${escapeHtml(title)}${meta}

`\n }\n return `

`\n}\n\nfunction renderAnswerDetails(answer: PerceptionAnswerViewModel): string {\n const quotes = answer.evidenceSentences.length\n ? `

What the answer said

${escapeHtml(sentence)}

`\n : `

What the answer said

${\n answer.verdict === null\n ? 'This answer was not measured, so nothing was read out of it.'\n : 'No sentence in this answer took a position on the brand.'\n }

`\n const concerns = answer.concerns.length\n ? `

Concerns this answer raised

${escapeHtml(concern)}

`\n : ''\n const sources = answer.sources.length\n ? `

${answer.sources.map(renderSource).join('')}`\n : `

${\n answer.verdict === null\n ? 'This answer was not measured, so no source was attributed to it.'\n : 'The engine attributed no source to this answer.'\n }

`\n const searches = answer.searchQueries.length\n ? `

Searches the engine ran${\n escapeHtml(answer.searchQueries.join(', '))\n }

`\n : ''\n const models = [\n answer.requestedModel ? `Requested: ${escapeHtml(answer.requestedModel)}` : '',\n answer.servedModel ? `Served: ${escapeHtml(answer.servedModel)}` : '',\n answer.completedAt && formatTime(answer.completedAt)\n ? `Completed: ${escapeHtml(formatTime(answer.completedAt)!)}`\n : '',\n ].filter(Boolean).join('')\n const answerText = answer.answerText\n ? `

Full answer

${\n escapeHtml(clip(answer.answerText))\n }

`\n : '

Answer text was not retained for this result.

'\n const error = answer.error\n ? `

Not measured${escapeHtml(answer.error)}

`\n : ''\n return `\n

View evidence

${models}

\n ${quotes}${concerns}${error}\n

Sources

${sources}

\n ${searches}\n ${answerText}\n

`\n}\n\n/**\n * The headline row.\n *\n * Flat KPI cells, never progress bars: \"2 of 3 answers\" has no target to fill,\n * and a bar would invent one. The denominator is written under every number\n * rather than once at the top, because the number and the bound it rests on\n * have to be read together.\n */\nfunction renderVerdictSnapshot(perception: PerceptionViewModel): string {\n const measured = perception.measuredAnswers\n const checkedAt = formatTime(perception.checkedAt)\n const cells = perception.verdicts.map((verdict) =>\n `

${escapeHtml(verdict.label)}

${\n measured === 0\n ? '

Not measured

'\n : `

${verdict.count}

`\n }${\n measured === 0 ? 'No answer was measured' : `of ${measured} ${plural(measured, 'answer', 'answers')}`\n }

`\n ).join('')\n return `\n

Verdict snapshot${renderInfoTip(VERDICT_TIP)}

\n ${checkedAt ? `

Checked ${escapeHtml(checkedAt)}

` : ''}\n

${perception.requestedAnswers} branded ${\n plural(perception.requestedAnswers, 'question', 'questions')\n }Gemini${measured} measured

${cells}\n

`\n}\n\nfunction renderAnswers(perception: PerceptionViewModel): string {\n const rows = perception.answers.map((answer) => `\n \n ${escapeHtml(answer.query)}\n ${verdictBadge(answer)}\n ${answerHeadlineCell(answer)}\n ${\n // An unmeasured answer has no sources because there is no answer, not\n // because the engine attributed none. A 0 here would be a measured zero.\n answer.verdict === null\n ? 'not measured'\n : answer.sources.length\n }\n ${renderAnswerDetails(answer)}\n `).join('')\n const empty =\n '

No answers were recorded

Answers appear once the engine replies to a branded question.

'\n return `\n

Answers${renderInfoTip(ANSWERS_TIP)}

\n ${perception.notice ? renderNotice(perception.notice) : ''}\n ${\n perception.answers.length\n ? `

${rows}

Branded question	Verdict	What the answer said	Sources	Evidence

`\n : empty\n }\n

`\n}\n\nfunction renderConcerns(perception: PerceptionViewModel): string {\n const measured = perception.measuredAnswers\n const body = perception.concerns.length\n ? `

${escapeHtml(concern.phrase)}in ${concern.answers} of ${measured} ${\n plural(measured, 'answer', 'answers')\n }

`\n : `

${\n measured === 0\n ? 'No answer was measured, so no concern could be read out of one.'\n : `No concern was raised in the ${measured} ${plural(measured, 'answer', 'answers')} measured.`\n }

`\n return `\n

Concerns raised${\n renderInfoTip(CONCERNS_TIP)\n }

\n ${body}\n

`\n}\n\nfunction renderSourceTypes(perception: PerceptionViewModel): string {\n const share = perception.sourceTypes\n const body = share\n ? `

${\n share.entries.map((entry) =>\n ``\n ).join('')\n }

Kind of source	Answers	Share
${escapeHtml(entry.label)}	${entry.answers} of ${share.measuredAnswers}	${entry.percent}%

${\n share.unattributedAnswers > 0\n ? `

${share.unattributedAnswers} measured ${\n plural(share.unattributedAnswers, 'answer', 'answers')\n } attributed no source at all, and ${\n plural(share.unattributedAnswers, 'is', 'are')\n } excluded from the share.

`\n : ''\n }`\n : `

${\n perception.measuredAnswers === 0\n ? 'No answer was measured, so no source could be attributed.'\n : 'No measured answer attributed a source.'\n }

`\n return `\n

Sources the engine attributed${\n renderInfoTip(SOURCES_TIP)\n }

\n ${body}\n

`\n}\n\n/**\n * The waiting state, revealed by script when a check is submitted.\n *\n * A check takes about 45 seconds and execution is request-bound, so a native\n * form POST leaves the browser blocked on a navigation for the whole time with\n * nothing on screen but a disabled button. The work still happens inside one\n * request; this just stops that request being the thing the browser is painting.\n *\n * It claims only what is knowable while waiting. The domain and any questions\n * the visitor typed are theirs already, and elapsed time is counted rather than\n * guessed. There is ONE track, not a list of phases: this runner really is\n * sequential — plan, then probe, then read the answers back — but the server\n * reports no phase, so naming three of them would tell the reader which one is\n * running when nothing knows that.\n */\nfunction renderLoadingView(): string {\n return `\n `\n}\n\n/**\n * The form does two different jobs, so it gets two treatments.\n *\n * On the landing page it is the ONLY thing to do, so it is the hero: a full\n * headline, a large field, and the scope note underneath. On a result page the\n * reader already has what they came for, so it shrinks into the header as a\n * secondary action and drops the onboarding copy.\n */\nfunction renderCheckForm(model: BrandPerceptionViewModel, variant: 'hero' | 'compact' = 'compact'): string {\n if (!model.form) return ''\n const value = model.status === 'empty' ? '' : model.domain\n const verificationStatus = model.form.verificationStatus ?? (model.form.turnstileSiteKey ? 'ready' : 'not-required')\n const hasTurnstile = Boolean(model.form.turnstileSiteKey && verificationStatus === 'ready')\n const disabled = verificationStatus === 'unavailable'\n // interaction-only: the challenge still runs on load, so a token is ready at\n // submit, but the widget is drawn only if the visitor has to do something.\n const verification = hasTurnstile\n ? `

`\n : verificationStatus === 'unavailable'\n ? `

!${\n escapeHtml(\n model.form.verificationUnavailableMessage ?? 'Human verification is unavailable. Public checks are disabled.',\n )\n }

`\n : ''\n const verificationSlot = verification\n ? `

${verification}

`\n : ''\n const hero = variant === 'hero'\n const submitLabel = model.form.submitLabel ?? 'Check a brand'\n return `\n `\n}\n\nfunction brandLockup(glyphHref: string): string {\n return `

brand perception checkpowered by $\"\"$ Canonry

`\n}\n\nconst FOOTER_LINKS =\n 'npm i -g @canonry/canonryGitHub canonry.ai'\n\nfunction renderMain(model: BrandPerceptionViewModel): string {\n const glyphHref = assetUrl(DEFAULT_GLYPH_SRC, canonryGlyph)\n const globalNotice = (model.status === 'ready' ? model.notice : model.notice ?? statusCopy(model.status)) ?? undefined\n const perception = model.perception\n // Nothing has been checked yet, so a report header over two empty panels is\n // scaffolding around an absence. Lead with the one action instead.\n if (model.status === 'empty') {\n return `\n

\n \n

What does AI say about your brand?${renderInfoTip(LANDING_EXPLAINER)}

\n ${renderCheckForm(model, 'hero')}\n

\n ${renderLoadingView()}\n

\n \n

`\n }\n return `\n

\n \n

${escapeHtml(model.domain)}

\n ${renderNotice(globalNotice)}\n

\n ${\n perception\n ? `${renderVerdictSnapshot(perception)}${renderAnswers(perception)}${renderConcerns(perception)}${\n renderSourceTypes(perception)\n }`\n : '

No brand perception result

Start a check to see what an answer engine says about this brand.

'\n }\n

\n \n

`\n}\n\n/** Returns a complete document so the runtime can use it as an HTTP response body. */\nexport function renderBrandPerception(model: BrandPerceptionViewModel): string {\n const title = `${model.displayName} | ${PRODUCT_NAME}`\n return `\n\n \n \n \n \n ${escapeHtml(title)}\n \n \n \n \n \n \n ${\n model.form?.turnstileSiteKey && (model.form.verificationStatus ?? 'ready') === 'ready'\n ? ''\n : ''\n }\n \n ${renderMain(model)}\n`\n}\n\n/** Useful for a host that owns the outer document or wants an embedded fragment. */\nexport function renderBrandPerceptionBody(model: BrandPerceptionViewModel): string {\n return renderMain(model)\n}\n","label",[166,167,64],"src","ui","pulls","branches","forks","envVars",{"_175":177,"_178":26},"connections",{"_175":26,"_176":26},"count","unfilledCount",5,"emptyCount",{"_33":385,"_13":166,"_62":313,"_43":-5,"_35":36,"_158":161,"_159":94,"_37":386,"_39":26,"_65":315,"_162":-5,"_67":24,"_164":-5,"_210":387,"_17":388},{"_33":380,"_13":379,"_62":373,"_43":313,"_35":36,"_158":381,"_159":94,"_37":382,"_39":26,"_65":315,"_162":-5,"_67":24,"_164":-5,"_210":383,"_17":384},{"_33":370,"_13":371,"_62":372,"_43":373,"_35":36,"_158":374,"_159":94,"_37":375,"_39":161,"_65":66,"_162":376,"_67":24,"_164":-5,"_210":377,"_17":378},{"_33":365,"_13":364,"_62":358,"_43":313,"_35":36,"_158":366,"_159":94,"_37":367,"_39":26,"_65":315,"_162":-5,"_67":24,"_164":-5,"_210":368,"_17":369},{"_33":355,"_13":356,"_62":357,"_43":358,"_35":36,"_158":359,"_159":94,"_37":360,"_39":161,"_65":66,"_162":361,"_67":24,"_164":-5,"_210":362,"_17":363},{"_33":351,"_13":342,"_62":336,"_43":313,"_35":36,"_158":177,"_159":94,"_37":352,"_39":26,"_65":315,"_162":-5,"_67":24,"_164":-5,"_210":353,"_17":354},{"_33":343,"_13":344,"_62":345,"_43":336,"_35":36,"_158":346,"_159":94,"_37":347,"_39":161,"_65":66,"_162":348,"_67":24,"_164":-5,"_210":349,"_17":350},{"_33":333,"_13":334,"_62":335,"_43":336,"_35":36,"_158":337,"_159":94,"_37":338,"_39":161,"_65":66,"_162":339,"_67":24,"_164":-5,"_210":340,"_17":341},{"_33":328,"_13":327,"_62":321,"_43":313,"_35":36,"_158":329,"_159":94,"_37":330,"_39":26,"_65":315,"_162":-5,"_67":24,"_164":-5,"_210":331,"_17":332},{"_33":318,"_13":319,"_62":320,"_43":321,"_35":36,"_158":322,"_159":94,"_37":323,"_39":161,"_65":66,"_162":324,"_67":24,"_164":-5,"_210":325,"_17":326},{"_33":312,"_13":167,"_62":157,"_43":313,"_35":36,"_158":218,"_159":94,"_37":314,"_39":26,"_65":315,"_162":-5,"_67":24,"_164":-5,"_210":316,"_17":317},{"_33":305,"_13":306,"_62":307,"_43":157,"_35":36,"_158":120,"_159":94,"_37":308,"_39":161,"_65":66,"_162":309,"_67":24,"_164":-5,"_210":310,"_17":311},{"_33":297,"_13":298,"_62":299,"_43":157,"_35":36,"_158":300,"_159":94,"_37":301,"_39":26,"_65":66,"_162":302,"_67":24,"_164":-5,"_210":303,"_17":304},{"_33":289,"_13":290,"_62":291,"_43":157,"_35":36,"_158":292,"_159":94,"_37":293,"_39":161,"_65":66,"_162":294,"_67":24,"_164":-5,"_210":295,"_17":296},{"_33":281,"_13":282,"_62":283,"_43":157,"_35":36,"_158":284,"_159":94,"_37":285,"_39":161,"_65":66,"_162":286,"_67":24,"_164":-5,"_210":287,"_17":288},{"_33":156,"_13":64,"_62":63,"_43":157,"_35":36,"_158":72,"_159":94,"_37":278,"_39":161,"_65":66,"_162":163,"_67":24,"_164":-5,"_210":279,"_17":280},{"_33":270,"_13":271,"_62":272,"_43":157,"_35":36,"_158":273,"_159":94,"_37":274,"_39":161,"_65":66,"_162":275,"_67":24,"_164":-5,"_210":276,"_17":277},{"_33":262,"_13":263,"_62":264,"_43":157,"_35":36,"_158":265,"_159":94,"_37":266,"_39":161,"_65":66,"_162":267,"_67":24,"_164":-5,"_210":268,"_17":269},{"_33":254,"_13":255,"_62":256,"_43":-5,"_35":36,"_158":257,"_159":94,"_37":258,"_39":26,"_65":21,"_162":259,"_67":24,"_164":-5,"_210":260,"_17":261},{"_33":246,"_13":247,"_62":248,"_43":-5,"_35":36,"_158":249,"_159":94,"_37":250,"_39":161,"_65":21,"_162":251,"_67":24,"_164":-5,"_210":252,"_17":253},{"_33":238,"_13":239,"_62":240,"_43":-5,"_35":36,"_158":241,"_159":94,"_37":242,"_39":26,"_65":21,"_162":243,"_67":24,"_164":-5,"_210":244,"_17":245},{"_33":230,"_13":231,"_62":232,"_43":-5,"_35":36,"_158":233,"_159":94,"_37":234,"_39":26,"_65":21,"_162":235,"_67":24,"_164":-5,"_210":236,"_17":237},{"_33":222,"_13":223,"_62":224,"_43":-5,"_35":36,"_158":225,"_159":94,"_37":226,"_39":26,"_65":21,"_162":227,"_67":24,"_164":-5,"_210":228,"_17":229},{"_33":213,"_13":214,"_62":215,"_43":-5,"_35":36,"_158":216,"_159":94,"_37":217,"_39":218,"_65":21,"_162":219,"_67":24,"_164":-5,"_210":220,"_17":221},{"_33":204,"_13":205,"_62":92,"_43":-5,"_35":36,"_158":206,"_159":94,"_37":207,"_39":161,"_65":208,"_162":209,"_67":24,"_164":-5,"_210":211,"_17":212},"c5dd2286-3989-4d5e-9a5e-7e768f29b707","main.http.tsx",37,["D",1788474102718],"http","import { sqlite } from 'https://esm.town/v/std/sqlite/main.ts'\nimport { loadValTownConfig } from 'npm:@canonry/val-kit@0.2.0/config'\nimport { createRequestBoundDispatcher } from 'npm:@canonry/val-kit@0.2.0/jobs'\nimport { createGeminiValPerceptionProbe } from 'npm:@canonry/val-kit@0.2.0/perception'\nimport { ValSqliteCheckStore } from 'npm:@canonry/val-kit@0.2.0/storage'\nimport { createValTownApp } from './src/app/app.ts'\nimport { createPerceptionCheckRunner } from './src/jobs/perception-check.ts'\nimport type { PerceptionCheckResult } from './src/runtime/check-result.ts'\nimport {\n brandPerceptionClientScript,\n brandPerceptionStyles,\n canonryGlyph,\n canonryMark,\n createPublicCheckForm,\n emptyLandingViewModel,\n PRODUCT_NAME,\n renderBrandPerception,\n toBrandPerceptionViewModel,\n} from './src/ui/index.ts'\n\nconst config = loadValTownConfig(Deno.env.toObject())\n// Named once. Every record this val reads back is typed from here; a cast on\n// the result path downstream means the generic was dropped somewhere above it.\nconst store = new ValSqliteCheckStore(sqlite)\nawait store.initialize()\n\nconst perceptionProbe = config.geminiApiKey\n ? createGeminiValPerceptionProbe({ apiKey: config.geminiApiKey, model: config.geminiModel ?? undefined })\n : null\nconst runner = createPerceptionCheckRunner({ store, perceptionProbe, ttlMs: config.checkTtlMs })\n\nconst form = createPublicCheckForm(config)\n\nconst app = createValTownApp({\n store,\n config,\n dispatcher: createRequestBoundDispatcher(runner),\n renderPage: (record) =>\n record\n ? renderBrandPerception(toBrandPerceptionViewModel(record, { form, title: PRODUCT_NAME }))\n : renderBrandPerception(emptyLandingViewModel(form)),\n assets: {\n styles: brandPerceptionStyles,\n script: brandPerceptionClientScript,\n mark: canonryMark,\n glyph: canonryGlyph,\n },\n})\n\nexport default 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See what an AI answer engine says about your brand when someone asks it directly.\n\nFree. No signup. About 45 seconds.\n\n## What you get\n\nThree questions people actually type — \"is X legit?\", \"X reviews\", \"what are the complaints about X?\" — put to an\nanswer engine, and for each one:\n\n| | |\n|---|---|\n| **The verdict** | Recommends, cautions, mixed, or took no position. |\n| **The sentences behind it** | Copied out of the answer word for word. Never a paraphrase. |\n| **The concerns** | Short phrases the answer itself raises as drawbacks. |\n| **The sources** | What the engine attributed, by kind: the brand's own site, community, review site, news, other. |\n\nNo sentiment score. Nothing here measures feeling, so nothing here pretends to. You get what the answer said and the\nline it said it in.\n\nAn answer that took no position says so. An answer the check could not measure says that instead, and is left out of\nevery count rather than scored as a zero.\n\n## This is not a visibility check\n\nEvery question here names your brand, so the engine was always going to talk about you. The finding is what it SAID.\n\nWhether AI mentions you at all, on questions that do not name you, is a different measurement —\n[AI Visibility Check](https://github.com/Canonry/canonry) is the tool for that. The two numbers are never comparable.\n\n## MCP\n\nThis is also an MCP server. No API key.\n\n```json\n{ \"mcpServers\": { \"brand-perception-check\": { \"url\": \"https:///mcp\" } } }\n```\n\nTools: `start_check`, `get_check`, `get_brand_perception`, `self_host`, `read_skill`.\n\nThe Canonry and Aero analyst playbooks are served as MCP resources.\n\n## Limits\n\n- One engine (Gemini)\n- Three branded questions\n- One snapshot. No history, no tracking, no alerts\n\n## Canonry\n\nCanonry is the open source platform this samples. It runs on your own machine.\n\n```sh\nnpm install -g @canonry/canonry\ncanonry init\n```\n\nFour answer engines. Unlimited questions on a schedule. History, so you can watch a concern appear, spread, or go away.\nNon-brand tracking, competitor share, whole-site audits, and joins to Search Console, GA4, and Bing.\n\n[github.com/Canonry/canonry](https://github.com/Canonry/canonry) · [canonry.ai](https://canonry.ai)\n",[],[231],"74e5c8ce-cbc0-41a1-bf72-c9442a5356ee","CLAUDE.md","ef242aaa-a7bd-11f1-a3ef-1607ee4eb77e",9,["D",1788456962784],"@AGENTS.md\n",[],[239],"68040758-e7f4-4674-94e7-2f4db7366f45","AGENTS.md","f160ebaa-a7bd-11f1-bcaa-1607ee4eb77e",36,["D",1788474102620],"# Brand Perception Check (Val Town)\n\nThis directory is the exact source mirrored to the public `canonry/Brand-Perception-Check` val. Git is authoritative; do\nnot make production-only edits in Val Town because `vt push` removes remote-only files.\n\n## Naming\n\nThe Val is **Brand Perception Check**, not Canonry. It puts 3 branded questions to one engine, once, where Canonry\ntracks four engines on a schedule with no such limits. Calling this \"Canonry\" would tell a visitor, or an agent reading\n`serverInfo`, that they had the platform when they have a sample of it. So the product name, the page title, the\nwordmark, and the MCP `serverInfo.title` all say Brand Perception Check.\n\n`serverInfo.name` is the slug `brand-perception-check`, matching the sibling Val's `ai-visibility-check`. MCP's `name`\nis the identifier a client keys off and `title` is where a human reads the display name; the shared contract wrote the\ndisplay name into `name`, and splitting the two is the deviation this file records.\n\nAttribution runs the other way, and deliberately: every surface points back at the open-source project. The header\nbyline, the footer (with the install command), the MCP `initialize` instructions, the `self_host` tool, and the bundled\nskills all name Canonry and link to it. Keep that; a sample that does not lead anywhere is just a toy.\n\n## What this measures, and what it must never claim\n\n**A verdict, carried by the answer's own sentences.** Each branded answer is read back for the position it takes —\n`recommends`, `cautions`, `mixed`, `none` — and that verdict survives only when sentences copied WORD FOR WORD out of\nthe answer carry it. The model proposes and exact matching disposes; the kit's `verifyVerdict` is the arbiter, and a\nverdict with no surviving evidence collapses to `none`. The page then QUOTES those sentences. That is the whole product:\na reader who does not believe a verdict can read the line it came from.\n\n- **Never a sentiment score.** There is no number between 0 and 1 here and there must not be one. Nothing in this\n pipeline measures feeling, and a score would read as though something did.\n- **`none` is a finding; `null` is not.** \"Took no position\" says the answer described the brand and took none.\n `null` says nobody read a position out of it — a failed probe or a failed extraction — and it leaves EVERY\n denominator, not just the verdict counts. Collapsing the second into the first turns an outage into a result.\n- **Sources are \"sources the answer engine attributed\"**, typed as official / community / review / news / other. They\n are not \"where opinions about this brand come from on the web\": three answers cannot support that claim. The phrase\n may appear on the page only inside the tooltip that denies it, and `render.test.ts` enforces exactly that.\n- **Concerns are the answer's own words.** A concern is a short phrase the answer wrote as a drawback, verified against\n the prose by the same adjacent-complete-words rule the brand matcher uses. It counts once per answer.\n\n## Branded is a separate instrument, not a variant\n\nEvery question here NAMES the brand, so the engine was always going to discuss it. A mention rate over this basket\nwould read near 100% and mean nothing. AI Visibility asks NON-BRAND questions, where placement is actually decided.\n\nThe two never share a denominator, a table, or a rate, and the scope travels with every number: `scope: 'branded'` plus\na `scopeNote` on every MCP payload, and the branded framing in the section tooltips on the page. The repository's own\n\"Branded vs non-brand\" rule is where this comes from — measured on a real basket, pooling the two inverted the ranking.\n\nThis is also why the two Vals cannot share a cache. `CHECK_FINGERPRINT_NAMESPACE` is `perception-v1`, and the kit's\n`checkFingerprint(namespace, domain, userQueries)` takes it as a required argument with no default: a namespace two\nproducts shared would hand each of them the other's result and report it as a cache hit. Bump THIS constant when THIS\nVal's measured signal set changes, and never another Val's.\n\n## Boundaries\n\n- Keep the host Deno-native and web-standard. Do not import the Node Canonry server, Fastify, `better-sqlite3`, or the\n Vite dashboard.\n- **The kit is the shared core; this Val is the product surface.** `@canonry/val-kit` owns the perception instrument\n (branded planner, verdict extraction and its arbiter, source typing, aggregation, the Gemini adapter); the URL and\n Turnstile guards; both `CheckStore` implementations; check-record identity and the generic admission/lease machinery;\n MCP framing and the generated skill resources; and the design tokens and mark. What stays here is what this Val IS:\n its HTTP routes and response policy, the check runner's phase and every sentence a visitor reads, its MCP tool\n surface, and its HTML. Moving one here is not a fork: delete the local copy and import the kit, or the two drift and\n only production finds out which one shipped.\n- **This Val owns its own result schema, and the kit stores it opaquely.** `PerceptionCheckResult` and\n `CHECK_FINGERPRINT_NAMESPACE` live in `src/runtime/check-result.ts`. The kit sees them only as the `TResult` of\n `CheckRecord` — written as JSON, handed back untouched. The parameter is named once, at\n `new ValSqliteCheckStore(sqlite)` in `main.http.tsx`, and every record downstream is typed from\n there; a cast on the result path means the generic was dropped somewhere above it.\n- The browser never receives provider, Turnstile-secret, deployment, or signing credentials.\n- Anonymous work is bounded by human verification, atomic quotas, cache reuse, output clipping, and a hard provider\n deadline.\n- **Never render data the Val did not measure.** The landing page ships an empty state, not a fixture. A fabricated\n verdict about a real brand is a worse lie than a fabricated percentage, and a badge in the header does not qualify it\n for anyone who scrolls.\n- Never present the Val as the full platform. Bounds stay stated on the surface that shows the number.\n- The bundled skills are generated into the KIT (`packages/val-kit/src/mcp/skills/`), not into this directory. Edit\n `skills//` at the repository root, then run `node scripts/sync-val-town-skills.mjs` and commit the result. CI\n runs it with `--check`. Never hand-edit a generated module.\n\n## One phase, and its budget is arithmetic\n\nThere is exactly ONE phase: `createGeminiValPerceptionProbe(...).probe(...)`. No site crawl, no second track. Inside it\nthe work is strictly SEQUENTIAL — plan, then one probe wave, then one verdict extraction — so the worst cases ADD\nrather than overlap, and there is no second phase to hide a slip behind:\n\n```\nplannerTimeoutMs (10s) + 1 wave x probeTimeoutMs (20s) + verdictTimeoutMs (12s) = 42s\n <= PUBLIC_CHECK_WORK_BUDGET_MS (45s)\n```\n\nFour numbers across two packages, and nothing but `perception-budget.test.ts` connects them to the job ceiling. It\nasserts the RELATIONSHIP, not the numbers: raising `maxProbeCalls` without raising `probeConcurrency` silently\nreintroduces a second wave, and the cost shows up as a shorter per-probe deadline rather than as an error. It also pins\n`verdictTimeoutMs === VERDICT_EXTRACT_LIMITS.timeoutMs`, because two constants that happen to match are two constants\nand the budget stops being true the first time only one moves.\n\nThe whole check is five provider calls: 1 planner + 3 probes in one wave + 1 verdict extraction. That number is meant\nto be noticed when a feature adds a call — it is the check on what a public visitor can make this Val spend.\n\n## A failed check says why, and an unmeasured one is not a zero\n\n`publicProbeError` translates each of the runner's closed-set failure strings into visitor wording (\"answer engine\",\nnot \"provider\") and falls back to a generic sentence for anything it does not recognize, so a sanitizer never trusts\nits input. `perceptionPhaseError` does the same for a failure thrown before any probe ran — a phase that produced no\nrow still has to say why, or the check is unexplainable from every surface.\n\nA planning failure gets its own sentence: **\"Branded questions could not be generated for this brand.\"** That is a fact\nabout the BRAND, not about the engine, and the difference between \"try again\" and \"this will not work\".\n\n`perception-runner.test.ts` drives the REAL probe runner into each failure and then asserts none of them lands on the\ngeneric fallback. That is the guard: a new failure mode added upstream without a translation fails the suite instead of\nsilently erasing its own reason.\n\n**Status is decided from what was measured.** `complete` when every answer produced a verdict, `partial` when some did\nnot, and `failed` when `successfulChecks === 0` — a report where nothing was measured has a zero in every denominator,\nwhich is not a result with empty sections but a check that found nothing. The evidence rows survive a `failed` record,\nso the page can still say why each answer was not measured. There is no configured ceiling here, so reaching one is\nnever the reason for a `partial`.\n\nThe UI reads the same evidence rather than the stored flag. `record.status` is frozen when the check runs, so\ncorrecting the rule behind it never reaches a record already written; `hasFailedWork` looks at the rows instead, and a\nstored `partial` with nothing failed reads as `ready`.\n\n## UI\n\nMatch the Canonry dashboard's restrained evidence-first hierarchy: compact header, tables, native disclosure controls,\nvisible focus. Four sections, in this order, and `render.test.ts` pins the order:\n\n1. **Verdict snapshot** — a flat KPI row. The counts are UNBOUNDED, so there is no bar and no meter: \"2 of 3\" has no\n target to fill and a bar would invent one. The denominator is written under every number, not once at the top,\n because the number and the bound it rests on have to be read together. With nothing measured the cells read\n \"Not measured\" rather than printing four zeroes.\n2. **Answers** — one row per branded question: the question, the verdict badge, the answer's own first verified\n sentence (quoted), and the source count. A native `

` per row opens every verified sentence, the concerns,\n the typed sources, the searches the engine ran, and the full answer.\n3. **Concerns raised** — phrase plus \"in N of M answers\". An empty list is STATED, not omitted: \"no concern was raised\"\n is a finding, and a missing section reads as a broken page.\n4. **Sources the engine attributed** — by kind, counted once per answer, with the share and the unattributed remainder\n stated separately.\n\nOther rules the tests hold:\n\n- **Heavy explanation lives in tooltips, not inline prose.** Each section heading carries a CSS-only `info-tip`: a real\n `

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