ZeroWidth Prism

The study's one-on-one qual conversations: guest, status, focus, and — once completed — the structured memo (summary, key claims, tensions, verbatim quotes). Raw transcript included only while no memo exists. inviteUrl is the link to forward for interviews still waiting on their guest.

THE core Prism gesture: generate variations of a node along a named semantic axis ('more visceral', 'for the skeptic', 'stripped to essentials'…) in one of four directions. Runs generation against the workspace's inference credit. May return `needs_confirmation`.

Web-sourced secondary research on a node's idea — market context with citations, stored on the node. depth 'thorough' runs three angled passes (market, evidence, shifts) at ~3× the credit. `focus` steers what it goes after; without one it answers a generic brief off the node's own description, so pass it whenever the user has said what they actually want to know. Runs against the workspace's inference credit. May return `needs_confirmation`.

Sets pinned state and/or the color tag on a node. Pins are the cross-field shortlist. May return `needs_confirmation`.

Stops a queued, running or paused panel run. Answers already in stay. May return `needs_confirmation`.

What running a study against a panel would cost: people, model calls, and approximate dollars of workspace credit. Starts nothing. Tell the person the cost before calling prism_panels_run.

Status, progress, spend and the run's own analysis. For the numbers, call prism_studies_results with panelRunId. Synthetic answers are never people's: always say so when you report them.

Saves a panel from a spec (usually from prism_panels_draft) and draws its people from population records in the background, which takes a few seconds. Visible to the workspace unless visibility is PRIVATE. May return `needs_confirmation`.

Turns a plain description of an audience ("100 Midwest senior citizens") into a panel spec: filters on Census and General Social Survey fields, a size, and stated shares for traits with no data behind them (assumed). Saves nothing. Show the person the spec and assumedNotes, then pass the spec to prism_panels_create. poolSize is how many people in the population sample match.

One panel with its current version and the share of people by every field (assumed fields flagged). Use it to confirm a draw looks like the audience the person meant.

Every synthetic panel the person can see: name, current version (people, status), runs so far, and fit (how far it sat from people the last time a study had both). Reuse an existing panel before drafting a new one.

Starts a background run: every person on the panel answers the study (or, for kind interview, sits with the study's interviewer). Spends workspace credit — give the person the estimate from prism_panel_runs_estimate first. Works on draft studies. Answers land as their own result set, labeled synthetic, never counted as responses. Follow with prism_panel_runs_get. May return `needs_confirmation`.

A cross-study digest: every study with responses — title, origin (standalone / canvas node / tracker wave), response count, latest analysis summary, top themes, and which insights were kept in Ledger. The starting point for 'what have we learned about X?' — follow up with prism_studies_get (insights) or prism_studies_results on the studies that matter.

One desk-research report in full: the brief (markdown) and the sources it cites. Its claims are what public sources say, not what anyone at the company told you; when you put them on the Compass map, cite sourceKind 'prism_report' with this report's id.

Every desk-research report on a study, newest first: id, title, the focus it was asked, depth, status (running / ready / failed) and an excerpt. Read one in full with prism_research_reports_get.

Web-sourced desk research kept as a report on a study, with citations. Steer it with `focus`: research accumulates, a broad sweep first, then narrower runs as the questions sharpen. depth 'thorough' runs three angled passes at ~3× the credit. To research a company, make a standalone study about it (prism_studies_draft_standalone) and run one report per area of the business, e.g. focus 'how finance closes the month and approves spend at <company>, from job posts, filings and press'. It plans questions, searches, reads the pages and keeps only findings it can quote from them, so it takes a few m…

Creates a research program that fields the SAME instrument on a schedule — brand tracking, a weekly pulse — each wave an ordinary study. The questions freeze once the first wave fields (that's the trendline), so get them right with the user first. Optionally bind a Ledger metric + score rule so every wave posts a reading (a `scorer` needs a `metricId`). Waves launch on schedule, or on demand with prism_series_launch_wave. May return `needs_confirmation`.

CLOSES the current wave (no more responses; it scores and posts to Ledger if bound) and fields the next one from the series' frozen instrument, readying its participant link. Fielding spends credit for follow-ups, closing chats, and analysis, and closing a live wave can't be undone — say both plainly before proposing. Manual launches don't move the schedule clock. May return `needs_confirmation`.

Recurring research programs (ADR 0031): name, cadence, enabled state, next wave time, wave count, and the metric + score rule when the series posts readings. Waves themselves are ordinary studies.

Edits a series: `enabled: false` pauses the schedule (true resumes), interval / scheduleTime / scheduleTimezone / dayOfWeek move the cadence, name and follow-up / auto-analyze settings change any time. The instrument (questions, scorer) is editable only while no wave has fielded — after that the server refuses with `invalid`, and the answer is a new series. Series ids come from prism_series_list. May return `needs_confirmation`.

Kicks the theme analysis (incremental — reads only responses and interviews since the last run; the previous themes carry forward). Returns immediately with the run's phase; the run continues server-side, so wait a moment and re-read prism_studies_get — findings are fresh once its insights stop reporting an active run. upToDate: true means there was nothing new (a healthy no-op, not an error). Runs against inference credit. May return `needs_confirmation`.

Server-computed banner cut with significance: a target question (scale/number → group means + NPS on 0–10; choice/multi → per-option shares) split by acquisition source or by any single-choice question, each group tested against its complement at 95% (Welch t / two-proportion z). vsRest says higher/lower/not_significant — or not_tested when either side is under the 30-response floor; never present not_tested as 'no difference'. Use this for every 'does X differ by Y?' question instead of eyeballing raw rows.

Permanently deletes a study with its responses, interviews, analysis, and share links. Right for a draft that won't be fielded or a duplicate; for a live study the user just wants to stop, prefer prism_studies_update closed: true — that keeps the data. Study ids come from prism_studies_list_workspace. May return `needs_confirmation` — name the study and its response count, then wait.

Generates a short study (5-8 questions probing the node's problem space — respondents never see the idea itself) and creates it as a DRAFT. The user reviews, previews, and opens it for responses from the study page (or via prism_studies_update publish). Runs generation against inference credit. May return `needs_confirmation`.

Qual-first design: drafts a survey GROUNDED in the study's completed interviews — the recurring claims become measurable questions, the tensions become the choices, in the interviewees' own words. Replaces the study's current questions with the draft (nothing fields until publish). Needs at least one completed interview (see prism_interviews_list). An optional steer biases the instrument ('focus on pricing'). Runs generation against inference credit. May return `needs_confirmation`.

Generates and creates a DRAFT study that isn't tied to any canvas node — brand tracking, workspace-level research, anything you can describe in a sentence. It appears on Prism's Studies page; the user previews and opens it for responses there (or via prism_studies_update publish). Runs generation against inference credit. May return `needs_confirmation`.

One study's questions, response count, open/closed state, and the STORED analysis (summary + themes with strength; insights.activeRun set = an analysis is running right now — wait for it before citing). participantUrl is the live share link to forward when the study is open and one has been minted. Cite theme strengths when reporting results.

Feedback studies attached to one Prism node: title, question count, response count, open/closed.

All studies regardless of origin — standalone, canvas-node, and series waves — newest first: title, question count, response count, draft / live / closed state, and the field / node / series it hangs off. The starting point for 'which studies do we have?'; prism_studies_list is the per-node view, prism_research_digest the findings view. Read one with prism_studies_get.

An AI research analyst writes the report's narrative layer — executive summary, key findings with their numbers, recommendations — from the study's computed record (funnel, per-question aggregates, themes, quotes). Stored on the study; the printable report at the study page renders it above the charts. Runs against inference credit. May return `needs_confirmation`.

Everything the Results tab shows, as numbers you can trust without counting raw rows: fielding funnel (opens → starts → completes, median completion time, sources, per-question drop-off), per-question aggregates (scale distributions + means + NPS on 0–10, choice/multi counts, rank first-place + mean ranks, MaxDiff set scores, word frequencies, text samples), and quality flags (speeders, duplicate participant ids). ALWAYS use this for quantitative questions about a study — never tally answers yourself.

The stored k-means segmentation over the study's numeric answers: named segments with size, share, and the distinguishing features (segment mean vs overall). Null when none computed yet — prism_studies_segments_compute discovers them (needs 30+ responses and 2+ numeric questions).

Runs k-means over the study's numeric answers (silhouette-picked k) and names the discovered segments from their computed profiles. Needs 30+ responses and 2+ numeric questions; recompute overwrites. Runs naming against inference credit. May return `needs_confirmation`.

publish: true takes a draft live (one-way — the participant link starts working). closed toggles whether a live study accepts responses. Also edits the study: title, the `questions` instrument (whole replacement — read prism_studies_get first; LOCKED once responses exist, the server refuses with `invalid` and the answer is a new study), `adaptiveFollowUps` (AI probes on curious answers), the closing chat (`endChatEnabled` + `endChatFocus`), `completionRedirectUrl` for a BYO panel (null clears), and `autoAnalyzeTarget` (null = manual only). Study ids come from prism_studies_list_workspace. May …

Cross-instrument findings for one subject (today: a Prism node) — attached feedback studies with response counts + theme counts, and whether desk research exists. (Interviews are study-level now — read them per study with prism_interviews_list.) THE PLACE TO LOOK before proposing new research: cite what already exists.

Search ZeroWidth product documentation. Returns matching pages with title, slug, public URL, and a query-relevant snippet. Use this when the user asks about a ZeroWidth product (Compass, Workbench, Caliper, Prism, Ledger, Napkin, the assistant), an API behavior, or a policy. No authentication required — the docs corpus is public.

Finds anything in the workspace in one call — uploaded files (searching the text inside them), folders, Workbench flows, shims, knowledge bases and tasks, Compass pages and changes, Caliper datasets, evals, rubrics, reviews, specs and sources, Ledger entries, Napkin sketches, decks, docs, sheets, diagrams and interfaces. The same search the person uses in Home. Use it FIRST whenever the user mentions something without saying where it lives ('the SOW', 'the dispatch interviews', 'that invoice'). The query is words in any order: each word must appear in the thing's name, its tags, the names of t…