data-programs

name	data-programs
description	Save a run-code fetch/join/aggregate script as a stored, refreshable data source any app's own charts/tables can render, instead of a hardcoded provider action or a per-view re-fetch. Use when an ad-hoc run-code or provider-api-request analysis should become a live, cached data source other users or panels can reuse.

Data Programs

A data program is a named, stored, agent-authored JS script that fetches, joins, filters, or aggregates data — provider APIs via providerFetch / providerFetchAll / providerSearchAll, app data via appAction, or Resources-backed workspace files — and emits a small { rows, schema } result that gets cached in SQL. Any app can render that result through its own table/chart components; nothing about the primitive is analytics-specific.

This ships automatically wherever run-code is available — there is nothing to mount, wire, or configure per app. If your app already has run-code enabled, save-data-program / preview-data-program / run-data-program / list-data-programs / get-data-program / delete-data-program are already registered actions.

Why this exists

Without it, "build a live view of X" tends to end in one of two bad places: a hardcoded action per vendor/query shape (brittle, doesn't scale to every customer's custom fields), or re-running the same fetch/join logic from scratch on every page view (slow, and it re-spends the same provider quota every time someone looks at a chart). A data program is the middle ground: write the fetch/join/aggregate logic once as ordinary run-code, save it, and let the cache + refresh policy handle "how often does this actually need to re-run."

Authoring workflow

1. Prototype the fetch/join/aggregate logic as a normal run-code script and confirm it returns the rows you expect.
2. Change it to call emit(rows, schema?) exactly once at the end instead of returning or printing the result.
3. Save it with save-data-program({ name, title, description, code, defaultParams?, refreshMode?, refreshTtlMs?, background? }). This dry-runs the code with defaultParams before persisting and rejects the save with a structured error if it fails, so a broken program is never stored. On success it returns { programId, rowCount, columns, sampleRows } — use that as proof the program produces the rows you expect.
4. Bind whatever UI needs the result. Your app's own read path calls run-data-program({ programId, params?, forceRefresh?, includeRows? }) and gets back rows/schema/cache metadata to hand to your own table/chart components — there is no dashboard-specific plumbing baked into the primitive itself. (The analytics template's "program" dashboard panel source is one such adoption, not a required pattern — see its data-programs skill if you want a concrete UI wiring reference.)
5. Iterate without persisting via preview-data-program({ code, params? }) — same dry-run path, no stored row.

The sandbox surface

Exactly the run-code globals (providerFetch, providerFetchAll, providerSearchAll, appAction, webFetch, workspace* Resources helpers), plus:

• params — a frozen global object holding the params for this run. Read-only; mutating it throws.
• emit(rows, schema?) — call exactly once, at the end. rows must be an array of plain objects. schema, when provided, is { name: string, type: string }[]; when omitted it's inferred from the first 50 rows (a column is "json" if rows disagree on primitive type, otherwise "number" / "string" / "boolean"). console.log remains free for debugging and never interferes with parsing the emitted result.

Caps enforced on every run:

Limit	Value
Max emitted rows	10,000 (excess dropped, truncated: true)
Max emitted result size	4 MiB (rows dropped from the end to fit; a single oversized row is a hard failure)
Max active programs per app	200
Minimum refresh TTL	60,000 ms
Run rows kept per (program, params)	5 most recent

Truncation is always explicit (truncated: true) — never a silent drop.

Caching and refresh model

Every (programId, paramsHash) pair has its own run history:

Situation	Behavior
Fresh cache (younger than refreshTtlMs, or refreshMode: "manual" with a prior success)	Returns cached rows instantly, cacheHit: true
Stale cache	Re-executes synchronously (25s budget for view-triggered reads, 120s for agent/manual calls), replacing the cache on success
refreshMode: "manual"	Only refreshes on explicit forceRefresh: true
No cache yet	Executes synchronously like a normal first fetch
background: true program with no fresh cache	Serves the last good run with stale: true and enqueues a durable background execution; a later call finalizes it once complete
Background program still running, no prior success	background_pending failure — surface an explicit "still computing" state, never a blank result
Execution fails	Structured { code, message } failure; a previous success is attached as lastGoodRun for stale-serve
Program archived (soft-deleted)	Explicit archived failure

Use background: true only for scripts that routinely exceed the foreground timeout (large multi-provider joins, deep pagination). Everything else should stay foreground.

Security notes

• Credentials are always the calling viewer's, never the program author's. providerFetch inside a program resolves auth using the caller's own request context. A viewer without a configured provider key sees that provider's normal auth error on that program's result — never another user's data.
• Programs are shareable org-internal only — never public. Sharing a program means teammates can view its cached result; because execution uses the viewer's credentials, only bind to or view a program you trust.
• Raw provider tokens never reach the model or the browser. Only the { rows, schema } an emit() call produces ever leaves the sandbox.
• The mutating/executing actions (save-data-program, preview-data-program, run-data-program, delete-data-program) are not callable from a sandboxed extension/iframe bridge — only from the agent or a server-side read path.

Related skills

• actions for run-code conventions and the sandbox globals this primitive is built on.
• sharing for the org-internal, never-public sharing model programs use.
• security for credential-handling and access-scoping invariants.