[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_205":206,"_207":208},"routes/_app.x.$handle.$name",{"_11":12,"_13":14,"_15":16,"_18":19,"_17":-7,"_156":-5,"_20":157,"_22":158,"_159":160,"_161":162,"_26":163,"_24":164,"_165":-5,"_166":-5,"_167":51,"_168":169,"_170":43,"_171":-5,"_172":-5,"_173":174},"routes/_app.x.$handle.$name.branch.$branch.code.$",{"_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","kingcobra","name","ai-candidate-analyzer","branchName","main","version","path","index.html","project",{"_33":98,"_13":14,"_44":99,"_100":-5,"_101":51,"_102":103,"_104":103,"_105":106,"_107":-5,"_108":109,"_110":61,"_111":43,"_112":-5,"_113":-5,"_114":46,"_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":51},"tree",[54,55,56,57,58],"file",{"_33":34,"_13":19,"_35":36,"_37":-5,"_38":39,"_40":41,"_42":43,"_44":45,"_17":46,"_47":24,"_48":49,"_50":51,"_52":-5,"_18":53},"contents",[],"upstream","pull","ahead","behind","envVars","id","626c3e64-88b7-4d75-8d89-1adb6d5eedd8","fileId","61a694f8-64e0-11f1-9b7b-1607ee4eb77e","parentId","branchId","5c630afb-10e9-4479-9701-a8cb09870273","branchVersion",3,"latest",true,"createdAt",["D",1781105029884],0,"fileType","content","\n\n\n \n \n AI Candidate Analyzer\n \n \n \n \n \n \n\n\n

\n \n\n\n","deleted",false,"label",[19],{"_33":90,"_13":91,"_35":92,"_37":-5,"_38":39,"_40":93,"_42":43,"_44":94,"_17":46,"_47":78,"_48":95,"_50":51,"_52":-5,"_67":96,"_18":97},{"_33":82,"_13":83,"_35":84,"_37":-5,"_38":39,"_40":85,"_42":43,"_44":86,"_17":46,"_47":78,"_48":87,"_50":51,"_52":-5,"_67":88,"_18":89},{"_33":73,"_13":74,"_35":75,"_37":-5,"_38":39,"_40":76,"_42":43,"_44":77,"_17":64,"_47":78,"_48":79,"_50":51,"_52":-5,"_67":80,"_18":81},{"_33":34,"_13":19,"_35":36,"_37":-5,"_38":39,"_40":41,"_42":43,"_44":70,"_17":46,"_47":24,"_48":49,"_50":51,"_52":-5,"_67":71,"_18":72},{"_33":59,"_13":60,"_35":61,"_37":-5,"_38":39,"_40":62,"_42":43,"_44":63,"_17":64,"_47":65,"_48":66,"_50":51,"_52":-5,"_67":68,"_18":69},"15b0e577-06b6-4727-bdae-19756932e2ec","main.ts","58207a02-64e0-11f1-8621-1607ee4eb77e",7,["D",1781105291276],2,"http","import { analyzeGitHub } from \"./analyzer.ts\";\nimport { generateAIReport } from \"./ai.ts\";\nimport { readFile } from \"https://esm.town/v/std/utils/index.ts\";\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n\n // API: analyze a GitHub profile\n if (url.pathname === \"/api/analyze\" && req.method === \"POST\") {\n try {\n const { input } = await req.json();\n if (!input || typeof input !== \"string\") {\n return Response.json({ error: \"Provide a GitHub username or URL.\" }, { status: 400 });\n }\n const result = await analyzeGitHub(input);\n // AI narrative is best-effort: if OpenRouter fails, still return the stats\n let aiReport = null;\n let aiError = null;\n try {\n aiReport = await generateAIReport(result);\n } catch (e) {\n aiError = String(e instanceof Error ? e.message : e);\n }\n return Response.json({ ...result, aiReport, aiError });\n } catch (err) {\n return Response.json({ error: String(err instanceof Error ? err.message : err) }, { status: 500 });\n }\n }\n\n // Serve the frontend\n const html = await readFile(\"/index.html\", import.meta.url);\n return new Response(html, { headers: { \"Content-Type\": \"text/html; charset=utf-8\" } });\n}\n","children",[],[60],["D",1781105029884],[],[19],"b3dea0aa-5e01-4258-98a7-8770c38feda7","app.jsx","784483c8-64e0-11f1-b476-1607ee4eb77e",9,["D",1781105329948],"script","const { useState } = React;\nconst {\n RadarChart, Radar, PolarGrid, PolarAngleAxis, PolarRadiusAxis,\n BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Cell,\n} = Recharts;\n\nconst REC_COLORS = {\n \"Strong Hire\": \"bg-emerald-500\",\n \"Hire\": \"bg-green-500\",\n \"Consider\": \"bg-amber-500\",\n \"Needs Improvement\": \"bg-orange-500\",\n \"Not Recommended\": \"bg-red-500\",\n};\n\nfunction ScoreCard({ label, value }) {\n const color = value >= 70 ? \"text-emerald-400\" : value >= 45 ? \"text-amber-400\" : \"text-red-400\";\n return (\n

{label}

{value}

\n );\n}\n\nfunction InsightList({ title, items, color }) {\n if (!items || !items.length) return null;\n return (\n

{title}

\n •\n {it}\n

\n );\n}\n\nfunction App() {\n const [input, setInput] = useState(\"\");\n const [loading, setLoading] = useState(false);\n const [error, setError] = useState(\"\");\n const [data, setData] = useState(null);\n const [dark, setDark] = useState(true);\n\n const toggleDark = () => {\n document.documentElement.classList.toggle(\"dark\");\n setDark(d => !d);\n };\n\n async function analyze(e) {\n e.preventDefault();\n setLoading(true); setError(\"\"); setData(null);\n try {\n const res = await fetch(\"/api/analyze\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({ input }),\n });\n const json = await res.json();\n if (!res.ok) throw new Error(json.error || \"Analysis failed\");\n setData(json);\n } catch (err) {\n setError(err.message);\n } finally {\n setLoading(false);\n }\n }\n\n const radarData = data\n ? Object.entries(data.scores).map(([k, v]) => ({ metric: k, score: v }))\n : [];\n const langData = data\n ? Object.entries(data.languages).sort((a, b) => b[1] - a[1]).slice(0, 8)\n .map(([name, count]) => ({ name, count }))\n : [];\n\n return (\n

AI Candidate Analyzer

GitHub recruitment intelligence

\n \n

\n\n

\n setInput(e.target.value)}\n placeholder=\"Enter GitHub username or URL, e.g. https://github.com/torvalds\"\n className=\"flex-1 px-4 py-3 rounded-xl bg-white dark:bg-slate-900 border border-slate-300 dark:border-slate-700 focus:outline-none focus:ring-2 focus:ring-indigo-500\"\n />\n \n {loading ? \"Analyzing…\" : \"Analyze\"}\n \n \n\n {error &&

{error}

}\n\n {loading && (\n

\n {[...Array(6)].map((_, i) => (\n

\n ))}\n

\n )}\n\n {data && (\n

\n {/* Profile + recommendation */}\n

{data.profile.name || data.profile.username}

@{data.profile.username} · {data.profile.location || \"—\"}

\n {data.profile.bio &&

{data.profile.bio}

}\n

\n {data.profile.followers} followers\n {data.profile.publicRepos} repos\n {data.profile.accountAgeYears}y on GitHub\n

\n {data.recommendation}\n

\n\n {/* Executive summary */}\n

Executive Summary

{data.summary}

\n\n {/* Score cards */}\n

\n {Object.entries(data.scores).map(([k, v]) => )}\n

\n\n {/* Charts */}\n

Score Breakdown

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

Language Distribution

\n \n \n \n \n \n \n {langData.map((_, i) => )}\n \n \n \n

\n\n {/* AI Insights */}\n {data.aiReport && (\n

\n 🤖\n

AI Insights

\n OpenRouter\n

{data.aiReport.executiveSummary}

\n \n \n \n \n

\n AI Recommendation\n \n {data.aiReport.hiringRecommendation}\n \n

{data.aiReport.reasoning}

\n )}\n {data.aiError && (\n

\n AI narrative unavailable: {data.aiError}\n

\n )}\n\n {/* Detected skills */}\n

Detected Skills

\n {data.detectedSkills.length ? data.detectedSkills.map(s => (\n {s}\n )) : No specific skill keywords detected.}\n

\n\n {/* Top repos */}\n

Top Repositories

\n {data.repos.map(r => (\n \n

\n {r.name}\n ★ {r.stars}\n

{r.description || \"No description\"}

\n {r.language || \"—\"}\n \n ))}\n

\n )}\n\n {!data && !loading && !error && (\n

Enter a GitHub profile to generate an instant candidate report.

Try torvalds, gaearon, or any username.

\n )}\n

\n );\n}\n\nReactDOM.createRoot(document.getElementById(\"root\")).render();\n",[],[74],"bb735697-4ff9-4b70-8644-3590b8b3106f","analyzer.ts","53d7745a-64e0-11f1-a429-1607ee4eb77e",1,["D",1781105006714],"// Core GitHub analysis + scoring logic (no API keys needed)\n\nconst SKILL_MAP: Record = {\n Frontend: [\"react\", \"next\", \"vue\", \"angular\", \"svelte\", \"tailwindcss\", \"bootstrap\", \"javascript\", \"typescript\", \"html\", \"css\"],\n Backend: [\"node\", \"express\", \"django\", \"flask\", \"spring\", \"asp.net\", \"fastapi\", \"nestjs\", \"go\", \"rust\", \"php\", \"ruby\"],\n Database: [\"mysql\", \"postgresql\", \"mongodb\", \"redis\", \"supabase\", \"sqlite\", \"prisma\"],\n Cloud: [\"aws\", \"azure\", \"gcp\", \"google-cloud\", \"vercel\", \"netlify\"],\n DevOps: [\"docker\", \"kubernetes\", \"github-actions\", \"jenkins\", \"terraform\", \"ansible\"],\n AI: [\"tensorflow\", \"pytorch\", \"langchain\", \"openai\", \"huggingface\", \"transformers\", \"scikit-learn\", \"pandas\", \"numpy\"],\n Mobile: [\"flutter\", \"react-native\", \"kotlin\", \"swift\", \"android\", \"ios\"],\n};\n\nconst LANGUAGE_TO_CATEGORY: Record = {\n JavaScript: \"Frontend\", TypeScript: \"Frontend\", HTML: \"Frontend\", CSS: \"Frontend\", Vue: \"Frontend\",\n Python: \"AI\", Java: \"Backend\", Go: \"Backend\", Rust: \"Backend\", PHP: \"Backend\", Ruby: \"Backend\", \"C#\": \"Backend\",\n Dart: \"Mobile\", Kotlin: \"Mobile\", Swift: \"Mobile\",\n};\n\nexport interface AnalysisResult {\n profile: any;\n repos: any[];\n languages: Record;\n skillCategories: Record;\n detectedSkills: string[];\n scores: Record;\n recommendation: string;\n summary: string;\n}\n\nasync function gh(path: string) {\n const headers: Record = {\n Accept: \"application/vnd.github+json\",\n \"User-Agent\": \"ai-candidate-analyzer\",\n };\n const token = Deno.env.get(\"GITHUB_TOKEN\");\n if (token) headers.Authorization = `Bearer ${token}`;\n const res = await fetch(`https://api.github.com${path}`, { headers });\n if (!res.ok) throw new Error(`GitHub API ${res.status}: ${await res.text()}`);\n return res.json();\n}\n\nexport function extractUsername(input: string): string {\n const trimmed = input.trim().replace(/\\/+$/, \"\");\n const match = trimmed.match(/github\\.com\\/([^\\/?#]+)/i);\n return (match ? match[1] : trimmed).replace(/^@/, \"\");\n}\n\nfunction clamp(n: number) {\n return Math.max(0, Math.min(100, Math.round(n)));\n}\n\nexport async function analyzeGitHub(usernameOrUrl: string): Promise {\n const username = extractUsername(usernameOrUrl);\n const profile = await gh(`/users/${username}`);\n const reposRaw = await gh(`/users/${username}/repos?per_page=100&sort=updated`);\n\n const repos = reposRaw\n .filter((r: any) => !r.fork)\n .map((r: any) => ({\n name: r.name,\n description: r.description,\n language: r.language,\n stars: r.stargazers_count,\n forks: r.forks_count,\n watchers: r.watchers_count,\n openIssues: r.open_issues_count,\n size: r.size,\n updatedAt: r.updated_at,\n hasDescription: !!r.description,\n url: r.html_url,\n }));\n\n // Aggregate languages\n const languages: Record = {};\n for (const r of repos) {\n if (r.language) languages[r.language] = (languages[r.language] || 0) + 1;\n }\n\n // Skill category detection\n const skillCategories: Record = {};\n const detectedSkills = new Set();\n const haystack = repos\n .map((r) => `${r.name} ${r.description || \"\"} ${r.language || \"\"}`.toLowerCase())\n .join(\" \");\n for (const [cat, keywords] of Object.entries(SKILL_MAP)) {\n let hits = 0;\n for (const kw of keywords) {\n if (haystack.includes(kw)) {\n hits++;\n detectedSkills.add(kw);\n }\n }\n skillCategories[cat] = hits;\n }\n for (const [lang, count] of Object.entries(languages)) {\n const cat = LANGUAGE_TO_CATEGORY[lang];\n if (cat) skillCategories[cat] = (skillCategories[cat] || 0) + count;\n }\n\n // Scoring\n const totalStars = repos.reduce((s, r) => s + r.stars, 0);\n const totalForks = repos.reduce((s, r) => s + r.forks, 0);\n const documented = repos.filter((r) => r.hasDescription).length;\n const accountAgeYears =\n (Date.now() - new Date(profile.created_at).getTime()) / (1000 * 60 * 60 * 24 * 365);\n const recentlyActive = repos.filter(\n (r) => (Date.now() - new Date(r.updatedAt).getTime()) / (1000 * 60 * 60 * 24) < 180,\n ).length;\n\n const githubScore = clamp(\n Math.log2(repos.length + 1) * 12 +\n Math.log2(totalStars + 1) * 6 +\n Math.log2(profile.followers + 1) * 5,\n );\n const technicalScore = clamp(\n Object.values(skillCategories).filter((v) => v > 0).length * 10 +\n Object.keys(languages).length * 5,\n );\n const portfolioScore = clamp(\n repos.length * 4 + Object.keys(languages).length * 6 + Math.log2(totalForks + 1) * 5,\n );\n const documentationScore = clamp(repos.length ? (documented / repos.length) * 100 : 0);\n const activityScore = clamp(repos.length ? (recentlyActive / repos.length) * 100 : 0);\n const employabilityScore = clamp(\n technicalScore * 0.35 + githubScore * 0.3 + portfolioScore * 0.2 + activityScore * 0.15,\n );\n\n const scores = {\n Technical: technicalScore,\n GitHub: githubScore,\n Portfolio: portfolioScore,\n Documentation: documentationScore,\n Activity: activityScore,\n Employability: employabilityScore,\n };\n\n let recommendation = \"Not Recommended\";\n if (employabilityScore >= 80) recommendation = \"Strong Hire\";\n else if (employabilityScore >= 65) recommendation = \"Hire\";\n else if (employabilityScore >= 50) recommendation = \"Consider\";\n else if (employabilityScore >= 35) recommendation = \"Needs Improvement\";\n\n const summary =\n `${profile.name || username} has ${repos.length} public repositories using ` +\n `${Object.keys(languages).length} languages, ${totalStars} total stars, and ` +\n `${profile.followers} followers over ${accountAgeYears.toFixed(1)} years on GitHub. ` +\n `Top skill areas: ${Object.entries(skillCategories)\n .filter(([, v]) => v > 0)\n .sort((a, b) => b[1] - a[1])\n .slice(0, 3)\n .map(([k]) => k)\n .join(\", \") || \"general development\"}.`;\n\n return {\n profile: {\n name: profile.name,\n username: profile.login,\n bio: profile.bio,\n company: profile.company,\n website: profile.blog,\n location: profile.location,\n followers: profile.followers,\n following: profile.following,\n avatar: profile.avatar_url,\n accountAgeYears: +accountAgeYears.toFixed(1),\n publicRepos: profile.public_repos,\n },\n repos: repos.sort((a, b) => b.stars - a.stars).slice(0, 12),\n languages,\n skillCategories,\n detectedSkills: [...detectedSkills],\n scores,\n recommendation,\n summary,\n };\n}\n",[],[83],"3e79e528-e3ef-49ac-ad20-b6cc5cd224cb","ai.ts","f1c2933e-64e0-11f1-804e-1607ee4eb77e",5,["D",1781105271660],"// AI service layer — OpenRouter integration for narrative report generation\n\nconst MODEL = \"openai/gpt-4o-mini\"; // fast + cheap; change to any OpenRouter model\n\nexport interface AIReport {\n executiveSummary: string;\n strengths: string[];\n weaknesses: string[];\n missingSkills: string[];\n improvementSuggestions: string[];\n hiringRecommendation: string;\n reasoning: string;\n}\n\nexport async function generateAIReport(analysis: any): Promise {\n const apiKey = Deno.env.get(\"OPENROUTER_API_KEY\");\n if (!apiKey) throw new Error(\"OPENROUTER_API_KEY is not set.\");\n\n // Compact the analysis so we don't blow the context window\n const compact = {\n profile: {\n name: analysis.profile.name,\n username: analysis.profile.username,\n bio: analysis.profile.bio,\n company: analysis.profile.company,\n location: analysis.profile.location,\n followers: analysis.profile.followers,\n publicRepos: analysis.profile.publicRepos,\n accountAgeYears: analysis.profile.accountAgeYears,\n },\n languages: analysis.languages,\n skillCategories: analysis.skillCategories,\n detectedSkills: analysis.detectedSkills,\n scores: analysis.scores,\n topRepos: analysis.repos.slice(0, 10).map((r: any) => ({\n name: r.name,\n description: r.description,\n language: r.language,\n stars: r.stars,\n forks: r.forks,\n })),\n };\n\n const system =\n \"You are a senior technical recruiter and engineering manager. \" +\n \"Analyze the candidate's GitHub data and produce an objective, evidence-based hiring assessment. \" +\n \"Be specific and reference concrete signals (languages, repos, stars, activity). \" +\n \"Respond ONLY with valid JSON matching the requested schema. No markdown, no prose outside JSON.\";\n\n const user =\n \"Candidate GitHub analysis data:\\n\" +\n JSON.stringify(compact, null, 2) +\n \"\\n\\nReturn JSON with exactly these keys:\\n\" +\n `{\n \"executiveSummary\": \"2-3 sentence overview\",\n \"strengths\": [\"...\"],\n \"weaknesses\": [\"...\"],\n \"missingSkills\": [\"skills/areas not evident that would strengthen the profile\"],\n \"improvementSuggestions\": [\"actionable suggestions\"],\n \"hiringRecommendation\": \"one of: Strong Hire | Hire | Consider | Needs Improvement | Not Recommended\",\n \"reasoning\": \"2-4 sentences justifying the recommendation\"\n}`;\n\n const res = await fetch(\"https://openrouter.ai/api/v1/chat/completions\", {\n method: \"POST\",\n headers: {\n Authorization: `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n \"HTTP-Referer\": \"https://val.town\",\n \"X-Title\": \"AI Candidate Analyzer\",\n },\n body: JSON.stringify({\n model: MODEL,\n messages: [\n { role: \"system\", content: system },\n { role: \"user\", content: user },\n ],\n temperature: 0.4,\n response_format: { type: \"json_object\" },\n }),\n });\n\n if (!res.ok) {\n throw new Error(`OpenRouter ${res.status}: ${await res.text()}`);\n }\n\n const json = await res.json();\n const content = json.choices?.[0]?.message?.content;\n if (!content) throw new Error(\"Empty response from OpenRouter.\");\n\n let parsed: AIReport;\n try {\n parsed = JSON.parse(content);\n } catch {\n // Best-effort extraction if the model wrapped JSON in text\n const match = content.match(/\\{[\\s\\S]*\\}/);\n if (!match) throw new Error(\"Could not parse AI response as JSON.\");\n parsed = JSON.parse(match[0]);\n }\n\n return {\n executiveSummary: parsed.executiveSummary || \"\",\n strengths: parsed.strengths || [],\n weaknesses: parsed.weaknesses || [],\n missingSkills: parsed.missingSkills || [],\n improvementSuggestions: parsed.improvementSuggestions || [],\n hiringRecommendation: parsed.hiringRecommendation || 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