adding-warehouse-perso…
Sync columns from a synced data warehouse table onto PostHog person or group properties, so warehouse data becomes usable anywhere person and group properties…
Creates product analytics or SQL-backed box plot insights in PostHog. Use when a user asks to create, build, or save a box plot, visualize a numeric distribution, compare quartiles or medians across dates or groups, or turn SQL results into a box plot. Chooses between a standard
$ npx -y skills add PostHog/ai-plugin --skill creating-box-plot-insights --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/creating-box-plot-insightsContext preview
The summary Claude sees to decide when to auto-load this skill.
Creates product analytics or SQL-backed box plot insights in PostHog. Use when a user asks to create, build, or save a box plot, visualize a numeric distribution, compare quartiles or medians across dates or groups, or turn SQL results into a box plot. Chooses between a standard
name: creating-box-plot-insights description: >- Creates product analytics or SQL-backed box plot insights in PostHog. Use when a user asks to create, build, or save a box plot, visualize a numeric distribution, compare quartiles or medians across dates or groups, or turn SQL results into a box plot. Chooses between a standard Trends box plot and a SQL insight, validates the distribution data, saves the insight, and verifies it.
Box plots need distribution data, not an already-aggregated average or total. Choose the simplest query type that can express the user's question.
Use a **standard product analytics box plot** when all of these are true:
Use a **SQL box plot** when the user needs custom grouping, joins, derived values, or bespoke SQL. Read `querying-posthog-data` before writing HogQL, then use [references/sql-examples.md](references/sql-examples.md) as a starting point.
Do not use SQL only to reproduce a standard Trends query.
1. Identify the event or action and its numeric property. Confirm the property is numeric before saving. 2. Build an `InsightVizNode` whose source is a `TrendsQuery`:
3. Run the query with `posthog:query-trends`. 4. If it returns distribution rows, save it with `posthog:insight-create`. 5. Read it back with `posthog:insight-get` and confirm the property, interval, and display.
A box plot without a numeric `math_property` is invalid. Do not substitute event counts unless counts are the values the user wants to distribute.
The SQL must return one pre-aggregated row for each X-axis and series pair. Calculate the summary in the database. Never calculate percentiles from the limited result rows in the client.
Required numeric roles:
The easiest result shape uses these aliases:
x, series, min, p25, median, mean, p75, max
`x` and `series` are optional:
Validate the HogQL with `posthog:execute-sql` before saving. Check that:
Then save this shape with `posthog:insight-create`:
{
"query": {
"kind": "DataVisualizationNode",
"source": {
"kind": "HogQLQuery",
"query": "<validated HogQL>"
},
"display": "BoxPlot",
"chartSettings": {
"boxPlot": {
"xAxisColumn": "x",
"seriesColumn": "series",
"minColumn": "min",
"p25Column": "p25",
"medianColumn": "median",
"meanColumn": "mean",
"p75Column": "p75",
"maxColumn": "max",
"excludeOutliers": true
}
}
}
}Use the actual aliases when the query uses different names. Do not map the six statistics as six Y-axis series.
1. Read the saved insight with `posthog:insight-get`. 2. Run it with `posthog:insight-query`. 3. Confirm the result still has the expected columns and one row per box. 4. Report the insight link, the numeric value being distributed, and the grouping choices.
If an individual row has a missing or invalid summary, PostHog omits that box while keeping valid boxes visible. Fix the SQL when omitted boxes are not expected.
Official PostHog plugin for AI clients. Access PostHog products directly from your AI coding tool.
Repo: PostHog/ai-plugin
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