skills/exploring-autocapture-events/SKILL.md
Guides exploration of $autocapture events captured by posthog-js to understand user interactions, find CSS selectors (especially data-attr attributes), evaluate selector uniqueness, query matching clicks ad-hoc, and create actions. Use when the user asks about autocapture data, wants to find what users are clicking, needs to build actions from click events, asks about elements_chain, wants to build a trend or funnel filtered by clicks or other autocapture interactions, asks which properties autocapture sends, or asks how to filter $autocapture events. Only applies to projects using posthog-js autocapture.
npx skillsauth add posthog/ai-plugin exploring-autocapture-eventsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
if users opt in then posthog-js automatically captures clicks, form submissions, and page changes as $autocapture events.
Each event records the clicked DOM element and its ancestors in the elements_chain column.
$autocapture is intentionally excluded from the posthog:read-data-schema taxonomy
because it is only useful with autocapture-specific filters (selector, tag, text, href).
This skill fills that gap.
The events table provides fast access to common element fields without parsing the full chain string.
| Column | Type | Description |
| ------------------------- | ------------- | ------------------------------------------------------------------------------------------------------ |
| elements_chain | String | Full semicolon-separated element chain (see format reference) |
| elements_chain_href | String | Last href value from the chain |
| elements_chain_texts | Array(String) | All text values from elements |
| elements_chain_ids | Array(String) | All id attribute values |
| elements_chain_elements | Array(String) | Useful tag names: a, button, input, select, textarea, label |
Use materialized columns for exploration queries whenever possible — they avoid regex parsing.
Every $autocapture event from posthog-js ships with a fixed set of properties.
Do not query the schema to "look them up" — they are these:
| Property | Examples | Notes |
| ----------------- | --------------------------------- | ----------------------------------------------------------- |
| $event_type | click, submit, change | the kind of interaction |
| $el_text | Sign up, Submit | text of the clicked element |
| $current_url | https://app.example.com/pricing | page the interaction happened on |
| $elements_chain | semicolon-separated chain | parsed via the elements_chain* materialized columns above |
Standard event properties ($browser, $os, $device_type, etc.) are also present.
Run a count query before doing anything else. If the count is zero, autocapture may be disabled. There are two ways this happens:
autocapture_opt_out in PostHog project settingsinit() call can pass autocapture: falseTell the user if no data is found so they can check both settings.
SELECT count() as cnt
FROM events
WHERE event = '$autocapture'
AND timestamp > now() - INTERVAL 7 DAY
Start broad using the materialized columns. The goal is to understand what users are clicking before narrowing down.
Useful explorations:
elements_chain_elements)elements_chain_texts)elements_chain_href)elements_chain values for a specific page (filtered by properties.$current_url)See example queries for all patterns.
Once the user identifies an interaction they care about, find a CSS selector that identifies it.
Priority order for selector attributes (best first):
data-attr or other data-* attributes — highest specificity, stable across deploys, developer-intended anchors.
Search with match(elements_chain, 'data-attr=') or extractAll.attr_id) — also highly stable, queryable via elements_chain_ids.When a data-attr value is found, construct a selector like [data-attr="value"] or button[data-attr="value"].
A selector is only useful if it matches the intended interaction and not unrelated events.
Run a uniqueness check using elements_chain =~ with the regex pattern for the selector.
Then sample matching events to inspect what the selector actually captures.
Compare the count against total autocapture volume to understand selectivity.
A good selector matches a single logical interaction. If it matches too many distinct elements, refine it in the next step.
If the selector alone is not unique enough, layer on additional filters:
elements_chain_textsproperties.$current_urlelements_chain_hrefRe-run the uniqueness check after each refinement. Only include filters that are needed — fewer filters means more resilience to minor DOM changes.
When the user wants a funnel, trend, or other insight, the filter shape is different from HogQL.
Each step in a FunnelsQuery / TrendsQuery is an EventsNode (or ActionsNode) with event: "$autocapture" and a properties array.
Two distinct property type values matter — they are not interchangeable:
type: "element" — keys: selector, tag_name, text, href. Matched against the parsed elements_chain. Operator support is split:
selector and tag_name only support exact and is_not — anything else raises NotImplementedError in the query compiler (posthog/hogql/property.py).text and href accept the full string operator set (exact, is_not, icontains, not_icontains, regex, not_regex, is_set, is_not_set).type: "event" — keys: any of the canonical autocapture properties ($event_type, $el_text, $current_url) or anything else on the event. Standard event-property operators (exact, icontains, regex, etc.).Example funnel from clicking one button to clicking another:
{
"kind": "FunnelsQuery",
"series": [
{
"kind": "EventsNode",
"event": "$autocapture",
"properties": [
{
"type": "element",
"key": "selector",
"value": ["[data-attr=\"autocapture-series-save-as-action-banner-shown\"]"],
"operator": "exact"
}
]
},
{
"kind": "EventsNode",
"event": "$autocapture",
"properties": [
{
"type": "element",
"key": "selector",
"value": ["[data-attr=\"autocapture-save-as-action\"]"],
"operator": "exact"
}
]
}
]
}
Two things easy to get wrong:
value is an array even when matching a single selector[data-attr="..."] wrapper — it is a CSS selector, not a bare attribute valueDecision rule: prefer an action (ActionsNode referencing an existing action — see Step 8) when the interaction will be referenced more than once; inline type: "element" / type: "event" filters when it's a one-off insight; raw HogQL (Step 7) when joining across events or doing custom aggregations.
The discovered selector can be used directly in HogQL without creating an action.
Trends — count matching clicks over time:
SELECT
toStartOfDay(timestamp) as day,
count() as clicks
FROM events
WHERE event = '$autocapture'
AND timestamp > now() - INTERVAL 14 DAY
AND elements_chain =~ '(^|;)button.*?data-attr="checkout"'
GROUP BY day
ORDER BY day
Funnel — pageview to click conversion:
SELECT
person_id,
first_pageview,
first_click_after
FROM (
SELECT
p.person_id,
p.pageview_time as first_pageview,
min(c.click_time) as first_click_after
FROM (
SELECT person_id, min(timestamp) as pageview_time
FROM events
WHERE event = '$pageview'
AND timestamp > now() - INTERVAL 14 DAY
AND properties.$current_url ILIKE '%/pricing%'
GROUP BY person_id
) p
INNER JOIN (
SELECT person_id, timestamp as click_time
FROM events
WHERE event = '$autocapture'
AND timestamp > now() - INTERVAL 14 DAY
AND elements_chain =~ '(^|;)button.*?data-attr="signup"'
) c ON p.person_id = c.person_id AND c.click_time > p.pageview_time
GROUP BY p.person_id, p.pageview_time
)
For recurring analysis, prefer creating an action (next step) or using posthog:query-trends / posthog:query-funnel with the action.
Actions are the durable version of ad-hoc selector queries.
Once the criteria uniquely identify the interaction, create an action using posthog:action-create.
Construct the step with only the filters needed for uniqueness:
{
"name": "Clicked checkout button",
"steps": [
{
"event": "$autocapture",
"selector": "button[data-attr='checkout']",
"text": "Complete Purchase",
"text_matching": "exact",
"url": "/checkout",
"url_matching": "contains"
}
]
}
Available step fields for $autocapture:
selector — CSS selector (e.g. button[data-attr='checkout'])tag_name — HTML tag name (e.g. button, a, input)text / text_matching — element text (exact, contains, or regex)href / href_matching — link href (exact, contains, or regex)url / url_matching — page URL (exact, contains, or regex)After creation, verify with matchesAction():
SELECT count() as matching_events
FROM events
WHERE matchesAction('Clicked checkout button')
AND timestamp > now() - INTERVAL 7 DAY
$autocapture is high volumeLIMIT generously when sampling elements_chain — the strings can be longelements_chain =~ operator matches CSS selectors as regex internally;
prefer materialized columns when possible for performancetesting
Focused Signals scout for PostHog projects running surveys. Watches active surveys for score regressions (NPS / CSAT / rating drops), response-volume drops, abandonment spikes, and targeting drift, AND aggregates open-text responses into recurring themes the team should know about (clusters of complaints, praise, feature requests). Emits findings only when a theme or anomaly clears the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills. Picked uniformly at random by the coordinator alongside `signals-scout-general` and other specialists.
development
Focused Signals scout for PostHog projects using revenue analytics. Watches the derived revenue product for upstream failures (Stripe sync stalls, capture regressions), config drift (missing subscription property, currency mix surprises, broken Stripe↔person joins, deferred-revenue gaps), and goal-miss escalations. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills. Picked uniformly at random by the coordinator alongside `signals-scout-general` and other specialists.
testing
Focused Signals scout for finding observability gaps in PostHog itself — significant event volumes the team isn't tracking, custom events with no insight or dashboard coverage, insights pointing at events that have stopped firing, dashboards missing related context, critical events with no alerts. Watches the event-stream-vs-saved- inventory delta as the team's product evolves and emits findings recommending new insights, dashboard additions, or alerts when gaps clear the confidence bar. Self-contained peer in the signals-scout-* fleet — picked uniformly at random by the coordinator alongside `signals-scout-general` and other specialists.
testing
Focused Signals scout for PostHog projects using logs. Watches for volume bursts, severity-distribution shifts, service silence, fresh message patterns, and trace-correlated bursts via the logs ingestion pipeline. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills. Picked uniformly at random by the coordinator alongside `signals-scout-general` and other specialists.