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\"\")\n .trim()\n .replace(/^```(?:json)?\\s*/i, \"\")\n .replace(/```\\s*$/, \"\")\n .trim();\n}\n\nfunction normalizeQuote(text: unknown): string {\n return String(text ?? \"\")\n .normalize(\"NFKD\") // decompose accents; the ASCII filter below drops the marks\n .toLowerCase()\n .replace(/[^a-z0-9@.]+/g, \" \")\n .replace(/\\s+/g, \" \")\n .trim();\n}\n\nfunction significantTokens(text: unknown): string[] {\n return normalizeQuote(text).split(\" \").filter((token) => token.length > 2);\n}\n\nexport function quoteLooksRelated(quote: unknown, sourceText: unknown): boolean {\n const q = normalizeQuote(quote);\n const s = normalizeQuote(sourceText);\n if (!q || !s) return false;\n if (s.includes(q) || q.includes(s)) return true;\n\n const quoteTokens = significantTokens(quote);\n if (quoteTokens.length < MIN_TOKENS_FOR_OVERLAP_CHECK) return false;\n const sourceTokens = new Set(significantTokens(sourceText));\n const hits = quoteTokens.filter((token) => sourceTokens.has(token)).length;\n return hits / quoteTokens.length >= QUOTE_TOKEN_OVERLAP_THRESHOLD;\n}\n\nfunction normalizeLineId(lineId: unknown): string {\n return String(lineId ?? \"\").trim().replace(/^\\[|\\]$/g, \"\").toUpperCase();\n}\n\nexport function validateCoaching(rawText: string, labelled: LabelledLine[]): Coaching {\n let parsed: any;\n try {\n parsed = JSON.parse(stripFences(rawText));\n } catch (error) {\n throw new Error(\n `Claude did not return valid JSON: ${(error as Error).message}\\n---\\n${String(rawText).slice(0, 500)}`\n );\n }\n\n const byId = new Map(labelled.map((line) => [line.id.toUpperCase(), line]));\n const dropped: DroppedItem[] = [];\n\n const keep = (items: unknown, bucket: string): T[] =>\n (Array.isArray(items) ? items : []).filter((item: any) => {\n const id = normalizeLineId(item.source_line_id);\n const line = byId.get(id);\n if (!line) {\n dropped.push({ bucket, item, reason: \"unknown_line_id\" });\n return false;\n }\n if (line.role !== \"INTERVIEWER\") {\n dropped.push({ bucket, item, reason: \"not_an_interviewer_line\" });\n return false;\n }\n if (!quoteLooksRelated(item.verbatim_quote, line.text)) {\n dropped.push({ bucket, item, reason: \"quote_not_in_line\" });\n return false;\n }\n item.source_line_id = id;\n return true;\n });\n\n const principles: Principle[] = (Array.isArray(parsed.principles) ? parsed.principles : [])\n .map((p: any) => ({\n principle: String(p.principle ?? \"\").trim(),\n verdict: PRINCIPLE_VERDICTS.has(p.verdict) ? p.verdict : \"🟡\",\n evidence: keep(p.evidence, \"principles\"),\n }))\n .filter((p: Principle) => p.principle);\n\n const score = Number.isFinite(parsed.score)\n ? Math.min(10, Math.max(0, Math.round(parsed.score)))\n : null;\n\n return {\n summary: String(parsed.summary ?? \"\").trim(),\n score,\n principles,\n strengths: keep(parsed.strengths, \"strengths\"),\n improvements: keep(parsed.improvements, \"improvements\"),\n risk_flags: keep(parsed.risk_flags, \"risk_flags\"),\n priority_action: String(parsed.priority_action ?? \"\").trim(),\n dropped,\n };\n}\n",[],[145,180],"14d0463c-bfb3-40ff-9846-dc002b60cc8b","types.ts","2f8aff1e-81c2-11f1-9145-1607ee4eb77e",["D",1784280644634],"export interface Speaker {\n display_name?: string;\n email?: string;\n matched_calendar_invitee_email?: string;\n matched_calendar_invitee?: { email?: string };\n}\n\nexport interface TranscriptEntry {\n speaker?: Speaker;\n text?: string;\n timestamp?: string;\n}\n\nexport interface Meeting {\n recording_id?: string | number;\n recording_start_time?: string;\n created_at?: string;\n url?: string;\n share_url?: string;\n transcript?: TranscriptEntry[];\n}\n\nexport interface Participant {\n email: string;\n name: string;\n words: number;\n}\n\nexport type IdentifyResult =\n | { ok: false; reason: \"transcript_too_short\" | \"no_internal_speaker\" | \"no_external_speaker\" }\n | { ok: true; interviewer: Participant; candidate: Participant; otherInternal: Participant[] };\n\nexport type Role = \"INTERVIEWER\" | \"CANDIDATE\" | \"OTHER_INTERNAL\" | \"OTHER_EXTERNAL\";\n\nexport interface LabelledLine {\n id: string;\n role: Role;\n email: string;\n name: string;\n text: string;\n line: string;\n}\n\nexport interface Metrics {\n interviewer_talk_ratio: number;\n candidate_talk_ratio: number;\n interviewer_turns: number;\n interviewer_questions: number;\n avg_words_per_interviewer_turn: number;\n longest_interviewer_monologue_words: number;\n interview_span_minutes: number | null;\n}\n\nexport interface Citation {\n source_line_id: string;\n verbatim_quote: string;\n}\n\nexport interface Strength extends Citation {\n point: string;\n}\n\nexport interface Improvement extends Citation {\n point: string;\n why_it_matters?: string;\n suggested_alternative?: string;\n}\n\nexport interface RiskFlag extends Citation {\n severity: string;\n issue: string;\n}\n\nexport interface Principle {\n principle: string;\n verdict: string;\n evidence: Citation[];\n}\n\nexport interface DroppedItem {\n bucket: string;\n item: unknown;\n reason: \"unknown_line_id\" | \"not_an_interviewer_line\" | \"quote_not_in_line\";\n}\n\nexport interface Coaching {\n summary: string;\n score: number | null;\n principles: Principle[];\n strengths: Strength[];\n improvements: Improvement[];\n risk_flags: RiskFlag[];\n priority_action: string;\n dropped: DroppedItem[];\n}\n\nexport interface RenderContext {\n interviewerName: string;\n metrics: Metrics;\n fathomUrl: string;\n}\n",[],[145,188],"c012561e-229b-4daa-9415-54270e3f97a4","svix.ts","5f532e10-81c2-11f1-b54e-1607ee4eb77e",7,["D",1784280724802],"const TOLERANCE_SECONDS = 300;\n\nexport interface SvixHeaders {\n id: string | null;\n timestamp: string | null;\n signature: string | null;\n}\n\nexport type SvixResult =\n | { ok: true }\n | { ok: false; reason: \"missing_headers\" | \"bad_timestamp\" | \"timestamp_out_of_tolerance\" | \"bad_signature\" };\n\nfunction base64ToBytes(b64: string): Uint8Array {\n const bin = atob(b64);\n const bytes = new Uint8Array(bin.length);\n for (let i = 0; i < bin.length; i++) bytes[i] = bin.charCodeAt(i);\n return bytes;\n}\n\nfunction bytesToBase64(bytes: Uint8Array): string {\n let bin = \"\";\n for (const b of bytes) bin += String.fromCharCode(b);\n return btoa(bin);\n}\n\n// Constant-time comparison. The length guard is deliberate: comparing lengths\n// leaks nothing useful, while looping over mismatched lengths would.\nfunction timingSafeEqual(a: Uint8Array, b: Uint8Array): boolean {\n if (a.length !== b.length) return false;\n let diff = 0;\n for (let i = 0; i < a.length; i++) diff |= a[i] ^ b[i];\n return diff === 0;\n}\n\nexport async function verifySvixSignature(\n secret: string,\n rawBody: string,\n headers: SvixHeaders,\n nowSeconds: number = Math.floor(Date.now() / 1000),\n): Promise {\n const { id, timestamp, signature } = headers;\n if (!id || !timestamp || !signature) return { ok: false, reason: \"missing_headers\" };\n\n if (!/^\\d+$/.test(timestamp.trim())) return { ok: false, reason: \"bad_timestamp\" };\n const ts = Number(timestamp.trim());\n if (Math.abs(nowSeconds - ts) > TOLERANCE_SECONDS) {\n return { ok: false, reason: \"timestamp_out_of_tolerance\" };\n }\n\n const key = base64ToBytes(secret.replace(/^whsec_/, \"\"));\n const cryptoKey = await crypto.subtle.importKey(\n \"raw\",\n key,\n { name: \"HMAC\", hash: \"SHA-256\" },\n false,\n [\"sign\"],\n );\n const signedContent = `${id}.${ts}.${rawBody}`;\n const mac = await crypto.subtle.sign(\"HMAC\", cryptoKey, new TextEncoder().encode(signedContent));\n const expected = new TextEncoder().encode(bytesToBase64(new Uint8Array(mac)));\n\n // webhook-signature may carry several space-separated entries like \"v1,\"\n for (const entry of signature.split(\" \")) {\n const candidate = entry.includes(\",\") ? entry.slice(entry.indexOf(\",\") + 1) : entry;\n if (timingSafeEqual(new TextEncoder().encode(candidate), expected)) return { ok: true };\n }\n return { ok: false, reason: \"bad_signature\" };\n}\n",[],[145,195],"a5db2322-903b-4efb-9006-de04d3c86b24","render.ts","59f9eecc-81c2-11f1-9c86-1607ee4eb77e",6,["D",1784280715813],"import type { Coaching, RenderContext } from \"./types.ts\";\n\nexport function renderFeedback(coaching: Coaching, context: RenderContext): string {\n const parts: string[] = [];\n parts.push(\"**🤖 Coaching IA de l'interviewer**\");\n parts.push(\n `_Feedback sur la conduite de cet entretien par **${context.interviewerName}**. ` +\n `Ce n'est pas une évaluation du candidat et ne comporte aucune recommandation d'embauche._`\n );\n parts.push(\"\");\n parts.push(coaching.summary);\n if (coaching.score != null) {\n parts.push(\"\");\n parts.push(`**Note globale : ${coaching.score}/10**`);\n }\n\n if (coaching.risk_flags.length) {\n parts.push(\"\");\n parts.push(\"### ⚠️ Signalements\");\n for (const flag of coaching.risk_flags) {\n parts.push(`- **${flag.severity.toUpperCase()}** — ${flag.issue}`);\n parts.push(` > \"${flag.verbatim_quote}\" _(${flag.source_line_id})_`);\n }\n }\n\n const principles = coaching.principles || [];\n if (principles.length) {\n parts.push(\"\");\n parts.push(\"### Évaluation des 4 principes\");\n for (const p of principles) {\n parts.push(`- ${p.verdict} **${p.principle}**`);\n for (const ev of p.evidence || []) {\n parts.push(` > \"${ev.verbatim_quote}\" _(${ev.source_line_id})_`);\n }\n }\n }\n\n if (coaching.strengths.length) {\n parts.push(\"\");\n parts.push(\"### Points forts\");\n for (const item of coaching.strengths) {\n parts.push(`- ${item.point}`);\n parts.push(` > \"${item.verbatim_quote}\" _(${item.source_line_id})_`);\n }\n }\n\n if (coaching.improvements.length) {\n parts.push(\"\");\n parts.push(\"### Axes d'amélioration\");\n for (const item of coaching.improvements) {\n parts.push(`- **${item.point}** — ${item.why_it_matters || \"\"}`.trimEnd());\n parts.push(` > \"${item.verbatim_quote}\" _(${item.source_line_id})_`);\n if (item.suggested_alternative) parts.push(` Essaie plutôt : _\"${item.suggested_alternative}\"_`);\n }\n }\n\n if (coaching.priority_action) {\n parts.push(\"\");\n parts.push(`**🎯 Action prioritaire pour le prochain entretien :** ${coaching.priority_action}`);\n }\n\n parts.push(\"\");\n parts.push(\n `_Mesuré : l'interviewer a prononcé ${Math.round(context.metrics.interviewer_talk_ratio * 100)} % des mots ` +\n `et posé ${context.metrics.interviewer_questions} questions._`\n );\n if (context.fathomUrl) parts.push(`_Enregistrement : ${context.fathomUrl}_`);\n\n return parts.join(\"\\n\");\n}\n",[],[145,203],"89e8e41d-5c79-4691-8aa7-08816d3ab1f0","participants.ts","35d7d716-81c2-11f1-896f-1607ee4eb77e",3,["D",1784280655207],"import { isInternal, speakerEmail, countWords } from \"./lib.ts\";\nimport type { IdentifyResult, Meeting, Participant } from \"./types.ts\";\n\nexport const MIN_TRANSCRIPT_ENTRIES = 10;\n\nexport function identifyParticipants(meeting: Meeting | null | undefined): IdentifyResult {\n const transcript = Array.isArray(meeting && meeting.transcript) ? meeting!.transcript! : [];\n if (transcript.length < MIN_TRANSCRIPT_ENTRIES) {\n return { ok: false, reason: \"transcript_too_short\" };\n }\n\n const byEmail = new Map();\n for (const entry of transcript) {\n const email = speakerEmail(entry);\n if (!email) continue;\n const existing = byEmail.get(email) || {\n email,\n name: (entry.speaker && entry.speaker.display_name) || \"Unknown\",\n words: 0,\n };\n existing.words += countWords(entry.text);\n byEmail.set(email, existing);\n }\n\n const speakers = [...byEmail.values()];\n const byWordsDesc = (a: Participant, b: Participant) => b.words - a.words;\n const internal = speakers.filter((s) => isInternal(s.email)).sort(byWordsDesc);\n const external = speakers.filter((s) => !isInternal(s.email)).sort(byWordsDesc);\n\n if (internal.length === 0) return { ok: false, reason: \"no_internal_speaker\" };\n if (external.length === 0) return { ok: false, reason: \"no_external_speaker\" };\n\n return {\n ok: true,\n interviewer: internal[0],\n candidate: external[0],\n otherInternal: internal.slice(1),\n };\n}\n",[],[145,211],"414d9a48-4f3e-4f03-89f6-c38d5f2590a4","notion-blocks.ts","69ae91ec-81c2-11f1-9851-1607ee4eb77e",8,["D",1784280742135],"// Converts the rendered French Markdown into Notion blocks.\n// Supported subset (all that render.ts emits): ### headings, \"- \" bullets,\n// \"> \" quotes (possibly indented), plain paragraphs, and inline **bold** / _italic_.\n\nconst MAX_TEXT_LENGTH = 2000; // Notion limit per rich_text element\n\nexport interface RichText {\n type: \"text\";\n text: { content: string };\n annotations?: { bold?: boolean; italic?: boolean };\n}\n\nexport interface NotionBlock {\n object: \"block\";\n type: string;\n [key: string]: unknown;\n}\n\nfunction chunk(content: string): string[] {\n const chunks: string[] = [];\n for (let i = 0; i < content.length; i += MAX_TEXT_LENGTH) {\n chunks.push(content.slice(i, i + MAX_TEXT_LENGTH));\n }\n return chunks.length ? chunks : [\"\"];\n}\n\nexport function parseInline(text: string): RichText[] {\n const parts: RichText[] = [];\n // Tokenize on **bold** and _italic_ spans; everything else is plain.\n const pattern = /(\\*\\*[^*]+\\*\\*|_[^_]+_)/g;\n let lastIndex = 0;\n for (const match of text.matchAll(pattern)) {\n const index = match.index ?? 0;\n if (index > lastIndex) {\n for (const c of chunk(text.slice(lastIndex, index))) {\n parts.push({ type: \"text\", text: { content: c } });\n }\n }\n const token = match[0];\n if (token.startsWith(\"**\")) {\n for (const c of chunk(token.slice(2, -2))) {\n parts.push({ type: \"text\", text: { content: c }, annotations: { bold: true } });\n }\n } else {\n for (const c of chunk(token.slice(1, -1))) {\n parts.push({ type: \"text\", text: { content: c }, annotations: { italic: true } });\n }\n }\n lastIndex = index + token.length;\n }\n if (lastIndex < text.length) {\n for (const c of chunk(text.slice(lastIndex))) {\n parts.push({ type: \"text\", text: { content: c } });\n }\n }\n return parts.length ? parts : [{ type: \"text\", text: { content: \"\" } }];\n}\n\nfunction block(type: string, text: string): NotionBlock {\n return { object: \"block\", type, [type]: { rich_text: parseInline(text) } };\n}\n\nexport function markdownToNotionBlocks(markdown: string): NotionBlock[] {\n const blocks: NotionBlock[] = [];\n for (const rawLine of markdown.split(\"\\n\")) {\n const line = rawLine.trim();\n if (!line) continue;\n if (line.startsWith(\"### \")) {\n blocks.push(block(\"heading_3\", line.slice(4)));\n } else if (line.startsWith(\"> \")) {\n blocks.push(block(\"quote\", line.slice(2)));\n } else if (line.startsWith(\"- \")) {\n blocks.push(block(\"bulleted_list_item\", line.slice(2)));\n } else {\n blocks.push(block(\"paragraph\", line));\n }\n }\n return blocks;\n}\n",[],[145,219],"e3ff3f8f-9ee2-42a0-896a-7b0f11aff754","metrics.ts","41c79fac-81c2-11f1-8bfd-1607ee4eb77e",4,["D",1784280675234],"import { speakerEmail, countWords, timestampToSeconds, round, isInternal } from \"./lib.ts\";\nimport type { LabelledLine, Metrics, Role, TranscriptEntry } from \"./types.ts\";\n\ninterface Turn {\n email: string;\n words: number;\n entries: TranscriptEntry[];\n}\n\nexport function toTurns(transcript: TranscriptEntry[]): Turn[] {\n const turns: Turn[] = [];\n for (const entry of transcript) {\n const email = speakerEmail(entry);\n const words = countWords(entry.text);\n const last = turns[turns.length - 1];\n if (last && last.email === email) {\n last.words += words;\n last.entries.push(entry);\n } else {\n turns.push({ email, words, entries: [entry] });\n }\n }\n return turns;\n}\n\nexport function labelTranscript(\n transcript: TranscriptEntry[],\n interviewerEmail: string,\n candidateEmail: string,\n): LabelledLine[] {\n return transcript.map((entry, index) => {\n const email = speakerEmail(entry);\n let role: Role;\n if (email === interviewerEmail) role = \"INTERVIEWER\";\n else if (email === candidateEmail) role = \"CANDIDATE\";\n else if (isInternal(email)) role = \"OTHER_INTERNAL\";\n else role = \"OTHER_EXTERNAL\";\n\n const id = `L${index + 1}`;\n const name = (entry.speaker && entry.speaker.display_name) || \"Unknown\";\n const text = String(entry.text ?? \"\");\n return { id, role, email, name, text, line: `[${id}] [${role} ${name}]: ${text}` };\n });\n}\n\nexport function computeMetrics(\n transcript: TranscriptEntry[],\n interviewerEmail: string,\n candidateEmail: string,\n): Metrics {\n const entries = Array.isArray(transcript) ? transcript : [];\n\n let interviewerWords = 0;\n let candidateWords = 0;\n let totalWords = 0;\n let interviewerQuestions = 0;\n\n for (const entry of entries) {\n const email = speakerEmail(entry);\n const words = countWords(entry.text);\n totalWords += words;\n if (email === interviewerEmail) {\n interviewerWords += words;\n if (String(entry.text ?? \"\").includes(\"?\")) interviewerQuestions += 1;\n } else if (email === candidateEmail) {\n candidateWords += words;\n }\n }\n\n const interviewerTurns = toTurns(entries).filter((t) => t.email === interviewerEmail);\n const longestMonologue = interviewerTurns.reduce((max, t) => Math.max(max, t.words), 0);\n\n const first = entries.length ? timestampToSeconds(entries[0].timestamp) : null;\n const last = entries.length ? timestampToSeconds(entries[entries.length - 1].timestamp) : null;\n const span = first !== null && last !== null ? Math.round((last - first) / 60) : null;\n\n return {\n interviewer_talk_ratio: totalWords ? round(interviewerWords / totalWords, 2) : 0,\n candidate_talk_ratio: totalWords ? round(candidateWords / totalWords, 2) : 0,\n interviewer_turns: interviewerTurns.length,\n interviewer_questions: interviewerQuestions,\n avg_words_per_interviewer_turn: interviewerTurns.length\n ? round(interviewerWords / interviewerTurns.length, 1)\n : 0,\n longest_interviewer_monologue_words: longestMonologue,\n interview_span_minutes: span,\n };\n}\n",[],[145,227],"9e574286-21e0-4ed3-9d03-3816c875a57d","lib.ts","3304c904-81c2-11f1-9c86-1607ee4eb77e",2,["D",1784280650418],"import type { TranscriptEntry } from \"./types.ts\";\n\nexport const RIOT_EMAIL_DOMAIN = \"tryriot.com\";\n\nexport function normalizeEmail(email: unknown): string {\n return String(email ?? \"\").trim().toLowerCase();\n}\n\nexport function isInternal(email: unknown): boolean {\n const normalized = normalizeEmail(email);\n const atCount = (normalized.match(/@/g) || []).length;\n if (atCount !== 1) return false;\n return normalized.split(\"@\").pop() === RIOT_EMAIL_DOMAIN;\n}\n\nexport function speakerEmail(entry: TranscriptEntry | null | undefined): string {\n const speaker = (entry && entry.speaker) || {};\n return normalizeEmail(\n speaker.matched_calendar_invitee_email ||\n speaker.email ||\n (speaker.matched_calendar_invitee && speaker.matched_calendar_invitee.email)\n );\n}\n\nexport function countWords(text: unknown): number {\n return String(text ?? \"\").trim().split(/\\s+/).filter(Boolean).length;\n}\n\nexport function timestampToSeconds(ts: unknown): number | null {\n const raw = String(ts ?? \"\").trim();\n if (!raw) return null;\n const segments = raw.split(\":\");\n if (segments.some((seg) => !/^\\d+$/.test(seg))) return null;\n const parts = segments.map(Number);\n if (parts.length === 3) return parts[0] * 3600 + parts[1] * 60 + parts[2];\n if (parts.length === 2) return parts[0] * 60 + parts[1];\n return null;\n}\n\nexport function round(n: number, dp: number): number {\n const factor = 10 ** dp;\n return Math.round(n * factor) / factor;\n}\n",[],[145,235],["D",1784280644634],[161,162,163,164,165,166,167,168],[145],["D",1784280622128],["D",1789527031073],{"_105":104,"_113":109},{"_105":104,"_106":107,"_108":109,"_18":60,"_110":111,"_112":25},{"_13":94,"_32":95,"_48":42,"_96":49,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":50,"_103":104},"ctx",{"_87":-5,"_133":283,"_284":285,"_286":287,"_288":25,"_289":25,"_290":25,"_291":64,"_292":-5,"_293":-7,"_294":-7,"_295":68,"_296":25,"_297":25,"_298":-5,"_299":25,"_300":25,"_301":25,"_302":-5},"ENV",{"_254":255,"_256":257,"_258":95,"_259":260,"_261":262,"_263":264,"_265":266,"_267":268,"_269":270,"_271":272,"_273":274,"_275":276,"_277":278,"_279":280,"_281":282},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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