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It is designed as a stateless adapter: LinkedIn\nis the only data source, and all outputs are pure transformations of the\nexported snapshot.\n\n## Supported Formats & Capabilities\n\n- **MAC (Manfred Awesomic CV)**: JSON format mapped to `aboutMe`, `experience`\n (jobs, volunteering, projects), and `knowledge` (skills, studies,\n certifications, publications, awards, languages).\n [MAC format repository](https://github.com/getmanfred/mac)\n- **JSON Resume**: Standardized JSON schema for resumes. Fully populates\n `basics`, `work`, `volunteer`, `education`, `awards`, `publications`,\n `skills`, `languages`, `projects`, and `certificates`.\n [JSON Resume schema](https://jsonresume.org/schema/)\n- **RenderCV**: Generates a valid YAML structure compatible with the RenderCV\n CLI and forces a file download (`rendercv.yaml`).\n [RenderCV documentation](https://docs.rendercv.com/user_guide/yaml_input_structure/)\n- **Debug APIs**: Endpoints to dump the raw LinkedIn `memberSnapshotData` arrays\n for inspection.\n\n## LinkedIn DMA / Portability API\n\nThis val uses LinkedIn’s **Member Data Portability (DMA) APIs**\n(`memberSnapshotData` endpoint).\n\n- **Official Documentation**:\n [Microsoft Learn - Member Data Portability](https://learn.microsoft.com/en-us/linkedin/dma/member-data-portability/member-data-portability-member/)\n- **Supported Domains List**:\n [Snapshot Domains](https://learn.microsoft.com/en-us/linkedin/dma/member-data-portability/shared/snapshot-domain)\n\n### ⚠️ Important API Limitations\n\n1. **Geographic Eligibility**: The DMA Member Data Portability APIs are designed\n to comply with the EU Digital Markets Act. They are available **only to\n members located in the European Economic Area (EEA) and Switzerland**.\n2. **Monolingual Export**: The API typically exports profile data in **one\n primary language only**. If you maintain multiple localized versions of your\n profile in the UI, only the primary language might appear in the snapshot.\n3. **Structural Differences**: The data returned does not replicate the LinkedIn\n UI exactly. Missing cross-domain relationships (e.g., skills linked to\n specific jobs) or empty sections might occur depending on your privacy\n settings.\n\n## Architecture Overview\n\nThe Val Town script acts as a single HTTP handler:\n\n1. **Token Extraction**: Expects an access token via the\n `Authorization: Bearer ` header, a `?token=` query parameter, or a Val\n Town environment variable (`token`).\n2. **Optimized Aggregation (`LIFullProfile`)**: To minimize HTTP calls, CV\n generation endpoints use a single, paginated API request _without_ specifying\n a domain. This automatically fetches all available snapshot data at once,\n saving time and rate limits.\n3. **Format Mappers**: Transforms the aggregated model into MAC, JSON Resume, or\n RenderCV models without relying on third-party schemas for the internal\n state.\n\n## HTTP Endpoints (All GET)\n\n### `GET /profile`\n\nReturns the aggregated `LIFullProfile` JSON object containing all fetched\ndomains. Useful for quick diagnostics.\n\n### `GET /mac`\n\nReturns a JSON object strictly mapped to the Manfred Awesomic CV format.\nIncludes extracted profile images from `RICH_MEDIA`.\n\n### `GET /jsonresume`\n\nReturns a JSON Resume compatible object.\n\n### `GET /rendercv`\n\nDownloads a generated `rendercv.yaml` file. The output is converted from JSON to\na valid YAML structure using the standard `yaml` package and is served with a\n`Content-Disposition: attachment` header.\n\n### `GET /debug`\n\nAllows inspection of the raw, unmapped data structures returned by LinkedIn.\n\n- `GET /debug`: Returns an HTTP 400 error (requires a query parameter).\n- `GET /debug?analyzeDomains`: Scans your entire snapshot and returns a summary\n counting the number of items found inside each domain.\n- `GET /debug?domains`: Fetches **all available domains** for your account in a\n single optimized request and returns the full JSON.\n- `GET /debug?domains=PROJECTS`: Fetches exactly 1 domain using a direct\n `domain=PROJECTS` API call.\n- `GET /debug?domains=PROJECTS,SKILLS`: Fetches all data using the optimized\n single-request algorithm, then filters and returns only the requested domains\n locally.\n\n## Deployment on Val Town\n\n1. Create a new HTTP val on [Val Town](https://www.val.town).\n2. Paste the provided TypeScript code.\n3. Add your LinkedIn DMA access token as a `token` environment variable in the\n Val settings (or pass it in your requests).\n4. Visit your val's URL with the desired endpoint appended (e.g.,\n `https://.web.val.run/rendercv`).","deleted","label","children",[],[160],{"_106":180,"_99":181,"_161":182,"_163":-5,"_164":146,"_165":183,"_166":167,"_108":184,"_103":185,"_170":186,"_172":187,"_174":22,"_175":-5,"_176":188,"_102":189},"9e0c977c-2d99-11f1-9027-42dde27851f2","main.ts","19f86d2a-2ce6-11f1-b489-42dde27851f2",13,["D",1775027322979],10,"http","// val.town HTTP Val — LinkedIn → MAC / JSON Resume / RenderCV (v10)\n// Optimized LinkedIn DMA API usage using paginated requests without domain filter.\n//\n// Official supported domains list:\n// https://learn.microsoft.com/en-us/linkedin/dma/member-data-portability/shared/snapshot-domain\n//\n// HTTP endpoints (all GET):\n// GET /profile → Aggregated LinkedIn snapshot (JSON)\n// GET /mac → MAC JSON\n// GET /jsonresume → JSON Resume object\n// GET /rendercv → RenderCV-compatible YAML file\n// GET /debug?analyzeDomains → Counts of elements per domain\n// GET /debug?domains → Raw snapshotData for ALL available domains in one request\n// GET /debug?domains=A → Current algorithm (1 specific domain request)\n// GET /debug?domains=A,B → Optimized algorithm (fetch all, then filter)\n\nimport { stringify as stringifyYaml } from \"npm:yaml\";\n\n// ── LinkedIn domain types (defensive mappings) ────────────────────\ninterface LIProfile {\n \"First Name\"?: string;\n \"Last Name\"?: string;\n Headline?: string;\n Summary?: string;\n}\ninterface LISkill {\n Name?: string;\n}\ninterface LIPosition {\n Title?: string;\n Description?: string;\n \"Company Name\"?: string;\n \"Started On\"?: string;\n \"Finished On\"?: string;\n}\ninterface LIEducation {\n \"School Name\"?: string;\n \"Degree Name\"?: string;\n \"Start Date\"?: string;\n \"End Date\"?: string;\n}\ninterface LIProject {\n Title?: string;\n Description?: string;\n \"Started On\"?: string;\n \"Finished On\"?: string;\n Url?: string;\n}\ninterface LICertification {\n Name?: string;\n \"Issuing Organization\"?: string;\n \"Issue Date\"?: string;\n \"Expiration Date\"?: string;\n Url?: string;\n}\ninterface LICourse {\n Name?: string;\n Number?: string;\n \"Associated With\"?: string;\n}\ninterface LILanguage {\n Name?: string;\n Proficiency?: string;\n}\ninterface LIRichMedia {\n \"Media Link\"?: string;\n \"Media Description\"?: string;\n \"Date/Time\"?: string;\n}\ninterface LIVolunteering {\n Role?: string;\n Organization?: string;\n Cause?: string;\n Description?: string;\n \"Started On\"?: string;\n \"Finished On\"?: string;\n}\ninterface LIHonor {\n Title?: string;\n Issuer?: string;\n \"Issue Date\"?: string;\n Description?: string;\n}\ninterface LIPublication {\n Title?: string;\n Publisher?: string;\n \"Publication Date\"?: string;\n Url?: string;\n Description?: string;\n}\ninterface LIPatent {\n Title?: string;\n \"Filing Date\"?: string;\n Url?: string;\n Description?: string;\n}\n\ninterface LIFullProfile {\n profile: LIProfile;\n skills: LISkill[];\n positions: LIPosition[];\n education: LIEducation[];\n projects: LIProject[];\n certifications: LICertification[];\n courses: LICourse[];\n languages: LILanguage[];\n richMedia: LIRichMedia[];\n volunteering: LIVolunteering[];\n honors: LIHonor[];\n publications: LIPublication[];\n patents: LIPatent[];\n}\n\n// ── MAC types (Manfred MAC-inspired) ─────────────────────────────\ninterface MacCompetence {\n name: string;\n type: string;\n}\ninterface MacSkill {\n skill: MacCompetence;\n}\ninterface MacChallenge {\n description: string;\n}\ninterface MacRole {\n name: string;\n startDate?: string;\n finishDate?: string;\n challenges: MacChallenge[];\n}\ninterface MacOrg {\n name: string;\n url?: string;\n}\ninterface MacJob {\n organization: MacOrg;\n roles: MacRole[];\n}\ninterface MacStudy {\n studyType: string;\n name: string;\n degreeAchieved: boolean;\n description?: string;\n startDate?: string;\n finishDate?: string;\n institution?: MacOrg;\n}\ninterface MacPerson {\n title: string;\n description?: string;\n imageUrl?: string;\n bannerUrl?: string;\n}\ninterface MacAboutMe {\n profile: MacPerson;\n}\ninterface MacExperienceProject {\n name: string;\n description?: string;\n startDate?: string;\n finishDate?: string;\n url?: string;\n}\ninterface MacExperience {\n jobs: MacJob[];\n projects: MacExperienceProject[];\n volunteering?: MacJob[];\n}\ninterface MacCourse {\n name: string;\n provider?: string;\n code?: string;\n}\ninterface MacCertification {\n name: string;\n organization?: string;\n issueDate?: string;\n expirationDate?: string;\n url?: string;\n}\ninterface MacLanguage {\n name: string;\n proficiency?: string;\n}\ninterface MacPublication {\n name: string;\n publisher?: string;\n releaseDate?: string;\n url?: string;\n summary?: string;\n}\ninterface MacAward {\n name: string;\n awarder?: string;\n date?: string;\n summary?: string;\n}\ninterface MacKnowledge {\n hardSkills: MacSkill[];\n studies: MacStudy[];\n courses: MacCourse[];\n certifications: MacCertification[];\n languages: MacLanguage[];\n publications?: MacPublication[];\n awards?: MacAward[];\n}\ninterface Mac {\n settings: { language: string };\n aboutMe: MacAboutMe;\n experience: MacExperience;\n knowledge: MacKnowledge;\n}\n\n// ── JSON Resume types (simplified) ───────────────────────────────\ninterface JsonResume {\n basics?: { name?: string; label?: string; image?: string; summary?: string };\n work?: Array<\n {\n name?: string;\n position?: string;\n url?: string;\n startDate?: string;\n endDate?: string;\n summary?: string;\n }\n >;\n volunteer?: Array<\n {\n organization?: string;\n position?: string;\n url?: string;\n startDate?: string;\n endDate?: string;\n summary?: string;\n }\n >;\n education?: Array<\n {\n institution?: string;\n area?: string;\n studyType?: string;\n startDate?: string;\n endDate?: string;\n }\n >;\n awards?: Array<\n { title?: string; date?: string; awarder?: string; summary?: string }\n >;\n publications?: Array<\n {\n name?: string;\n publisher?: string;\n releaseDate?: string;\n url?: string;\n summary?: string;\n }\n >;\n skills?: Array<{ name?: string; keywords?: string[] }>;\n languages?: Array<{ language?: string; fluency?: string }>;\n projects?: Array<\n {\n name?: string;\n description?: string;\n url?: string;\n startDate?: string;\n endDate?: string;\n }\n >;\n certificates?: Array<\n { name?: string; issuer?: string; date?: string; url?: string }\n >;\n}\n\n// ── RenderCV types (simplified YAML model) ───────────────────────\ninterface RenderCvExperienceEntry {\n company: string;\n position: string;\n start_date?: string;\n end_date?: string;\n summary?: string;\n}\ninterface RenderCvEducationEntry {\n institution: string;\n degree?: string;\n start_date?: string;\n end_date?: string;\n}\ninterface RenderCvProjectEntry {\n name: string;\n start_date?: string;\n end_date?: string;\n url?: string;\n summary?: string;\n}\ninterface RenderCvNormalEntry {\n name: string;\n date?: string;\n details?: string;\n}\ninterface RenderCvPublicationEntry {\n title: string;\n date?: string;\n journal?: string;\n url?: string;\n}\ninterface RenderCvSection {\n experience?: RenderCvExperienceEntry[];\n education?: RenderCvEducationEntry[];\n volunteering?: RenderCvExperienceEntry[];\n projects?: RenderCvProjectEntry[];\n honors?: RenderCvNormalEntry[];\n publications?: RenderCvPublicationEntry[];\n patents?: RenderCvNormalEntry[];\n}\ninterface RenderCvModel {\n cv: { name: string; sections: RenderCvSection };\n}\n\n// ── Token extraction ─────────────────────────────────────────────\nfunction extractToken(req: Request, url: URL): string | undefined {\n const authHeader = req.headers.get(\"Authorization\");\n if (authHeader && authHeader.toLowerCase().startsWith(\"bearer \")) {\n return authHeader.substring(7).trim();\n }\n const queryToken = url.searchParams.get(\"token\");\n if (queryToken) return queryToken;\n // @ts-ignore\n const envToken = typeof Deno !== \"undefined\"\n ? Deno.env.get(\"token\")\n : undefined;\n return envToken;\n}\n\n// ── Low-level LinkedIn API helpers ────────────────────────────────\n\n// 1. Current algorithm (specific domain)\nasync function fetchRawDomain(token: string, domain: string): Promise {\n const apiUrl =\n `https://api.linkedin.com/rest/memberSnapshotData?q=criteria&domain=${domain}`;\n const res = await fetch(apiUrl, {\n headers: { Authorization: `Bearer ${token}`, \"LinkedIn-Version\": \"202312\" },\n });\n if (res.status === 404) return null;\n if (!res.ok) {\n throw new Error(\n `LinkedIn API error for ${domain}: ${res.status} ${res.statusText}`,\n );\n }\n const json = await res.json();\n return json?.elements?.[0]?.snapshotData ?? null;\n}\n\n// 2. Optimized algorithm (all domains, paginated)\nasync function fetchAllDomains(\n token: string,\n maxPages = 50,\n): Promise> {\n let url: string | null =\n \"https://api.linkedin.com/rest/memberSnapshotData?q=criteria\";\n const result: Record = {};\n let pages = 0;\n\n while (url && pages < maxPages) {\n pages++;\n const res = await fetch(url, {\n headers: {\n Authorization: `Bearer ${token}`,\n \"LinkedIn-Version\": \"202312\",\n },\n });\n if (!res.ok) {\n throw new Error(`LinkedIn API error: ${res.status} ${res.statusText}`);\n }\n\n const json: any = await res.json();\n const elements = json?.elements ?? [];\n\n for (const el of elements) {\n const domain = el?.snapshotDomain;\n const data = el?.snapshotData;\n\n if (domain && data) {\n if (!result[domain]) result[domain] = [];\n if (Array.isArray(data)) result[domain].push(...data);\n else result[domain].push(data);\n }\n }\n\n const links: any[] = json?.paging?.links ?? [];\n const next = links.find((l: any) => l.rel === \"next\");\n if (next?.href) {\n url = next.href.startsWith(\"http\")\n ? next.href\n : \"https://api.linkedin.com\" + next.href;\n } else {\n url = null;\n }\n }\n\n return result;\n}\n\n// Extract full profile using the optimized paginated request\nasync function fetchFullProfile(token: string): Promise {\n const allData = await fetchAllDomains(token);\n\n const profileRaw = allData[\"PROFILE\"] || [];\n const profile = profileRaw[0] || {};\n\n return {\n profile,\n skills: allData[\"SKILLS\"] || [],\n positions: allData[\"POSITIONS\"] || [],\n education: allData[\"EDUCATION\"] || [],\n projects: allData[\"PROJECTS\"] || [],\n certifications: allData[\"CERTIFICATIONS\"] || [],\n courses: allData[\"COURSES\"] || [],\n languages: allData[\"LANGUAGES\"] || [],\n richMedia: allData[\"RICH_MEDIA\"] || [],\n volunteering: allData[\"VOLUNTEERING_EXPERIENCES\"] || [],\n honors: allData[\"HONORS\"] || [],\n publications: allData[\"PUBLICATIONS\"] || [],\n patents: allData[\"PATENTS\"] || [],\n };\n}\n\n// ── Helpers ───────────────────────────────────────────────────────\nfunction toIsoDate(raw?: string): string | undefined {\n if (!raw) return undefined;\n const parsed = new Date(raw);\n return !isNaN(parsed.getTime())\n ? parsed.toISOString().split(\"T\")[0]\n : undefined;\n}\n\nfunction pickRichMedia(items: LIRichMedia[]) {\n let profilePhoto, backgroundPhoto;\n for (const item of items) {\n const dt = (item[\"Date/Time\"] || \"\").toLowerCase();\n const link = item[\"Media Link\"] || \"\";\n if (!link) continue;\n if (dt.includes(\"profile photo\")) profilePhoto = link;\n else if (dt.includes(\"profile background photo\")) backgroundPhoto = link;\n }\n return { profilePhoto, backgroundPhoto };\n}\n\n// ── Mappers ───────────────────────────────────────────────────────\nfunction toMac(li: LIFullProfile): Mac {\n const rich = pickRichMedia(li.richMedia);\n const jobs: MacJob[] = li.positions.map((p) => ({\n organization: { name: p[\"Company Name\"] || \"\" },\n roles: [{\n name: p.Title || \"\",\n startDate: toIsoDate(p[\"Started On\"]),\n finishDate: toIsoDate(p[\"Finished On\"]),\n challenges: [{ description: p.Description || \"\" }],\n }],\n }));\n const volunteering: MacJob[] = li.volunteering.map((v) => ({\n organization: { name: v.Organization || \"\" },\n roles: [{\n name: v.Role || \"\",\n startDate: toIsoDate(v[\"Started On\"]),\n finishDate: toIsoDate(v[\"Finished On\"]),\n challenges: [{ description: v.Description || \"\" }],\n }],\n }));\n\n return {\n settings: { language: \"en\" },\n aboutMe: {\n profile: {\n title: li.profile.Headline || \"\",\n description: li.profile.Summary,\n imageUrl: rich.profilePhoto,\n bannerUrl: rich.backgroundPhoto,\n },\n },\n experience: {\n jobs,\n projects: li.projects.map((p) => ({\n name: p.Title || \"\",\n description: p.Description,\n startDate: toIsoDate(p[\"Started On\"]),\n finishDate: toIsoDate(p[\"Finished On\"]),\n url: p.Url,\n })),\n volunteering,\n },\n knowledge: {\n hardSkills: li.skills.map((s) => ({\n skill: { name: s.Name || \"\", type: \"technology\" },\n })),\n studies: li.education.map((e) => ({\n studyType: \"officialDegree\",\n name: e[\"Degree Name\"] || e[\"School Name\"] || \"\",\n degreeAchieved: !!e[\"End Date\"],\n description: e[\"Degree Name\"],\n startDate: toIsoDate(e[\"Start Date\"]),\n finishDate: toIsoDate(e[\"End Date\"]),\n institution: { name: e[\"School Name\"] || \"\" },\n })),\n courses: li.courses.map((c) => ({\n name: c.Name || \"\",\n code: c.Number,\n provider: c[\"Associated With\"],\n })),\n certifications: li.certifications.map((c) => ({\n name: c.Name || \"\",\n organization: c[\"Issuing Organization\"],\n issueDate: toIsoDate(c[\"Issue Date\"]),\n expirationDate: toIsoDate(c[\"Expiration Date\"]),\n url: c.Url,\n })),\n languages: li.languages.map((l) => ({\n name: l.Name || \"\",\n proficiency: l.Proficiency,\n })),\n publications: li.publications.map((p) => ({\n name: p.Title || \"\",\n publisher: p.Publisher,\n releaseDate: toIsoDate(p[\"Publication Date\"]),\n url: p.Url,\n summary: p.Description,\n })),\n awards: li.honors.map((h) => ({\n name: h.Title || \"\",\n awarder: h.Issuer,\n date: toIsoDate(h[\"Issue Date\"]),\n summary: h.Description,\n })),\n },\n };\n}\n\nfunction toJsonResume(li: LIFullProfile): JsonResume {\n const rich = pickRichMedia(li.richMedia);\n const resume: JsonResume = {\n basics: {\n name: [li.profile[\"First Name\"], li.profile[\"Last Name\"]].filter(Boolean)\n .join(\" \"),\n label: li.profile.Headline,\n image: rich.profilePhoto,\n summary: li.profile.Summary,\n },\n work: li.positions.map((p) => ({\n name: p[\"Company Name\"],\n position: p.Title,\n startDate: toIsoDate(p[\"Started On\"]),\n endDate: toIsoDate(p[\"Finished On\"]),\n summary: p.Description,\n })),\n volunteer: li.volunteering.map((v) => ({\n organization: v.Organization,\n position: v.Role,\n startDate: toIsoDate(v[\"Started On\"]),\n endDate: toIsoDate(v[\"Finished On\"]),\n summary: v.Description,\n })),\n education: li.education.map((e) => ({\n institution: e[\"School Name\"],\n area: e[\"Degree Name\"],\n studyType: e[\"Degree Name\"],\n startDate: toIsoDate(e[\"Start Date\"]),\n endDate: toIsoDate(e[\"End Date\"]),\n })),\n awards: li.honors.map((h) => ({\n title: h.Title,\n date: toIsoDate(h[\"Issue Date\"]),\n awarder: h.Issuer,\n summary: h.Description,\n })),\n publications: li.publications.map((p) => ({\n name: p.Title,\n publisher: p.Publisher,\n releaseDate: toIsoDate(p[\"Publication Date\"]),\n url: p.Url,\n summary: p.Description,\n })),\n skills: li.skills.length\n ? [{\n name: \"Skills\",\n keywords: li.skills.map((s) => s.Name || \"\").filter(Boolean),\n }]\n : [],\n languages: li.languages.map((l) => ({\n language: l.Name,\n fluency: l.Proficiency,\n })),\n projects: li.projects.map((p) => ({\n name: p.Title,\n description: p.Description,\n url: p.Url,\n startDate: toIsoDate(p[\"Started On\"]),\n endDate: toIsoDate(p[\"Finished On\"]),\n })),\n certificates: li.certifications.map((c) => ({\n name: c.Name,\n issuer: c[\"Issuing Organization\"],\n date: toIsoDate(c[\"Issue Date\"]),\n url: c.Url,\n })),\n };\n return resume;\n}\n\nfunction toRenderCv(li: LIFullProfile): RenderCvModel {\n const sections: RenderCvSection = {};\n if (li.positions.length) {\n sections.experience = li.positions.map((p) => ({\n company: p[\"Company Name\"] || \"\",\n position: p.Title || \"\",\n start_date: toIsoDate(p[\"Started On\"]),\n end_date: toIsoDate(p[\"Finished On\"]),\n summary: p.Description,\n }));\n }\n if (li.volunteering.length) {\n sections.volunteering = li.volunteering.map((v) => ({\n company: v.Organization || \"\",\n position: v.Role || \"\",\n start_date: toIsoDate(v[\"Started On\"]),\n end_date: toIsoDate(v[\"Finished On\"]),\n summary: v.Description,\n }));\n }\n if (li.education.length) {\n sections.education = li.education.map((e) => ({\n institution: e[\"School Name\"] || \"\",\n degree: e[\"Degree Name\"],\n start_date: toIsoDate(e[\"Start Date\"]),\n end_date: toIsoDate(e[\"End Date\"]),\n }));\n }\n if (li.projects.length) {\n sections.projects = li.projects.map((p) => ({\n name: p.Title || \"\",\n start_date: toIsoDate(p[\"Started On\"]),\n end_date: toIsoDate(p[\"Finished On\"]),\n url: p.Url,\n summary: p.Description,\n }));\n }\n if (li.honors.length) {\n sections.honors = li.honors.map((h) => ({\n name: h.Title || \"\",\n date: toIsoDate(h[\"Issue Date\"]),\n details: h.Description,\n }));\n }\n if (li.publications.length) {\n sections.publications = li.publications.map((p) => ({\n title: p.Title || \"\",\n date: toIsoDate(p[\"Publication Date\"]),\n journal: p.Publisher,\n url: p.Url,\n }));\n }\n if (li.patents.length) {\n sections.patents = li.patents.map((p) => ({\n name: p.Title || \"\",\n date: toIsoDate(p[\"Filing Date\"]),\n details: p.Description,\n }));\n }\n\n return {\n cv: {\n name: [li.profile[\"First Name\"], li.profile[\"Last Name\"]].filter(Boolean)\n .join(\" \"),\n sections,\n },\n };\n}\n\n// ── Main HTTP handler ────────────────────────────────────────────\nexport default async function handler(req: Request): Promise {\n const url = new URL(req.url);\n const path = url.pathname;\n const json = (data: unknown, status = 200) =>\n new Response(JSON.stringify(data, null, 2), {\n status,\n headers: { \"Content-Type\": \"application/json\" },\n });\n const err = (msg: string, status = 400) => json({ error: msg }, status);\n\n try {\n const token = extractToken(req, url);\n if (!token) {\n return err(\n \"Missing token. Provide via Authorization: Bearer , ?token= query param, or 'token' env var.\",\n 401,\n );\n }\n\n // Profile and Format endpoints (optimized fetching)\n if (path === \"/profile\") return json(await fetchFullProfile(token));\n if (path === \"/mac\") return json(toMac(await fetchFullProfile(token)));\n if (path === \"/jsonresume\") {\n return json(toJsonResume(await fetchFullProfile(token)));\n }\n\n if (path === \"/rendercv\") {\n const renderCvModel = toRenderCv(await fetchFullProfile(token));\n return new Response(stringifyYaml(renderCvModel), {\n status: 200,\n headers: {\n \"Content-Type\": \"text/yaml; charset=utf-8\",\n \"Content-Disposition\": 'attachment; filename=\"rendercv.yaml\"',\n },\n });\n }\n\n // Debug logic\n if (path === \"/debug\") {\n // 1. Analyze mode (check if parameter exists, value doesn't matter)\n if (url.searchParams.has(\"analyzeDomains\")) {\n const allData = await fetchAllDomains(token);\n const counts: Record = {};\n for (const [domain, items] of Object.entries(allData)) {\n counts[domain] = items.length;\n }\n return json({ analyzeDomains: counts });\n }\n\n // If neither analyzeDomains nor domains is provided, return an error\n if (!url.searchParams.has(\"domains\")) {\n return err(\n \"Missing query parameter. Use ?analyzeDomains to count items, ?domains to fetch all, or ?domains=A,B to fetch specific ones.\",\n 400,\n );\n }\n\n const domainsParam = url.searchParams.get(\"domains\") || \"\";\n\n // 2. Empty domains parameter (?domains= or ?domains) -> fetch all available domains optimally\n if (domainsParam.trim() === \"\") {\n const allData = await fetchAllDomains(token);\n return json({ requested_domains: \"ALL (Available)\", data: allData });\n }\n\n const domainsList = domainsParam.split(\",\").map((d) =>\n d.trim().toUpperCase()\n ).filter(Boolean);\n\n // 3. Exactly 1 domain -> use current specific-domain algorithm\n if (domainsList.length === 1) {\n const domain = domainsList[0];\n const debugResult: Record = {};\n try {\n debugResult[domain] = await fetchRawDomain(token, domain);\n } catch (e: any) {\n debugResult[domain] = { error: e.message };\n }\n return json({ requested_domains: domainsList, data: debugResult });\n }\n\n // 4. More than 1 domain -> fetch all optimally and filter\n if (domainsList.length > 1) {\n const allData = await fetchAllDomains(token);\n const debugResult: Record = {};\n for (const domain of domainsList) {\n debugResult[domain] = allData[domain] || null; // Will show null if the domain is missing\n }\n return json({ requested_domains: domainsList, data: debugResult });\n }\n }\n\n return err(\n \"Not found. Available GET endpoints: /profile, /mac, /jsonresume, /rendercv, /debug\",\n 404,\n );\n } catch (e: unknown) {\n return err(\n `Internal error: ${e instanceof Error ? e.message : String(e)}`,\n 500,\n );\n }\n}",[],[181],"files",[158,179],"counts",{"_194":33,"_195":196,"_197":33,"_198":199,"_203":204},"pulls","branches",1,"forks","envVars",{"_200":201,"_202":33},"count",3,"emptyCount","connections",{"_200":33,"_205":33},"unfilledCount","contents",[208,211],{"_106":159,"_99":160,"_161":162,"_163":-5,"_164":146,"_165":145,"_166":167,"_108":209,"_103":169,"_170":171,"_172":173,"_174":22,"_175":-5,"_102":210},["D",1775027682715],[160],{"_106":180,"_99":181,"_161":182,"_163":-5,"_164":146,"_165":183,"_166":167,"_108":212,"_103":185,"_170":186,"_172":187,"_174":22,"_175":-5,"_102":213},["D",1775027322979],[181],"upstreamProject","upstreamBranch","emptyEnvVars","isNew","flags",[],"routes/_app.x.$handle.$name.environment-variables",{"_97":98,"_99":100,"_104":222,"_198":227,"_242":22},{"_106":107,"_99":100,"_108":223,"_110":-5,"_111":22,"_112":113,"_114":-5,"_115":-5,"_14":116,"_117":-5,"_118":22,"_119":-5,"_120":-5,"_121":33,"_16":122,"_123":-5,"_124":-5,"_125":-5,"_126":127,"_128":127,"_129":130,"_131":130,"_132":130,"_133":127,"_134":127,"_23":224,"_138":225,"_143":226,"_155":22},["D",1774950221482],{"_97":98,"_136":137},{"_97":98,"_140":137,"_14":116,"_141":142},{"_103":145,"_99":45,"_106":146,"_147":107,"_148":-5,"_149":-5,"_150":-5,"_151":-5,"_152":-5,"_153":100,"_154":98},[228,234,238],{"_229":230,"_108":231,"_232":-5,"_114":76,"_233":22},"key","token",["D",1774951691791],"updatedAt","isEmpty",{"_229":235,"_108":236,"_232":237,"_114":76,"_233":22},"LINKEDIN_CLIENT_SECRET",["D",1774951537223],["D",1774951830806],{"_229":239,"_108":240,"_232":241,"_114":76,"_233":22},"LINKEDIN_CLIENT_ID",["D",1774951522501],["D",1774951665760],"isTeamOrg","actionData","errors"]

Key	Description
token
LINKEDIN_CLIENT_SECRET
LINKEDIN_CLIENT_ID

foxdark410

linkedin-mdp-api

Environment variables