session and 1,228 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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