[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_206":207,"_208":209},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":-7,"_18":19,"_17":-7,"_156":-5,"_20":157,"_22":158,"_159":160,"_161":162,"_26":163,"_24":164,"_165":-5,"_166":-5,"_167":64,"_168":169,"_170":58,"_171":-5,"_172":-5,"_173":174},"routes/_app.x.$handle.$name.code.$",{"_11":12,"_13":14,"_15":16,"_17":-7,"_18":19,"_20":21,"_22":23,"_24":25,"_26":27,"_28":-5,"_29":-5,"_30":-5,"_31":-5,"_32":-5},"handle","knowledge0101010lang","name","nl-to-code-agent","branchName","main","version","path","analytics-dashboard.html","project",{"_33":98,"_13":14,"_44":99,"_100":-5,"_101":64,"_102":103,"_104":103,"_105":106,"_107":-5,"_108":109,"_110":55,"_111":58,"_112":-5,"_113":-5,"_114":42,"_115":116,"_117":118,"_119":120,"_121":122,"_123":-5,"_124":125,"_126":125,"_127":128,"_129":128,"_130":128,"_131":125,"_132":125,"_133":134,"_135":136,"_137":138,"_139":64},"tree",[50,51,52],"file",{"_33":34,"_35":36,"_37":38,"_13":19,"_39":40,"_41":24,"_17":42,"_43":-5,"_44":45,"_46":47,"_48":-5,"_18":49},"contents",[],"upstream","pull","ahead","behind","envVars","id","9aa65419-198a-48d3-9b42-6aface389761","branchId","0153b071-368f-4208-9b3e-e84776ffec0e","fileId","bc334f7a-7a83-11f1-8dbf-1607ee4eb77e","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","fileType",0,"parentId","createdAt",["D",1783484164189],"branchVersion",9,"label",[19],{"_33":71,"_13":72,"_37":73,"_43":-5,"_35":36,"_46":74,"_57":58,"_44":75,"_17":42,"_41":76,"_39":-5,"_63":64,"_48":-5,"_65":77,"_18":78},{"_33":34,"_13":19,"_37":38,"_43":-5,"_35":36,"_46":47,"_57":58,"_44":68,"_17":42,"_41":24,"_39":40,"_63":64,"_48":-5,"_65":69,"_18":70},{"_33":53,"_13":54,"_37":55,"_43":-5,"_35":36,"_46":56,"_57":58,"_44":59,"_17":60,"_41":61,"_39":62,"_63":64,"_48":-5,"_65":66,"_18":67},"979de4ce-9020-48a2-864e-62cd004147a0","main.ts","0611b0ec-7a83-11f1-8a55-1607ee4eb77e",13,"latest",true,["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","deleted",false,"children",[],[54],["D",1783484164189],[],[19],"f5b2d57b-faaa-4c43-a93c-2caf59f2ff91","lib","2df540fa-e4d5-401b-b625-bc7ee89bbdac",3,["D",1783484057587],"directory",[79,80],[72],{"_33":89,"_13":90,"_37":91,"_43":73,"_35":36,"_46":92,"_57":58,"_44":93,"_17":94,"_41":85,"_39":95,"_63":64,"_48":-5,"_65":96,"_18":97},{"_33":81,"_13":82,"_37":83,"_43":73,"_35":36,"_46":74,"_57":58,"_44":84,"_17":42,"_41":85,"_39":86,"_63":64,"_48":-5,"_65":87,"_18":88},"2f30095a-e804-4664-a765-f595141ec8aa","hf-dataset.ts","7ca9292e-7a83-11f1-b50b-1607ee4eb77e",["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",[],[72,82],"227d4fa4-fbed-4ac8-ac31-a2c52f76d33c","analytics.ts","aae9f28c-7a83-11f1-a869-1607ee4eb77e",7,["D",1783484137390],1,"// 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",[],[72,90],"10654066-155c-463d-bc88-46388a1c7aba",["D",1783483833950],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","NL-to-Code Agent - turns English descriptions into 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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