Qualtrics Ops

Qualtrics Live-Survey Operations

Instructions

1. When to use

This skill covers operating a survey already built and imported into Qualtrics — publishing, quotas, flow routing, live text patches, panel-vendor integration, security options — on an instrument that is or will be fielding real respondents. It is not survey design or QSF construction; assume the survey definition already exists and the question is how to change it without breaking what respondents currently see.

Two modes. The default is operations: sections 2-8 below govern any write to a live instrument. Audit mode ($qualtrics-ops audit) is the read-only pre-fielding assessment in the section at the end — it produces ranked findings and makes no changes, and any repair it surfaces comes back through the operations sections as a separate, authorized change. The write-safety rules in section 2 are the shared frame for both: audit mode never writes at all, and operations never publish or activate by accident.

2. Non-negotiables

Backups first, always. Pull and save the survey definition (and flow and options as separate JSON) before any write. Qualtrics version restore exists but is coarse and carries data-risk caveats; your pre-change snapshot is the only precise recovery path, and the only one that covers objects (quota counters, options) outside version history.

Verify by read-back, never by absence of error. A 200 response proves the API accepted the request, not that respondents will see the result. After every write, GET the changed object back and confirm it matches intent.

The version list is the proof a change is live, not the publish-state flag. Question/block writes create no version-history entry, so publish-state can report "in sync" while the live version still lags behind your edit. The standard: your publish call created a NEW published version entry carrying your own description — and, because a description proves provenance rather than content, pair it with a read-back of the published content where the API exposes it.

One writer at a time. Confirm you are on the right survey, brand, and environment, that no one has the builder open concurrently, and that you know the rollback path before the first write. On an actively fielding instrument, prefer a change window; and remember the platform's own warning that deleting or restructuring questions and choices can invalidate already-collected data.

Assert everything untouched is unchanged, not just that your target changed — compare parsed structures, since the server may normalize ordering and defaults. Several of the traps below are full-replace endpoints that silently wipe sibling data; catching that requires diffing the whole object against the pre-change backup, not just checking the field you meant to edit.

3. Publish gating — two distinct failure modes

Most flow and options edits to an active survey are staged, not live, until an explicit version publish — respondents keep getting the old version while the API happily reads back your new one. (A few option keys apply immediately, availability among them; treat "staged until published" as the safe default assumption and verify per key.) Question/block writes are worse: they create no list_versions entry at all, so publish-state can say "in sync" immediately after a write that hasn't actually gone anywhere near respondents. Stage all related writes for a change as one batch, verify the batch by read-back, then publish ONCE, deliberately (forcing the publish if your client supports it) — publishing after every individual write can expose respondents to internally inconsistent intermediate versions. Reload the builder UI before trusting its Draft/Published badge — it lags the API.

Publishing an inactive survey activates it. Treat activation as a separate, deliberate decision from publishing a change — have your API client require an explicit activation flag so a routine content publish can never launch fielding as a side effect.

4. Quota API

• START HERE IF A QUOTA COUNTS BUT NEVER BLOCKS: check ActionInfo. QuotaAction: "EndCurrentSurvey" only NAMES the action. The nested ActionInfo object is what actually FIRES it. A quota written with an empty stub —

  "ActionInfo": {"Type": "BooleanExpression"}
  

  — counts every matching respondent flawlessly, forever, and acts on none of them: no termination, no screen-out, no error at write time, at publish time, or at runtime, and the builder UI renders the quota as normal. The platform's own serialization is:

  "ActionInfo": {"0": {"0": {"ActionType": "EndCurrentSurvey",
                              "Type": "Expression",
                              "LogicType": "QuotaAction"},
                        "Type": "If"},
                  "Type": "BooleanExpression"}
  

  ActionType must repeat the QuotaAction value. This was shipped as an empty stub in a homegrown API client and made every quota it ever created inert across multiple live fielding surveys — the true reason quotas "didn't work", after days of chasing logic-dialect theories that were all downstream of it. If a quota's count is incrementing, its matching logic is already correct and the problem is not the logic; go straight to ActionInfo.

• Proven by controlled before/after, and this is the method to reuse. Do NOT try to diagnose quota enforcement on a clone of the real instrument — geo gates, duplicate-device gates, anti-automation paradata and session-resume cookies will each eject or short-circuit the walk, and stripping them piecemeal corrupts the flow (all of this was tried, and burned hours). Instead build a minimal disposable survey from scratch: three quota-bearing questions on page 1, one marker question on page 2 (reaching page 2 is the "not blocked" signal — a single-page survey cannot demonstrate blocking at all, because the respondent has already answered everything by the time the quota evaluates), plus TWO quotas: one the walk matches and one differing in a single condition as a negative control. Walk it once to fill the match quota, then walk again and observe. The control must stay at 0 — that is what proves the multi-condition AND is sound rather than collapsing. Change ONE variable per walk. In the reference case the immediately-preceding walk already had the group's Selected flag, canonical dialect and EndSurveyOptions all in place and still reached page 2; populating ActionInfo alone flipped the identical walk to terminating at page 1.

• Ruled out along the way — do not re-chase these (each was a plausible theory that cost real time): the quota group's Selected flag (setting it True to match a working reference changed nothing on its own); the presence or absence of EndSurveyOptions (worth having for the QuotaMet flag, but not what makes a quota fire); and Occurrences: 0 as a way to express "already full" — that IS wrong and no platform-authored quota uses it (minimum observed across 49 reference quotas was 16), but with a broken ActionInfo no limit value of any kind would have blocked anyone. Use Occurrences: 1 when a cell is already over target and you want it shut: the first matching respondent tips it to full and every subsequent one is blocked.

• The actual root cause of "compound quota logic never fires" is a hand-authoring dialect mismatch, not a platform limit on condition count — and getting this wrong once already cost a full day of live-fielding churn across two surveys, so read this one carefully. Qualtrics's own internally-generated quota JSON uses "Conjuction" (not "Conjunction" — a legacy misspelling baked permanently into the schema) as the key joining multiple conditions, and "q://{QID}/ChoiceTextEntryValue" (not "q://{QID}/TextEntry") as the operand/locator for a numeric comparison against a text-entry question, with ChoiceLocator duplicating the same string. Hand-write either one with the "obviously correct" English spelling or the intuitive-looking locator and the quota accepts the write (200 OK), reads back exactly as sent, renders correctly in the builder UI's condition editor, and then matches nobody, ever, with no error anywhere — because the engine simply doesn't recognize the key/locator and silently drops that half of the condition. This produced the exact symptom pattern that looks like "compound conditions never fire": single-condition quotas (no Conjuction needed) counted correctly the whole time; every age-range and multi-choice-OR quota sat at zero, on multiple live production surveys, for over a week, undetected. The fix, once diagnosed, is to write compound conditions in the correct dialect, not to avoid them. A same-day rebuild using the verified dialect — including three- and four-condition flat AND groups spanning multiple different QuestionIDs (age range AND gender-selected AND region-selected in one group) — fired correctly and counted real respondents within the hour. Do not hand-guess this dialect from documentation or from what "looks right." Find a real, platform-exported QSF from the same account (or ask the user for one — even an unrelated old survey works) and diff your generated Logic block against its quota objects byte-for-byte before trusting anything with more than one condition; this is the single highest- leverage check available and takes minutes. Absent a reference file, the fallback is the advisor-consult pattern: hand a second, independent model the full evidence trail (what fired, what didn't, exact JSON of both) and ask it to reason from first principles rather than guessing again yourself — that is what actually surfaced this dialect mismatch after multiple failed self-directed attempts.

• LogicType: "EmbeddedField" quota conditions take the BARE field name as LeftOperand, not the e://Field/... piped reference — and getting this wrong is silent, like every other quota-dialect error. A platform-authored Cross quota carries {"LogicType": "EmbeddedField", "LeftOperand": "gc", "Operator": "EqualTo", "RightOperand": "1"} — just "gc". Writing "e://Field/gc" (the form that is correct everywhere else in Qualtrics: Branch logic, display logic, piped text, redirect URLs) produces a quota that accepts the write, reads back intact, and matches nobody. This was learned expensively: a Branch-precompute design (merge a value into one flat embedded field, then quota on equality) was built at ~130 quota objects across two live surveys, with the field VERIFIED correctly populated in the response export for real respondents, and every one of those quotas read count: 0 — because all of them used the e://Field/ prefix. Do not conclude from a failure like that that embedded-field quota logic is unsupported; check the operand format against a platform-authored example first. Note also that when the goal is an interlock, LogicType: "Cross" (below) is the native mechanism and usually beats precomputing a merged field at all.

• LogicType: "Cross" is Qualtrics's native interlock quota, and it is a different object shape from Simple — know which one you are reading. A Simple quota is one cell: Logic is a single expression tree (a dict) and Occurrences is that cell's absolute target. A Cross quota is a whole grid: Logic is an ARRAY of logic sets, the engine crosses the sets to generate cells, and Occurrences is the TOTAL across the grid. In a Cross quota the Conjuction key does not hold "And"/"Or" — it holds that condition's percentage allocation ("27%", "29%"), and each cell's effective target is Occurrences x the product of its shares. So the same misspelled key means two completely different things depending on quota type; do not pattern-match one onto the other. Choosing between them: Cross expresses an age x gender x region interlock as ONE object instead of hundreds, which is decisive when building a grid up front. But because Cross targets are percentages of a total, it is poorly suited to rebasing a partially-collected field — "this cell has 33 slots left of 473" is trivial as a Simple quota's absolute Occurrences: 33 and awkward as a share of a total that is already half filled. Build with Cross; repair mid-field with Simple.

• Sidestep the whole range-comparison problem at design time by asking age as a categorical band question rather than a numeric text entry. A platform-authored reference survey that quotas cleanly on age does it with a multiple-choice item (18-29 / 30-39 / 40-49 / 50-59 / 60+) and plain Selected conditions — no ChoiceTextEntryValue locator, no >=/<= pair, no compound condition, nothing to get wrong. If the instrument is not yet fielded and the analysis does not need exact age, this is strictly the safer design.

• For a bilingual/multi-arm instrument, don't reflexively split every marginal quota by arm. If one arm carries the overwhelming majority of traffic (check the actual split from the response export, don't assume), splitting age/gender/etc. into one quota per arm doubles the object count and produces a nonsensical-looking result on screen (a demographic quota that appears to depend on survey language). Fold the minority arm into the majority arm's quota instead — check only the majority-language question, size it to the full combined remaining target — and accept that the minority arm isn't independently capped by that specific quota. Disclose the tradeoff; don't build the split by default.

• Before trusting ANY quota with more than a trivial condition, prove the engine actually fires it: create it, then either drive one real or preview response through the matching path and confirm count increments, or — cheaper — diff its Logic shape against a quota on the same live survey that is already demonstrably counting; identical shape, live proof either way. Note preview/import responses are not reliable for this: a response created via POST .../responses (import) does not trigger quota evaluation at all — that's expected, uninformative behavior, not a signal either way.

• A quota's count never back-counts responses collected before the quota existed. It only increments on new submissions from creation forward. On a survey that has been fielding for a while, a freshly created or freshly fixed quota reading 0 or low is not evidence it's broken — and, the more dangerous direction, it is also not evidence a previous broken quota didn't already let the sample run uneven. Either way, count cannot tell you the true current composition of an already-fielding survey. Pull the actual response export (POST .../export-responses) and recompute fill directly from respondents' real answers against your target grid — that's the only number that reflects who has actually been collected, and it's required reading before reporting "verified" on any quota fix applied mid-field.

• Quota creation's group auto-assignment is flaky, not just "always goes to the first group" — confirmed empirically across two surveys built with near-identical scripts. There is no field to target a specific group on write. In one run, three sequential create_quota_group calls (Age, Gender, Region) resulted in Gender's quotas silently landing in the Age group while Region correctly got its own; in another run on a different survey, all three groups' quotas landed in the very first group regardless of creation order. Do not assume any particular assignment pattern, and do not rely on the UI's "Move to…" menu for anything beyond a handful of objects — it does not scale. Instead: create every quota first (accept whatever group it lands in), THEN read back the actual membership and fix it programmatically via the group PUT below. Order matters when fixing it: a quota already listed in group A's membership cannot be added to group B's membership directly — the API returns ESDEF44 ("already exists in Quota Group X"). PUT the source group first with a shrunk membership list (removing the quotas you're about to move), THEN PUT the destination group with them added. Verify final state by reading back every group's membership and matching quota names against your intended structure — the group's own Name field is not proof its Quotas array is what you think it is.

• The quota-group update endpoint is a full replace: omit the quotas array and the group's membership is silently wiped. Always resend the complete membership plus any fields the API requires on write but omits from its own list payload (e.g. a match-mode flag) — write-shape and read-shape are not the same contract.

• Choice-based quota conditions need both the operand the evaluation engine reads AND the locator the editor UI renders its dropdown from. Write only the operand and the condition still works but displays as an empty "Select Choice…" in the UI — and a later UI-side save of that blank state can overwrite live quota logic with nothing.

• Confirm the exact operator enum the API expects (vendors sometimes reject a plausible-looking synonym) rather than assuming from REST convention.

• Give every hard quota its own EndSurveyOptions at build time. A quota created without one inherits the survey-level termination settings, which works — the respondent still exits and still hits whatever redirect the survey-level EOSRedirectURL resolves to — but the resulting response row carries no QuotaMet flag, so afterwards you cannot tell a quota-terminated respondent from any other early exit except by inferring it from their answer pattern. On an instrument that also produces consent-refusal and screen-out rows of the same shape (demographics answered, no outcome data), that inference gets genuinely fiddly. The platform's own serialization for a hard quota looks like:

  "EndSurveyOptions": {"EndingType": "Advanced",
                        "ResponseFlag": "QuotaMet",
                        "Screenout": "Yes", "IgnoreResponse": "Yes",
                        "AnonymizeResponse": "Yes", "CountQuotas": "No",
                        "SurveyTermination": "Redirect",
                        "EOSRedirectURL": "<vendor quota-full URL>"}
  

  Note this supersedes an earlier claim in this skill that the API accepts no per-quota redirect — a platform-exported reference survey carries EOSRedirectURL inside EndSurveyOptions on its quota objects, so the quota-full URL can live on the quota itself rather than being bracketed in the flow. Treat the old claim as untested rather than true; it dates from the same period as the compound-logic misdiagnosis above.

• The flow-bracket pattern remains the right retrofit when quotas are already live without EndSurveyOptions: set an embedded field to the quota-full exit URL immediately BEFORE the block holding the quota-bearing questions, reset it to the screen-out URL immediately AFTER, and have the survey-level end-of-survey redirect read that field. Verify by mapping flow indices — the setter must precede the block, the reset must follow it. This gets the vendor disposition right (which is the billing-relevant half) even though the response row stays unflagged. Retrofitting EndSurveyOptions onto many live quota objects is a mass mutation on a fielding survey; if the bracket is already correct, the remaining benefit is forensic tidiness only, and is usually not worth the write.

• Quota counts can retain stale values after response deletion even when the deletion call requests a decrement. Before a FIRST fielding wave, zero the counters explicitly rather than trusting the decrement flag; mid-study, reconcile in-progress sessions and prior-wave records first — resetting a live counter is destructive and needs explicit authorization.

• Quota-list endpoints paginate at a small page size — always follow the next-page cursor, or an audit silently covers only the first page of quotas.

5. Flow mutation and routing placement

• Anchor routing gates on block descriptions, not on a data-capture node. A capture node's position can vary across instruments (some run it pre-consent, some post-), so a gate anchored to "wherever that node sits" can end up before consent on some builds — ethics-relevant if the gate is a termination. Anchor each gate type to a stable semantic point instead: consent-dependent gates immediately after the consent block; paradata-based gates after the point where every field they read is guaranteed to exist; questionnaire-anchored checks right after their own block, never held to end-of-survey (a late termination costs the respondent the whole length of interview for nothing).
• Know which multilingual architecture you have. Qualtrics' native translation layer keeps ONE block structure with per-language text; a branch-per-language build duplicates every block per arm. On the latter, duplicate each gate into every arm with a fresh flow-element ID — one gate does not cover all arms.
• Capture-before-gate order is load-bearing. The node that writes a field must precede every gate that reads it. Get the order wrong and the gate fails safe — no error, no fire, just silently dead — which is far more dangerous than a routing bug that throws.
• When the exit URL is carried in an embedded-data field consumed by the end-of-survey redirect, the node that SETS the field must precede the terminating element inside the gate — the termination ends flow evaluation, so anything ordered after it never executes.
• On paired-language (twin) instruments, never express a failure condition as "the correct option was not selected" — an unanswered field in the respondent's other language arm also satisfies "not selected" and routes out the wrong arm entirely. Express failure as positive selection of a wrong option instead.
• Guard any geolocation-based termination with an explicit "value present and not equal to the excluded value," not just "not equal to." An unresolved lookup (proxy, privacy relay, corporate VPN) must not silently satisfy a bare not-equal check and terminate a legitimate respondent.
• Reserve live termination for signals that cannot belong to a real, eligible respondent: ineligibility, duplicate device/session, a hard machine signature. Anything that is scored or graded on a continuum — a bot-risk score, a fraud score, an attention-check failure, response-speed outliers — belongs in analysis-side exclusion criteria, not a live termination branch, because a live gate can't be revisited once it has turned away a respondent.

6. Live text edits vs. source specs

When a survey is built from versioned source specs (YAML, a survey-builder config, etc.), a live typo or wording fix still often needs to go directly against the live question via a targeted patch — matching exact surrounding text and replacing only the intended span — rather than a full spec rebuild and repush, because a rebuild will clobber any manual formatting or ordering that was applied directly in the live tool since the last build. When syncing the fix back into the source spec afterward, match whitespace-insensitively: prose in structured source formats commonly soft-wraps, so a byte-for-byte diff against live text produces false mismatches.

Keep an explicit list of anything the build pipeline does not emit (e.g. quality-routing branches, vendor-specific disclosures added live) — a rebuild silently drops these, so they must be reapplied by hand after every rebuild and repush.

7. Panel-vendor integration basics

Redirect logic for panel-vendor traffic follows a stable pattern regardless of vendor: a pre-consent screen-out value, a terminal complete value, and an end-of-survey redirect to whichever URL parameter carries the vendor's completion redirect — typically piped from an embedded-data field the vendor's entry link populated. Bracket any quota-bearing block with a quota-full redirect variant so respondents who close out a quota mid-survey get routed to the vendor's quota-full endpoint rather than falling through to a generic completion or termination redirect.

URL query parameters resolve into piped references at session start regardless of where (or whether) the embedded-data declaration sits in the flow — live-verified against redirect pipes. Declare the field anyway: declaration is what makes the value reliably typed, saved, and exported, and downstream logic easier to read.

(Panel vendors vary — a generic panel-vendor redirect endpoint is the concept that matters here, not any particular vendor's API shape.)

Before enabling ANY quota's hard-terminate action (EndCurrentSurvey) on a survey running through a panel vendor, check prior vendor correspondence for an explicit statement of which real-time termination paths are in use. Vendors are sometimes told directly — in an email, not just implied by default config — that a given exit status (a quota-full redirect code, say) is deliberately not used, with real-time termination limited to a named, narrower set of conditions (duplicate device, geo-ineligibility, automation detection). Flipping a quota to hard-terminate is, from inside Qualtrics, a purely internal config change with a green checkmark and no warning — but it silently starts exercising a redirect path the vendor's system was told to expect never to see. This is a compliance question, not a technical one, and the fix isn't a Qualtrics setting: read the actual correspondence (search for the vendor's redirect status codes by name, not just "quota") before assuming a hard quota is safe to activate on a live vendor-sourced field.

8. Security options

Treat the survey's security/options block as read-modify-write: fetch the full current object, change only the target keys, and write the whole object back — then assert every key you did not intend to touch is byte-identical to the pre-change value. Options endpoints are as prone to full-replace semantics as the quota-group endpoint above, and a security setting silently reset to a default (e.g. a fraud-detection threshold, a ballot-box-stuffing prevention flag) is the kind of regression that goes unnoticed until an incident, not at write time.

After any live toggling of quota actions or repeated publishes on a fielding survey, verify no real respondent was actually affected — don't just reason about it. Pull the response export, filter to sessions with StartDate inside the affected window, and check Progress/Finished across all of them. A clean 100/Finished=True for every session is checkable proof nobody was cut off mid-survey by a config change in flight; don't rely on "the targets never should have hit zero" reasoning alone when the actual data is one export call away.

Audit mode (audit)

Run this mode when the task is to assess a survey rather than change it, invoked as $qualtrics-ops audit. It reads the live instrument the way it will actually run, not the way its build files say it should, and it produces findings, never fixes.

Audit mode is read-only on the survey definition. GET everything; PUT/POST nothing. If the platform offers a no-op write check for token scope, that is the only write. Every repair the audit surfaces goes back through the operations sections above as a separate, explicitly authorized change — with its own backup, read-back, and publish-with-proof — rather than being folded into the audit. The single exception is the optional browser walk (Phase H), which generates test responses: data-plane writes with their own cleanup obligations, and possibly test hits on a vendor dashboard, so it is opt-in and announced, never silent.

The API mechanics the audit reads through are documented above and not repeated here: what proves a change is actually live (§3), quota object shapes, logic dialect, and list pagination (§4), flow anchoring and capture-before-gate order (§5), the vendor redirect pattern and query-parameter resolution (§7), and options read semantics (§8). The audit checks the live objects against those standards; it does not restate how to write them.

When to run an audit

Immediately before a soft launch or full launch; after any live patch to a fielding instrument; when a vendor reports a broken redirect or "different content"; when handed an unfamiliar survey to take over. Inputs: API credentials and the survey id; ideally also the pre-registration or PAP (for the report-only-vs-terminating posture), the vendor's integration sheet (redirect URLs, ID parameter name), and the quota targets. A browser automation tool enables Phase H; without it, run A–G and say so in the report.

Fielding now happens in an environment where AI agents complete surveys at scale and pass conventional attention checks (documented since 2025 in peer-reviewed and platform validations), panel vendors bill on redirect passbacks, and platforms silently stage rather than publish edits. Each of those failure classes is invisible in a casual preview and cheap to catch here.

Audit posture

• Evidence or it didn't happen: every PASS cites the object read back (flow element, option key, quota logic), never the absence of an error.
• The registered design wins. Where a PAP declares an item report-only, a live branch that terminates on it is a blocking finding even if well-built.

Phase A — identity and publish state

• Confirm the survey id, name, and active/inactive state match intent. An inactive instrument scheduled for launch is fine; an active one nobody meant to open is a finding.
• Apply the §3 standard: the working definition and the published version must match, proven by the version list rather than an in_sync flag. Staged-but-unpublished edits to a fielding survey are a blocking finding.
• Response settings that shape the data: partial-response window, multiple- submission prevention, anonymization/IP recording, link type, expiration — and whether in-progress respondents stay pinned to the version they started.

Phase B — consent before anything

• The first substantive screen a respondent reaches is consent (or a language selector whose every arm leads first to consent).
• Nothing evaluates or acts before affirmative consent: no terminating gates, no quality branches, no telemetry collectors on or before the consent page. (If the approved protocol places a minimal eligibility screener before consent, audit that instead for authorization, minimization, and whether pre-consent data are retained.) Location/device capture nodes may write earlier (platforms populate them at session start), but every branch that reads them must sit after consent.
• Decline path: declining consent must route to the vendor's screen-out (or the study's stated exit), not dead-end or count as a complete.
• Consent text ↔ configuration consistency, both directions: if invisible scoring or fingerprinting is enabled (reCAPTCHA, device checks), the text discloses it; if the text promises skippable questions, optional questions actually exist. A consent page describing a survey that isn't this one is a finding whichever direction the drift runs.

Phase C — question integrity

• Force-response completeness: enumerate every question; classify descriptive (no answer possible), forced, requested, and unvalidated. The check is consistency, not a universal forced-by-default norm (optional is often the right call for sensitive items): every unvalidated answerable item must be one the design names optional, and if any exist, Phase B's consent-consistency check must see them.
• Attention and manipulation checks: present where the design says, and their consequence (terminate vs record-only) matches the registration. In the current environment, terminating on an attention check screens out humans while catching almost no agents — flag it as a design smell even when it matches the PAP.
• Multilingual instruments: first identify which architecture you have (§5). Under the native translation layer, audit the translations for coverage; under a branch-per-language build, every item, choice set, validation setting, and embedded JS must exist symmetrically in each arm. A check present in one arm only, or logic testing "correct option NOT selected" on a twin build, is a blocking finding.

Phase D — flow structure

Walk the full flow tree, at every nesting depth:

• Block order matches the intended instrument; randomizers present with the intended settings (even presentation, subset size).
• Capture-before-gate holds for every embedded-data field (§5). A guarded condition on a never-yet-written field is silently dead — it fails safe, which is exactly why nobody notices.
• Terminating branches: condition logic decodes to the intended trigger; inner flow sets the exit redirect before the End-of-Survey element; unique flow IDs throughout; the terminal "completion" redirect node is the last element.
• On branched (language/arm) instruments, structural checks run per arm, not once globally.

Phase E — vendor integration

• Redirect pattern (§7): a pre-consent default carrying the screen-out URL, a terminal overwrite carrying the complete URL, end-of-survey set to redirect to the piped field. Early leavers must exit as screen-outs, completers as completes, quota-fulls (if hard quotas exist) as quota-fulls — each URL byte-exact against the vendor's sheet.
• The vendor's respondent-ID parameter is captured as embedded data and echoed back on every exit path, including declines. Because query parameters resolve regardless of declaration (§7), treat a missing declaration as a minor finding and a wrong parameter name as fatal.
• Enumerate which vendor endpoints can receive traffic and which are dead by design, and check that against what the vendor was told in writing (§7). A quality or quota-full endpoint the vendor expects to fire, wired to nothing, is a relationship problem waiting for fieldwork.

Phase F — quotas

• Decode every quota's logic against the live question's choices and audit it against the RATIFIED grid, not an assumed one: marginal-family designs should partition each frame exactly once with per-family targets summing to the commissioned N; interlocked or deliberately overlapping designs have their own intended structure (check the multiple-match setting, and identify Simple vs Cross before reading Logic at all — §4). Screening categories ("I don't live here") belong to no quota either way.
• Hard vs soft actions match the ratified design; group labels say which is which truthfully. For any quota whose action is EndCurrentSurvey, read ActionInfo and confirm it is populated with the nested firing shape (§4) — an empty stub counts every match and blocks nobody, and no other symptom ever surfaces.
• Verify each quota's condition dialect against §4 (Conjuction, ChoiceTextEntryValue, bare field names for EmbeddedField operands, both the evaluation operand and the UI ChoiceLocator). A quota reading count: 0 on a fielding survey is a dialect finding until proven otherwise, and pagination must be followed or the audit silently covers page one only.
• All counts are zero before fielding (test responses leave phantom counts even after deletion-with-decrement — read the actual counters). On an instrument that has already been fielding, count cannot tell you composition at all; recompute fill from the response export (§4).

Phase G — anti-automation layer

• Platform toggles (bot-detection scoring, device fingerprinting, geo capture) are on if the design says so — and disclosed per Phase B.
• Behavioral instrumentation (interaction paradata, honeypots, page timers) is present on the pages the design instruments, in every language arm.
• The live-terminating set is restricted to signals that cannot plausibly be a real person: ineligibility, duplicate device, machine signature. Anything scored or graded (bot-score thresholds, fraud scores, speed cutoffs, attention items) belongs to analysis, not to a live gate (§5) — a scored live gate is a finding.

Phase H — browser walk (optional, needs browser automation)

• Use the LIVE distribution link, never the preview (preview banners change rendering and skip embedded-data population). Append a test value for the vendor ID parameter.
• Walk at minimum: one decline (assert the screen-out redirect fires with the ID echoed), one complete per language arm (assert the complete redirect), one mobile-viewport pass (conjoint tables and stacked layouts render; nothing clips). Where feasible add: the quota-full path, one pass per experimental arm, a missing-vendor-ID entry, and validation/back-button behavior on one forced item.
• Confirm no screen precedes consent, and that the consent page renders in the right language for each arm.
• Clean up: delete the test responses with quota decrement, then re-read quota counts (Phase F) — and note that in-progress partials usually cannot be deleted via API and must expire or be cleared in the UI.

Audit report

Rank findings blocking / major / minor, each with the evidence read back and the phase that produced it. State explicitly: live version vs working version; which phases ran (and that H was skipped, if it was); which findings the registered design forces you to leave alone. End with the test-response cleanup confirmation if Phase H ran.

Quality Checks

• Pre-change backup saved (survey definition + flow + options as separate JSON) before any write
• Publish issued after every quota/question/flow write, with activation (allow_activation/equivalent) triggered only at deliberate launch, never as a side effect
• Change verified live via list_versions showing a version with your own description — not via publish-state or the builder UI badge alone
• Full object read back post-write; every untouched key confirmed byte-identical to the backup
• Quota-group and options writes sent as complete objects (full membership / full key set), never partial
• Any quota with more than one condition is diffed byte-for-byte against a real, platform-exported QSF's quota Logic shape (Conjuction spelling, ChoiceTextEntryValue locator) before being trusted — never hand-authored from the "obviously correct" spelling
• Any LogicType: "EmbeddedField" quota condition uses the BARE field name as LeftOperand ("gc"), never the e://Field/ piped form that is correct everywhere else in Qualtrics
• Quota type identified before reading or writing Logic: Simple = one cell, dict-shaped logic, absolute Occurrences; Cross = a grid, array-of-logic-sets, Occurrences is the total and Conjuction holds a percentage share, not And/Or
• Fill verified against a real response export, never against count alone, on any survey that was already fielding before the quota was created or fixed
• Every hard quota's ActionInfo is POPULATED with the nested {"0":{"0":{"ActionType":<same as QuotaAction>,"Type":"Expression", "LogicType":"QuotaAction"},"Type":"If"},"Type":"BooleanExpression"} shape -- an empty {"Type":"BooleanExpression"} stub yields a quota that counts every match and blocks nobo

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