[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_210":211,"_212":213},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":16,"_17":18,"_38":-7,"_124":-5,"_19":125,"_126":127,"_128":129,"_130":131,"_132":133,"_21":134,"_135":-5,"_136":-5,"_137":24,"_138":139,"_140":83,"_141":-5,"_142":-5,"_143":144},"routes/_app.x.$handle.$name.branch.$branch.history_.$",{"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":22,"_23":24,"_25":26,"_27":28,"_29":24,"_30":-5},"handle","knowledge0101010lang","name","nl-to-code-agent","branchName","main","path","analytics-dashboard.html","project",{"_32":70,"_13":14,"_36":71,"_72":-5,"_73":24,"_74":75,"_76":75,"_77":78,"_79":-5,"_30":66,"_80":81,"_82":83,"_84":-5,"_85":-5,"_86":26,"_40":41,"_87":88,"_89":90,"_91":92,"_93":-5,"_94":95,"_96":95,"_97":98,"_99":98,"_100":98,"_101":95,"_102":95,"_52":103,"_104":105,"_106":107,"_108":24},"file",{"_62":63,"_13":18,"_64":21,"_65":24,"_69":-5},"isDirectory",false,"page",0,"history",[31],"hasNext","userId",{"_32":33,"_34":35,"_36":37,"_38":39,"_40":41,"_42":43,"_44":-5,"_45":46,"_47":-5,"_48":-5,"_49":-5,"_50":-5,"_51":-5,"_52":53,"_21":54,"_55":-5,"_56":-5,"_57":-5,"_58":-5,"_59":60},"id","2021e7b3-3c95-4aa9-9825-f2501a638a4e","branchId","0153b071-368f-4208-9b3e-e84776ffec0e","createdAt",["D",1783484164189],"version",9,"orgId","019f3fe8-65fc-778f-9899-d1e31465da24","parentId","612f3a97-9ee2-4bab-a2d7-d2db3160882e","incomingCommitId","aiChatMessageId","3014d84c-ee10-4131-a0ce-ef151af3949b","aiChatThreadId","aiChatThreadUserId","aiChatThreadTitle","source","clientLabel","user",{"_32":66,"_40":41,"_11":12,"_67":68},{"_62":63,"_13":18,"_64":21,"_65":24},"pull","merge","revert","multiple","commits",[61],{"_32":33,"_34":35,"_36":37,"_38":39,"_40":41,"_42":43,"_44":-5,"_45":46,"_47":-5,"_48":-5,"_49":-5,"_50":-5,"_51":-5,"_52":53,"_21":54,"_55":-5,"_56":-5,"_57":-5},"fileId","bc334f7a-7a83-11f1-8dbf-1607ee4eb77e","fileType","deleted","327efecb-6ba4-475b-b06e-baa2ed1cf8f3","avatar","https://avatars.githubusercontent.com/u/266552520?v=4","deletedAtVersion","10654066-155c-463d-bc88-46388a1c7aba",["D",1783483833950],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","NL-to-Code Agent - turns English descriptions into code","imageUrl","pinnedFileId","0611b0ec-7a83-11f1-8a55-1607ee4eb77e","pinPreview",true,"pinMigrated","screenshotVersion","likeCount","tursoName","10654066155c463dbc8846388a1c7aba","tursoHostname","10654066155c463dbc8846388a1c7aba-val-town.aws-us-east-2.turso.io","tursoMeasuredAt",["D",1789525245864],"pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm",{"_11":12,"_123":68},"org",{"_11":12,"_118":119,"_67":68,"_30":66,"_120":121,"_122":24},"branch",{"_38":109,"_13":16,"_32":35,"_110":70,"_111":-5,"_112":-5,"_113":-5,"_114":-5,"_115":-5,"_116":14,"_117":12},"isOwned",13,"projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","displayName","knowledge0101010-lang","tier","free","canRestrictVals","profileImageUrl","adoptOrgId",{"_32":70,"_13":14,"_36":205,"_72":-5,"_73":24,"_74":75,"_76":75,"_77":78,"_79":-5,"_30":66,"_80":81,"_82":83,"_84":-5,"_85":-5,"_86":26,"_40":41,"_87":88,"_89":90,"_91":206,"_93":-5,"_94":95,"_96":95,"_97":98,"_99":98,"_100":98,"_101":95,"_102":95,"_52":207,"_104":208,"_106":209,"_108":24},"tree",[164,167,168],"files",[164,165,166,167,168],"counts",{"_153":26,"_154":155,"_156":26,"_157":158,"_159":160},"contents",[],{"_32":145,"_13":18,"_62":63,"_42":-5,"_34":35,"_146":39,"_147":83,"_36":148,"_38":26,"_64":21,"_149":150,"_65":24,"_151":-5,"_17":152},"upstreamProject","upstreamBranch","emptyEnvVars","flags",[],"hasHttpFiles","grants","orgs","customDomains",{},"9aa65419-198a-48d3-9b42-6aface389761","branchVersion","latest",["D",1783484164189],"content","\n\n\n\n\nFable-5-Traces Analytics\n\n\n\n

\n ← NL → Code Agent\n

Fable-5-Traces 데이터셋 분석

Glint-Research/Fable-5-traces — 4,665개 AI 에이전트 실행 기록 통계

\n\n

전체 4,665개 행을 병렬로 fetch 중... (약 5-15초 소요)

\n\n \n

\n\n\n\n\n","label",[18],"pulls","branches",1,"forks","envVars",{"_161":26,"_163":26},"connections",{"_161":26,"_162":26},"count","unfilledCount","emptyCount",{"_32":200,"_13":191,"_62":184,"_42":-5,"_34":35,"_146":185,"_147":83,"_36":201,"_38":26,"_64":202,"_149":-5,"_65":24,"_151":-5,"_175":203,"_17":204},{"_32":192,"_13":193,"_62":194,"_42":184,"_34":35,"_146":195,"_147":83,"_36":196,"_38":155,"_64":187,"_149":197,"_65":24,"_151":-5,"_175":198,"_17":199},{"_32":181,"_13":182,"_62":183,"_42":184,"_34":35,"_146":185,"_147":83,"_36":186,"_38":26,"_64":187,"_149":188,"_65":24,"_151":-5,"_175":189,"_17":190},{"_32":145,"_13":18,"_62":63,"_42":-5,"_34":35,"_146":39,"_147":83,"_36":178,"_38":26,"_64":21,"_149":150,"_65":24,"_151":-5,"_175":179,"_17":180},{"_32":169,"_13":170,"_62":81,"_42":-5,"_34":35,"_146":109,"_147":83,"_36":171,"_38":172,"_64":173,"_149":174,"_65":24,"_151":-5,"_175":176,"_17":177},"979de4ce-9020-48a2-864e-62cd004147a0","main.ts",["D",1783484191819],8,"http","import { Hono } from \"npm:hono@4\";\nimport { serveFile } from \"https://esm.town/v/std/utils/index.ts\";\nimport {\n loadDatasetPage,\n getDatasetInfo,\n searchDataset,\n} from \"./lib/hf-dataset.ts\";\nimport { computeAnalytics } from \"./lib/analytics.ts\";\n\nconst app = new Hono();\n\n// Pattern-matching code generator\nfunction generateCode(input: string): string {\n const q = input.toLowerCase().trim();\n\n // Function that reverses a string\n if (q.includes(\"reverse\") && q.includes(\"string\")) {\n return `function reverseString(str: string): string {\\n return str.split(\"\").reverse().join(\"\");\\n}\\n\\n// Example: reverseString(\"hello\") → \"olleh\"`;\n }\n\n // Sort an array\n if (q.includes(\"sort\") && (q.includes(\"array\") || q.includes(\"list\"))) {\n return `function sortArray(arr: T[]): T[] {\\n return [...arr].sort((a, b) =>\\n a > b ? 1 : a < b ? -1 : 0\\n );\\n}\\n\\n// Example: sortArray([3, 1, 4, 1, 5]) → [1, 1, 3, 4, 5]`;\n }\n\n // Fibonacci\n if (q.includes(\"fibonacci\") || q.includes(\"fib\")) {\n return `function fibonacci(n: number): number[] {\\n const seq = [0, 1];\\n for (let i = 2; i < n; i++) {\\n seq.push(seq[i - 1] + seq[i - 2]);\\n }\\n return seq.slice(0, n);\\n}\\n\\n// Example: fibonacci(8) → [0, 1, 1, 2, 3, 5, 8, 13]`;\n }\n\n // Factorial\n if (q.includes(\"factorial\")) {\n return `function factorial(n: number): number {\\n if (n <= 1) return 1;\\n return n * factorial(n - 1);\\n}\\n\\n// Example: factorial(5) → 120`;\n }\n\n // Palindrome check\n if (q.includes(\"palindrome\")) {\n return `function isPalindrome(str: string): boolean {\\n const cleaned = str.toLowerCase().replace(/[^a-z0-9]/g, \"\");\\n return cleaned === cleaned.split(\"\").reverse().join(\"\");\\n}\\n\\n// Example: isPalindrome(\"racecar\") → true`;\n }\n\n // Fetch API\n if (q.includes(\"fetch\") || q.includes(\"api\") || q.includes(\"http request\")) {\n return `async function fetchJSON(url: string): Promise {\\n const res = await fetch(url);\\n if (!res.ok) throw new Error(\\`HTTP \\${res.status}\\`);\\n return res.json() as Promise;\\n}\\n\\n// Example: const data = await fetchJSON(\"https://api.example.com/data\")`;\n }\n\n // Debounce\n if (q.includes(\"debounce\")) {\n return `function debounce void>(\\n fn: T,\\n delay: number\\n): (...args: Parameters) => void {\\n let timer: ReturnType;\\n return (...args: Parameters) => {\\n clearTimeout(timer);\\n timer = setTimeout(() => fn(...args), delay);\\n };\\n}`;\n }\n\n // Default: function from description\n return `// Generated from: \"${input}\"\\nfunction generatedFunction() {\\n // TODO: Implement: ${input}\\n console.log(\"TODO: ${input}\");\\n}\\n\\n// This is a template — add more patterns to the agent for better results!`;\n}\n\napp.get(\"/\", (c) => {\n const lang = c.req.query(\"lang\") ?? \"\";\n const code = lang ? generateCode(lang) : \"\";\n\n return c.html(`\n\n\n\n\nNL → Code Agent\n\n\n\n

NL → Code Agent

Describe what you want in plain English. Get TypeScript code back.

\n \n

\n \n \n \n \n \n \n \n

\n ${code ? `

Generated Code

${code.replace(//g, \">\")}

` : \"\"}\n

\n\n`);\n});\n\n// ── Dataset routes ──\n\n// Browse dataset with pagination (JSON API)\napp.get(\"/dataset\", async (c) => {\n const offset = parseInt(c.req.query(\"offset\") ?? \"0\", 10);\n const length = Math.min(parseInt(c.req.query(\"length\") ?? \"10\", 10), 100);\n const page = await loadDatasetPage(offset, length);\n return c.json(page);\n});\n\n// Dataset metadata\napp.get(\"/dataset/info\", async (c) => {\n const info = await getDatasetInfo();\n return c.json(info);\n});\n\n// Search dataset by keyword\napp.get(\"/dataset/search\", async (c) => {\n const q = c.req.query(\"q\") ?? \"\";\n const limit = parseInt(c.req.query(\"limit\") ?? \"10\", 10);\n if (!q) return c.json({ error: \"Provide ?q=keyword\" }, 400);\n const results = await searchDataset(q, limit);\n return c.json({ query: q, results });\n});\n\n// Get a specific row by index\napp.get(\"/dataset/row/:idx\", async (c) => {\n const idx = parseInt(c.req.param(\"idx\"), 10);\n const page = await loadDatasetPage(idx, 1);\n if (page.rows.length === 0) return c.json({ error: \"Row not found\" }, 404);\n return c.json(page.rows[0]);\n});\n\n// ── Analytics routes ──\n\n// JSON 분석 데이터 (전체 4,665행 fetch → 통계 계산)\napp.get(\"/dataset/analytics\", async (c) => {\n const result = await computeAnalytics();\n return c.json(result);\n});\n\n// 분석 대시보드 (HTML)\napp.get(\"/dataset/analytics/dashboard\", async (c) => {\n return await serveFile(\"analytics-dashboard.html\");\n});\n\n// Always add this for full stack traces on errors:\napp.onError((err) => Promise.reject(err));\n\nexport default app.fetch;\n","children",[],[170],["D",1783484164189],[],[18],"2f30095a-e804-4664-a765-f595141ec8aa","hf-dataset.ts","7ca9292e-7a83-11f1-b50b-1607ee4eb77e","2df540fa-e4d5-401b-b625-bc7ee89bbdac",3,["D",1783484057587],"script","// TypeScript equivalent of:\n// from datasets import load_dataset\n// ds = load_dataset(\"Glint-Research/Fable-5-traces\")\n//\n// Uses the Hugging Face datasets-server REST API:\n// https://huggingface.co/docs/datasets-server/\n//\n// Val Town runs Deno (not Python), so we fetch via HTTP instead.\n\nconst DATASET = \"Glint-Research/Fable-5-traces\";\nconst CONFIG = \"pi_agent\";\nconst SPLIT = \"train\";\nconst BASE = \"https://datasets-server.huggingface.co\";\n\nexport interface FableTrace {\n harness: string;\n session_id: string;\n prompt: string;\n messages: any[];\n tools: any[];\n metadata: any;\n sent_at: string;\n num_user_messages: number;\n num_tool_calls: number;\n trace: any;\n file_path: string;\n}\n\nexport interface HFRowsResponse {\n features: any[];\n rows: { row_idx: number; row: FableTrace }[];\n num_rows_total: number;\n num_rows_per_page: number;\n}\n\n/** Fetch a page of rows from the dataset (max 100 per call). */\nexport async function loadDatasetPage(\n offset = 0,\n length = 100,\n): Promise {\n const url = `${BASE}/rows?dataset=${encodeURIComponent(DATASET)}&config=${CONFIG}&split=${SPLIT}&offset=${offset}&length=${length}`;\n const res = await fetch(url);\n if (!res.ok) {\n throw new Error(`HF API error ${res.status}: ${await res.text()}`);\n }\n return res.json();\n}\n\n/** Get dataset metadata (splits, features, num_examples, etc.) */\nexport async function getDatasetInfo() {\n const res = await fetch(\n `${BASE}/info?dataset=${encodeURIComponent(DATASET)}&config=${CONFIG}`,\n );\n if (!res.ok) {\n throw new Error(`HF API error ${res.status}: ${await res.text()}`);\n }\n return res.json();\n}\n\n/** Load the full dataset by paginating through all rows.\n * Returns an async generator so you can iterate without loading everything\n * into memory at once.\n *\n * Usage:\n * for await (const row of loadDataset()) {\n * console.log(row.prompt);\n * }\n */\nexport async function* loadDataset(): AsyncGenerator {\n const pageSize = 100;\n let offset = 0;\n let total = Infinity;\n\n while (offset < total) {\n const page = await loadDatasetPage(offset, pageSize);\n total = page.num_rows_total;\n for (const r of page.rows) {\n yield r.row;\n }\n offset += page.rows.length;\n if (page.rows.length === 0) break;\n }\n}\n\n/** Search the dataset by keyword in the prompt field.\n * Iterates through all 4,665 rows (47 API calls).\n */\nexport async function searchDataset(\n query: string,\n limit = 10,\n): Promise<{ row_idx: number; prompt: string; session_id: string }[]> {\n const q = query.toLowerCase();\n const results: { row_idx: number; prompt: string; session_id: string }[] = [];\n\n for await (const row of loadDataset()) {\n if (row.prompt.toLowerCase().includes(q)) {\n results.push({\n row_idx: results.length,\n prompt: row.prompt.slice(0, 200),\n session_id: row.session_id,\n });\n if (results.length >= limit) break;\n }\n }\n\n return results;\n}\n",[],[191,182],"lib","227d4fa4-fbed-4ac8-ac31-a2c52f76d33c","analytics.ts","aae9f28c-7a83-11f1-a869-1607ee4eb77e",7,["D",1783484137390],"// Fable-5-traces 데이터셋 분석 모듈\n// 전체 4,665개 행을 병렬로 fetch하여 통계 추출\n\nimport { loadDatasetPage, type FableTrace } from \"./hf-dataset.ts\";\n\nexport interface AnalyticsResult {\n totalTraces: number;\n totalToolCalls: number;\n totalUserMessages: number;\n avgToolCalls: number;\n avgUserMessages: number;\n avgPromptLength: number;\n maxToolCalls: number;\n maxUserMessages: number;\n maxPromptLength: number;\n // 분포\n toolCallsBuckets: { range: string; count: number }[];\n userMessagesBuckets: { range: string; count: number }[];\n promptLengthBuckets: { range: string; count: number }[];\n // 카테고리별\n harnessDist: Record;\n modelDist: Record;\n thinkingLevelDist: Record;\n // 상위 도구\n topTools: { name: string; count: number }[];\n // 날짜별\n tracesByDate: { date: string; count: number }[];\n // turn_count 분포\n turnCountBuckets: { range: string; count: number }[];\n // 처리 시간\n fetchedAt: string;\n fetchDurationMs: number;\n}\n\n/** 전체 데이터셋을 병렬로 fetch (47개 API 호출을 배치로 처리) */\nasync function fetchAllRows(): Promise {\n const PAGE_SIZE = 100;\n const firstPage = await loadDatasetPage(0, PAGE_SIZE);\n const total = firstPage.num_rows_total;\n const numPages = Math.ceil(total / PAGE_SIZE);\n\n const rows: FableTrace[] = firstPage.rows.map((r) => r.row);\n\n // 나머지 페이지를 병렬로 fetch (10개씩 배치)\n const batchSize = 10;\n for (let i = 1; i < numPages; i += batchSize) {\n const batch = [];\n for (let j = i; j < Math.min(i + batchSize, numPages); j++) {\n batch.push(\n loadDatasetPage(j * PAGE_SIZE, PAGE_SIZE).then((p) =>\n p.rows.map((r) => r.row)\n ),\n );\n }\n const results = await Promise.all(batch);\n for (const r of results) rows.push(...r);\n }\n\n return rows;\n}\n\nfunction makeBuckets(\n values: number[],\n edges: number[],\n): { range: string; count: number }[] {\n const buckets = edges.map((e, i) => ({\n range: i === 0 ? `0-${e}` : `${edges[i - 1] + 1}-${e}`,\n count: 0,\n }));\n buckets.push({\n range: `${edges[edges.length - 1] + 1}+`,\n count: 0,\n });\n\n for (const v of values) {\n let placed = false;\n for (let i = 0; i < edges.length; i++) {\n if (v <= edges[i]) {\n buckets[i].count++;\n placed = true;\n break;\n }\n }\n if (!placed) buckets[buckets.length - 1].count++;\n }\n\n return buckets;\n}\n\nexport async function computeAnalytics(): Promise {\n const start = Date.now();\n const rows = await fetchAllRows();\n const fetchDurationMs = Date.now() - start;\n\n const totalTraces = rows.length;\n let totalToolCalls = 0;\n let totalUserMessages = 0;\n let totalPromptLength = 0;\n let maxToolCalls = 0;\n let maxUserMessages = 0;\n let maxPromptLength = 0;\n\n const toolCallsValues: number[] = [];\n const userMessagesValues: number[] = [];\n const promptLengthValues: number[] = [];\n const turnCountValues: number[] = [];\n\n const harnessDist: Record = {};\n const modelDist: Record = {};\n const thinkingLevelDist: Record = {};\n const toolNames: Record = {};\n const tracesByDate: Record = {};\n\n for (const row of rows) {\n // 기본 통계\n totalToolCalls += row.num_tool_calls;\n totalUserMessages += row.num_user_messages;\n const promptLen = row.prompt.length;\n totalPromptLength += promptLen;\n\n maxToolCalls = Math.max(maxToolCalls, row.num_tool_calls);\n maxUserMessages = Math.max(maxUserMessages, row.num_user_messages);\n maxPromptLength = Math.max(maxPromptLength, promptLen);\n\n toolCallsValues.push(row.num_tool_calls);\n userMessagesValues.push(row.num_user_messages);\n promptLengthValues.push(promptLen);\n\n // harness 분포\n const h = row.harness || \"unknown\";\n harnessDist[h] = (harnessDist[h] || 0) + 1;\n\n // metadata에서 정보 추출\n const meta = row.metadata || {};\n const model = meta.model || \"unknown\";\n modelDist[model] = (modelDist[model] || 0) + 1;\n\n const thinkingLevel = meta.thinking_level || \"unknown\";\n thinkingLevelDist[thinkingLevel] = (thinkingLevelDist[thinkingLevel] || 0) +\n 1;\n\n const turnCount = meta.turn_count ?? 0;\n turnCountValues.push(turnCount);\n\n // 도구 이름 추출\n if (Array.isArray(row.tools)) {\n for (const tool of row.tools) {\n const name = tool?.function?.name ?? tool?.name ?? \"unknown\";\n toolNames[name] = (toolNames[name] || 0) + 1;\n }\n }\n\n // 날짜별 분포\n const date = row.sent_at?.slice(0, 10) ?? \"unknown\";\n tracesByDate[date] = (tracesByDate[date] || 0) + 1;\n }\n\n // 상위 15개 도구\n const topTools = Object.entries(toolNames)\n .map(([name, count]) => ({ name, count }))\n .sort((a, b) => b.count - a.count)\n .slice(0, 15);\n\n // 날짜별 정렬\n const tracesByDateArr = Object.entries(tracesByDate)\n .map(([date, count]) => ({ date, count }))\n .sort((a, b) => a.date.localeCompare(b.date));\n\n return {\n totalTraces,\n totalToolCalls,\n totalUserMessages,\n avgToolCalls: Math.round((totalToolCalls / totalTraces) * 100) / 100,\n avgUserMessages: Math.round((totalUserMessages / totalTraces) * 100) / 100,\n avgPromptLength: Math.round((totalPromptLength / totalTraces)),\n maxToolCalls,\n maxUserMessages,\n maxPromptLength,\n toolCallsBuckets: makeBuckets(toolCallsValues, [0, 5, 10, 20, 50, 100]),\n userMessagesBuckets: makeBuckets(userMessagesValues, [1, 3, 5, 10, 20, 50]),\n promptLengthBuckets: makeBuckets(promptLengthValues, [\n 500,\n 1000,\n 2000,\n 5000,\n 10000,\n 20000,\n ]),\n turnCountBuckets: makeBuckets(turnCountValues, [1, 5, 10, 20, 50, 100]),\n harnessDist,\n modelDist,\n thinkingLevelDist,\n topTools,\n tracesByDate: tracesByDateArr,\n fetchedAt: new Date().toISOString(),\n fetchDurationMs,\n };\n}\n",[],[191,193],"f5b2d57b-faaa-4c43-a93c-2caf59f2ff91",["D",1783484057587],"directory",[165,166],[191],["D",1783483833950],["D",1789525245864],{"_11":12,"_123":68},{"_11":12,"_118":119,"_67":68,"_30":66,"_120":121,"_122":24},{"_38":109,"_13":16,"_32":35,"_110":70,"_111":-5,"_112":-5,"_113":-5,"_114":-5,"_115":-5,"_116":14,"_117":12},"ctx",{"_52":-5,"_142":243,"_244":245,"_246":247,"_248":24,"_249":24,"_250":24,"_251":83,"_252":-5,"_253":-7,"_254":-7,"_255":26,"_256":24,"_257":24,"_258":-5,"_259":24,"_260":24,"_261":24,"_262":-5},"ENV",{"_214":215,"_216":217,"_218":16,"_219":220,"_221":222,"_223":224,"_225":226,"_227":228,"_229":230,"_231":232,"_233":234,"_235":236,"_237":238,"_239":240,"_241":242},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","fdb67791413f4386b4dbf18f354029a617f6160f","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"]

Fable-5-Traces 데이터셋 분석

도구 호출 횟수 분포

사용자 메시지 수 분포

프롬프트 길이 분포 (문자 수)

턴 카운트 분포

모델 분포

사고 수준 (Thinking Level)

상위 15개 도구

날짜별 트레이스 수

NL → Code Agent

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