Menu

Individual skill

data-querying

General guidance on querying data sources, using existing scripts vs ad-hoc queries, filtering patterns, and generating charts for the analytics app.

templates/analytics/.agents/skills/data-querying/SKILL.md

Read pinned source

Source commit ff01b4009091c2140edb34d6f790ec64e7823356

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Network or broad shell
Last checked
Nov 2, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
7,644
Repository last updated
Nov 7, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Data Querying

The analytics app connects to multiple data sources. This skill covers general patterns for querying data effectively.

## Approach

0. **Use retrieved references first** — data questions may start with a small set of relevant data-dictionary entries and saved dashboard panels in `<resource scope="analytics-catalog">`. Treat them as definitions and query examples, never live results. If they do not fit, call `find-data` before querying; use data-source status when provider availability matters.
1. **Route named account health deliberately** — for a customer/org health, QBR, renewal, contract-utilization, risk, or adoption request, read `account-health` before writing SQL. It adds identity-lock and metric-definition checks that an ordinary lookup does not need.
2. **Read the relevant provider skill first** — check `.agents/skills/<provider>/SKILL.md` for table names, column mappings, auth, and gotchas. For BigQuery, read `.agents/skills/bigquery/SKILL.md` and use `search-bigquery-schema` before guessing table or column names.
3. **Clarify if ambiguous** — if the metric definition, date range, or grain is unclear and a wrong guess would change the numbers, use the `ask-question` clarifying tool (multiple-choice) before querying. Ask at most once per turn; skip it when the dictionary or the user already answered.
4. **Use existing actions or connected provider MCP tools** — call the provider action/tool with structured arguments, then filter or aggregate the returned records in your answer
5. **Write ad-hoc scripts** — if no existing script covers the question, create one in `actions/`
6. **Present data in chat** — don't just say "check the dashboard" — actually query, get the data, and present it. Only present numbers you actually retrieved; never report a value you did not query.