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

8.8 KiB

name, description
name description
creating-ai-subscription Create a recurring AI-generated Insights report — schedule a free-text prompt to run on a cron, with the LLM-synthesized markdown delivered to email or Slack on each tick. Use when the user wants a recurring AI summary of X on any cadence (daily, weekly, monthly, yearly) rather than a one-off report. (To attach an AI summary to an existing insight/dashboard subscription instead of a free-text prompt, see `managing-subscriptions` and its `summary_enabled` option.)

Creating a prompt subscription

When to use this

A subscription delivers a Insights report to email or Slack on a recurring schedule. There are three kinds, distinguished by which field you set — the kind is derived and returned as the read-only resource_type:

  • insight — periodic snapshots of one existing insight (resource_type: "insight")
  • dashboard — periodic snapshots of a dashboard's tiles (resource_type: "dashboard")
  • prompt — a recurring AI-generated report from a free-text prompt: an LLM plans and runs InsightsQL over the project's data and synthesizes a fresh markdown report each tick (resource_type: "ai_prompt")

Use this skill for the prompt kind — i.e. when the user wants a recurring AI summary of X (on any cadence — daily, weekly, monthly, yearly) rather than a recurring snapshot of one existing insight/dashboard, or a single one-off report. Pick a prompt subscription when the value is the analysis itself (the LLM deciding what to query and writing it up), not a fixed chart they already built. For an insight/dashboard subscription, set insight/dashboard instead of prompt and the AI gates below don't apply.

This skill covers creating the subscription. Once it exists you manage its lifecycle with the same subscriptions-* tools (see below): list it, edit/disable/ re-enable it, send a test delivery, or delete it.

Tools

Tool Purpose
insights:subscriptions-create Create the recurring prompt subscription
insights:subscriptions-list Confirm it landed; inspect existing subscriptions
insights:subscriptions-partial-update Edit, disable (enabled: false), or re-enable it
insights:subscriptions-test-delivery-create Send an immediate test delivery to its target(s)
insights:subscriptions-delete Soft-delete it (stops all future deliveries)
insights:integrations-list Find a Slack integration_id (filter kind=slack)
insights:integrations-channels-retrieve List a Slack integration's channels (id + name)

What you need before calling

The endpoint enforces three create-time gates and will return 400 if any fails:

  1. Insights Cloud, or DEBUG=true — self-hosted production deployments are not eligible (the LLM call routes through a Insights-managed key).
  2. Org-level "AI data processing approved" — must be toggled on in Org settings → Data → AI data processing. The user must opt in to AI features for the organization first.
  3. Prompt subscriptions enabled for the organization — a Insights-managed rollout flag. If it's off, the org has not been granted access yet; tell the user to reach out to Insights to enable it (there is no self-serve toggle).

If any of the three is missing, stop and tell the user which one to fix — re-calling the tool will not help.

Your access token also needs the query:read scope in addition to subscription:write: a prompt subscription runs LLM-generated InsightsQL over the project's data, so the backend requires query access to create, edit/re-enable, test-deliver, or delete one. A subscription:write-only token is rejected with a 403.

Required arguments

prompt: "..."                         # ≤4000 chars; setting this (with no insight/dashboard) makes it a prompt sub → resource_type "ai_prompt"
target_type: "email" | "slack"        # webhook is rejected for prompt subs
target_value: "..."                   # comma-separated emails, or "<channel_id>|<channel_name>"
frequency: "daily" | "weekly" | "monthly" | "yearly"
interval: 1                            # 1 = every tick; 2 = every other tick; etc.
start_date: "2026-09-15T09:00:00Z"   # anchors the recurrence + time-of-day; need not be in the future — the scheduler delivers the next occurrence
title: "..."                          # display name in the subscriptions list

There is no resource_type argument to send — the kind is derived from which field you set (prompt ⇒ AI report) and returned as the read-only resource_type.

Optional arguments

byweekday: ['monday', 'wednesday'] # weekly only — days the rrule fires
bysetpos: 1 # most useful with monthly; requires byweekday — e.g. byweekday:['monday']+bysetpos:-1 = last Monday
count: 10 # cap total deliveries
until_date: '2026-12-31T00:00:00Z' # stop on/before this date
integration_id: 42 # Slack only — required; from integrations-list (see "Slack target")

Slack target

target_value must be <channel_id>|<channel_name> (the format the integration returns). Build it in three steps:

  1. insights:integrations-list filtered by kind=slack → pick the Slack integration's id.
  2. insights:integrations-channels-retrieve with that id → pick a channel; it returns each channel's id and name, which you assemble into target_value as <id>|<name>.
  3. Pass that integration's id as integration_id — the subscription is pinned to one specific Slack integration so reconnections elsewhere don't accidentally re-route deliveries.

Examples

Weekly Monday-morning AI summary by email

prompt: 'Top events week over week, with the biggest drops and any new failure modes called out.'
target_type: email
target_value: [email protected]
frequency: weekly
interval: 1
byweekday: ['monday']
start_date: '2026-09-14T08:00:00Z'
title: 'Weekly product pulse'

Daily Slack report at 9am

prompt: "Yesterday's sign-ups, where they came from, and any errors they hit during onboarding."
target_type: slack
target_value: 'C0123456789|growth-updates' # <channel_id>|<channel_name>; only the channel id is used, the name is cosmetic
integration_id: 42
frequency: daily
interval: 1
start_date: '2026-09-15T09:00:00Z'
title: 'Daily onboarding watch'

Pitfalls

  • The kind is immutable. It's derived from which relation is set, so you can't flip an insight or dashboard sub into a prompt sub after the fact (or vice versa) — a PATCH that adds a prompt to an insight sub is rejected. Pick the right kind at create time.
  • Re-enabling a previously auto-disabled prompt sub has two preconditions, both enforced on the PATCH: (1) a valid prompt — already persisted on the row, or a new one in the PATCH body (so bare {"enabled": true} works when the stored prompt is still valid, but is rejected when the disable cause was an invalid prompt until you supply a good one); and (2) the original creator is still an active user — if that account was deactivated the sub cannot be re-enabled at all (no prompt will help; re-create it instead).
  • next_delivery_date is server-computed from the rrule. Don't try to set it manually — it's read-only. The first delivery fires at the first start_date occurrence that is at least a short buffer (currently ~15 minutes) in the future, so a start_date only seconds ahead rolls to the next occurrence.
  • Transient send failures retry; only permanent failures auto-disable. A transient failure (Slack rate limit, SMTP blip, network) fails that delivery and is retried by Temporal within the run, then re-fires on the next scheduled tick — it does not auto-disable the subscription, so a persistently-failing channel will keep retrying every tick until you fix it. Only permanent/structural causes auto-disable: a disconnected Slack integration, a revoked channel permission, an invalid prompt, or revoked AI data-processing consent. (For multi-recipient email, a delivery only fails when every recipient fails; partial successes still send.) Within a single delivery run the rendered markdown is cached, so Temporal retries of that run don't re-run the LLM pipeline — but each new scheduled tick generates a fresh report.

After it lands

subscriptions-list will return the new row. Confirm resource_type: "ai_prompt", enabled: true, next_delivery_date is in the future, and prompt matches what you sent. The first scheduled tick will run the planner → InsightsQL → synthesis pipeline and email/Slack the rendered markdown.