name: datastudio-qa description: Use this skill when the user asks to "QA the dashboard", "verify the Looker Studio dashboard", "verify the Data Studio dashboard", "check dashboard numbers against Excel", "audit the dashboard", or "compare dashboard to source data". Provides the workflow for validating a Looker Studio dashboard against a local .xlsx source file using the datastudio CLI. Use this skill any time the user wants to validate, spot-check, or cross-reference a Looker Studio / Data Studio report against a spreadsheet, even if they don't say "QA" explicitly.

Looker / Data Studio QA

The goal is to catch drift between what the dashboard displays and what the source workbook actually contains, both in the default view and under each filter.

Prerequisites

• datastudio CLI installed and on PATH (install once with uv tool install git+https://github.com/spookyuser/datastudio-cli). If datastudio --help fails, stop and ask the user to install it before proceeding.
• uv on PATH (required for the install above). If uv --version fails, stop and ask the user to install it from https://docs.astral.sh/uv/getting-started/installation/ — do not try to install uv yourself or fall back to pip/pipx.
• A source .xlsx file available locally. Ask the user for the filename if you cannot find one in the working directory — .xlsx files are often gitignored.
• A Looker Studio reporting URL that is shared with anonymous viewers.

Do not assume sheet names, column positions, or expected values. Discover them from the workbook and the dashboard on each run.

CLI reference

Run every command as datastudio <command>.

Workflow

1. Connect. datastudio connect "<url>".
2. Baseline. Record, on the landing page with all filters cleared:
   • respondent / row count,
   • headline KPI values (big-number tiles),
   • means of the key numeric columns the dashboard exposes. These are your reference points for detecting drift when filters are applied.
3. Map pages. datastudio pages. Work through each data page.
4. Per page, for each component:
   • datastudio go "<page>"
   • datastudio data --format json — returns every component on the current page in components[]; reach for --component <token> only when a single tile needs re-extraction after a filter change.
   • Derive the expected value from the workbook (see snippet below) and compare.
5. Filter checks. Use filters to discover dimensions, apply one at a time, re-run data, and confirm aggregates move in the same direction the workbook predicts. Then filter reset.
6. Report. Write findings (see format below).

Reading the source workbook

Use openpyxl inline — the correct column layout varies per workbook, so inspect headers first, then compute.

# uv run --with openpyxl python
import openpyxl
wb = openpyxl.load_workbook("<file>.xlsx", data_only=True)
ws = wb["<sheet>"]
headers = [c.value for c in ws[1]]
idx = {h: i for i, h in enumerate(headers)}
rows = [r for r in ws.iter_rows(min_row=2, values_only=True) if any(r)]
# Example: mean of a numeric column, filtered by a demographic column
vals = [r[idx["<metric>"]] for r in rows
        if r[idx["<demographic>"]] == "<value>"
        and r[idx["<metric>"]] is not None]
print(len(vals), sum(vals) / len(vals))

Pull column names from the workbook; do not hard-code them between runs.

Filters and anonymity thresholds

Dashboards commonly suppress segments below a minimum respondent count (n<4 or n<5 is typical) to protect anonymity. If a filter combination returns a row count the dashboard hides or reports as "—", that is expected behavior, not a bug — verify it against the workbook's own count for that segment.

Reporting format

For each page, report:

• Page name
• Components checked (by name or index)
• Discrepancies: dashboard value vs expected workbook value, with the column and filter used to compute the expected value
• Filter behavior: whether applying each filter moved the numbers the way the workbook predicts
• Confidence: exact match, within rounding, or approximate (and why)

Example comparison

Dashboard "Average Engagement" tile reads 3.42 with Region=North applied. Workbook: mean(engagement_score where region == "North") = 3.417 across 128 rows. → Within rounding (tile shows 2 decimals). Report as a pass, not a discrepancy.