Files
zeekay 1b367e2b62 chore: rebase the tree onto upstream master
The tree was a debranded snapshot of upstream taken around 2026-02-20, and
upstream has moved about five months since. This replaces it with upstream
master, mechanically re-derived: bin/debrand does the rename, and the
pipeline and squashed migrations are restored from the shipped tree.

Not a merge. The previous import replaced the tree wholesale in a single
commit, so there is no shared content history to merge against. Reproducing
the rename on the new tree is what actually converges.

What survives: .hanzo/workflows, the Dockerfiles and bin/docker* entrypoints,
compose files, LLM.md, and insights/migrations. The squashed initial
migration is load-bearing — the live database records it by name, so
upstream's ~918 individual migrations cannot come back.

Nine products are dropped as unreferenced. Twenty-one more that we do not run
are kept anyway because the core app still imports them; unpicking those is a
refactor and does not belong in a rebase.

This base does not build yet, and the reasons are not in the rename:
upstream now wants Python 3.13.13 against our 3.12.12 and Django 5.2 against
our 4.2, and the dependency manifests still name upstream packages that we
republish under our own names. Those are decisions, not renames.
2026-08-01 15:44:40 -07:00

9.8 KiB

name, description
name description
finding-sessions-to-watch Guides a user from "I want to watch recordings but don't know which ones" to a short, high-signal list of sessions worth watching. Use when the user asks which sessions or replays to watch, wants help finding interesting / useful recordings, says they don't know where to start in session replay, or wants to watch sessions about a goal (signup, pricing, onboarding, checkout, a feature, rageclicks, errors, mobile, a specific person) without naming exact filters. Turns a vague intent into a focused RecordingsQuery via `query-session-recordings-list`, then deep-links the best few and hands off to `investigating-replay`. Do NOT use when the user already has a recording/session ID (use investigating-replay) or wants the replay for a known error issue (use finding-replay-for-issue).

Finding sessions to watch

Most people open session replay with a goal ("why are signups dropping?") but no idea which of thousands of recordings to watch. A raw, unfiltered list is the worst possible answer — it buries the useful sessions in noise. Your job is to turn their intent into a focused filter, return a handful of high-signal recordings, and offer to dig into one.

The starting points below are the same ones the product surfaces as "filter templates" — they encode the jobs people actually use replay for. Treat them as a menu, not a script.

The one rule

Never dump an unfiltered recording list. Always either (a) apply a goal-based filter, or (b) sort by a signal (activity, errors) so the first few rows are worth a click. If the user's goal is unclear, ask one short question or offer the menu before querying.

Available tools

Tool Purpose
insights:query-session-recordings-list Find/filter recordings (the workhorse). Returns metadata + id per row.
insights:read-data-schema Confirm real event names, URLs, and property values before filtering.
insights:execute-sql Collect $session_ids for sessions where a specific event happened.
insights:cohorts-list Resolve a cohort name → id when scoping to a user segment.
insights:session-recording-playlist-create Save the resulting filter as a saved filter view (type: 'filters').

Hand off to the investigating-replay skill once the user picks a recording to understand in depth.

Workflow

1. Pin down the goal

Map the request to one of the starting points below. If it's vague ("show me something interesting"), offer 3-4 options rather than guessing, or default to most active sessions (high signal, no setup).

2. Discover before you filter

Event names and URLs vary per project — never assume $pageview paths, a signup_completed event, or a person property exists. Confirm with read-data-schema (event_properties, event_property_values, entity_property_values) before putting a value in a filter. If the needed event/property doesn't exist, say so and suggest the closest available signal.

3. Run a minimal query

Call query-session-recordings-list with only the filters that serve the goal. Recommended settings:

  • set filter_test_accounts: true (the tool defaults to false) to exclude internal users, unless the user is debugging their own session.
  • date_from of -7d to -30d for goal-based searches; -3d for "recent".
  • A deliberate order — activity_score for "interesting", console_error_count for "broken", start_time for "recent".
  • limit: 10 — you want a shortlist, not a dump.

4. Triage and present

Don't relay raw rows. Pick the 3-5 most promising and say why each is worth watching (long active duration, many errors, reached the key page, high activity score). Deep-link each as {insights_base_url}/replay/{id} — never /replay/home?sessionRecordingId={id}. Note total matches so the user knows how much is behind the shortlist.

5. Offer the next step

  • "Want me to walk through one?" → investigating-replay.
  • "Want to keep watching these?" → save it as a saved filter view with session-recording-playlist-create (type: 'filters' — a filter view, not a 'collection', which is for manually curated recordings and can't carry filters).

Starting points → filters

Two filter shapes cover almost everything:

  • Reached a page → recording metric visited_page ({ "type": "recording", "key": "visited_page", "operator": "icontains", "value": "/pricing" }).
  • Did a specific event (signup, search, rageclick, used a feature) → there is no event-name filter on the recordings query, so first collect session IDs with execute-sql, then pass them as session_ids (see the two-step pattern below).
User goal Approach
Signup / onboarding / pricing / checkout friction visited_page icontains the relevant path (confirm the real path first). Order start_time, or console_error_count to surface broken ones.
A specific feature Two-step: execute-sql for $session_ids where the feature event fired, then session_ids. Pair with visited_page if the feature lives on one page.
Rageclicks / frustration Two-step on the $rageclick event → session_ids.
Errors / something broken properties: [{ "type": "recording", "key": "console_error_count", "operator": "gt", "value": 0 }], order console_error_count.
A/B test / feature flag { "type": "flag", "key": "<flag-key>", "operator": "flag_evaluates_to", "value": "<variant or true>" }.
A specific person / segment person_uuid, a person property filter (e.g. email), or a cohort filter (cohorts-list for the id).
Mobile / responsive issues { "type": "event", "key": "$device_type", "operator": "exact", "value": ["Mobile"] }, or { "type": "event", "key": "$screen_width", "operator": "lt", "value": 600 }.
Most active users / "just show me good ones" No filter; order: "activity_score". The reliable default when the user has no specific goal.
Most active pages execute-sql to rank $pageview by URL, then filter recordings by the hottest page's visited_page.

Two-step pattern: "sessions where event X happened"

The recordings query filters by event properties, not event names. To find sessions that contain a particular event, collect the session IDs first:

insights:execute-sql
SELECT $session_id
FROM events
WHERE event = '$rageclick'          -- or your signup/search/feature event (confirm via read-data-schema)
    AND timestamp > now() - INTERVAL 7 DAY
    AND $session_id != ''
GROUP BY $session_id
ORDER BY max(timestamp) DESC         -- recent first: UUIDs aren't time-ordered, so the LIMIT must keep the freshest sessions
LIMIT 100

Then fetch those recordings (some session IDs won't have a recording — that's expected). Pass the same date_from window as the SQL step — with only session_ids, the query falls back to its -3d default and would drop sessions whose event was older than that:

insights:query-session-recordings-list
{ "date_from": "-7d", "session_ids": ["<id1>", "<id2>", "..."] }

Worked example

User: "Why are people bouncing on our pricing page? Show me some sessions."

  1. Goal = pricing-page friction → visited_page approach.
  2. read-data-schema (event_property_values for $pathname) to confirm the path is /pricing.
  3. Query:
insights:query-session-recordings-list
{
  "date_from": "-14d",
  "filter_test_accounts": true,
  "order": "activity_score",
  "limit": 10,
  "properties": [
    { "type": "recording", "key": "visited_page", "operator": "icontains", "value": "/pricing" }
  ]
}
  1. Present the 3-5 most active, each as {base}/replay/{id}, noting which lingered or hit errors.
  2. Offer to investigate the most promising one (investigating-replay) or save it as a saved filter view (type: 'filters').

Tips

  • Prefer one good filter over many — over-filtering returns nothing and reads as "no data".
  • If a query returns zero recordings, widen the date range or loosen the filter before concluding there's nothing to watch; if it's still empty, recordings may not be captured for that flow (point the user to diagnosing-missing-recordings).
  • activity_score is a solid default proxy for "worth watching" when there's no sharper signal — but it rewards raw interaction volume, so prefer a goal-based filter (errors, a key page) when you have one.
  • Keep the shortlist short. The value is in choosing for the user, not handing back the haystack.