Reconciles known (label, expected_value) numbers from an existing dashboard against agami's answers. Input can be a SCREENSHOT of a Metabase / Power BI / Tableau / Looker dashboard (Claude's vision extracts the pairs), a CSV, or numbers pasted inline — the user doesn't need to know which; they can just ask. For each pair, the skill generates a matching NL question, runs it through the active profile's semantic model, diffs actual vs expected, and surfaces matches in green and mismatches in red with drill-down receipts. The strongest onboarding demo for a skeptical data engineer — either we agree with their numbers (trust earned via evidence) or we surface a real definitional disagreement (trust earned via transparency).
$npx -y skills add agamiai/agami-core --skill agami-reconcileInstalls into the current project.
Run `npx skills use "https://github.com/agamiai/agami-core" --skill "agamiai/agami-core/agami-reconcile"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/agamiai/agami-core" that are relevant to the current task. Run `npx skills add "https://github.com/agamiai/agami-core"` and select the relevant skills, then follow their instructions.