{ "id": "HHRhCg2X00IauLhl", "name": "\ud83c\udfaf Mind the Gap \u2014 Job-market skill-demand radar (LLM + webhook)", "tags": [], "nodes": [ { "id": "9d482430-a708-4eda-9835-15c7288d4fe9", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ -560, -32 ], "parameters": { "width": 480, "height": 832, "content": "## \ud83c\udfaf Mind the Gap \u2014 Job-market skill-demand radar (LLM + webhook)\n\n### How it works\n\nThis workflow produces a web-based job-market skill-demand radar from a webhook or manual run. It loads the desired role and the user\u2019s current skills, serves a cached HTML radar when inputs have not changed, or fetches recent remote jobs, ranks relevant postings, asks an LLM to extract skills, and renders the demand-versus-gap report as HTML.\n\n### Setup steps\n\n- Configure the webhook URL/path and activate the workflow so the radar can be opened in a browser.\n- Edit the \u201cDesired job config\u201d node with the target role, keywords, LLM endpoint/model settings, and any required API key or authorization header.\n- Edit the \u201cYour Skills\u201d node with the user\u2019s CV text, skill list, tools, or background to compare against market demand.\n- Run the \u201cManual test\u201d trigger once to validate the LLM request/response format and confirm the rendered HTML looks correct.\n\n### Customization\n\nAdjust the target role keywords, ranking rules, cache freshness logic, job source, LLM model, and radar rendering thresholds to tune the analysis for a specific market or career path." }, "typeVersion": 1 }, { "id": "0ff04a64-3f0b-42fc-ac82-d18b826c3169", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ 0, -32 ], "parameters": { "color": 7, "width": 240, "height": 512, "content": "## Start radar request\n\nEntry points for opening the radar through the webhook or running the workflow manually for testing." }, "typeVersion": 1 }, { "id": "423a9925-f6c1-44d6-8490-a81e2a55b7d3", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [ 320, 64 ], "parameters": { "color": 7, "width": 432, "height": 320, "content": "## Configure target profile\n\nDefines the target job search configuration and the user\u2019s known skills that will be used for comparison." }, "typeVersion": 1 }, { "id": "333758e0-9166-41b0-b7b5-87a09883f78d", "name": "Sticky Note3", "type": "n8n-nodes-base.stickyNote", "position": [ 832, 64 ], "parameters": { "color": 7, "width": 432, "height": 320, "content": "## Check cached output\n\nDetermines whether the current inputs match a fresh cached radar page and either returns it immediately or continues to recompute results." }, "typeVersion": 1 }, { "id": "754f0efc-c8c4-4d75-9d91-a94dd2942bb4", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [ 1360, 320 ], "parameters": { "color": 7, "width": 432, "height": 320, "content": "## Fetch and rank jobs\n\nRetrieves recent remote job postings from the configured source, filters them by keywords, ranks them for relevance, and caps the result set." }, "typeVersion": 1 }, { "id": "c9b90795-0243-42b7-8fe9-d9f9440798a6", "name": "Sticky Note5", "type": "n8n-nodes-base.stickyNote", "position": [ 1840, 320 ], "parameters": { "color": 7, "width": 672, "height": 320, "content": "## Extract demanded skills\n\nBuilds per-job extraction prompts, sends them to the LLM endpoint, parses the responses, canonicalizes skills, and emits structured skill-demand rows." }, "typeVersion": 1 }, { "id": "073a9849-0d2b-4ca7-8f91-4843379768c1", "name": "Sticky Note6", "type": "n8n-nodes-base.stickyNote", "position": [ 2560, 272 ], "parameters": { "color": 7, "width": 240, "height": 368, "content": "## Render radar page\n\nAggregates extracted market demand, compares it against the user\u2019s skills, and builds the final inlined HTML radar visualization." }, "typeVersion": 1 }, { "id": "7be4e3b4-cae9-4b78-98b5-13fb4c22d308", "name": "Sticky Note7", "type": "n8n-nodes-base.stickyNote", "position": [ 2848, 32 ], "parameters": { "color": 7, "width": 240, "height": 336, "content": "## Return HTML response\n\nSends either the cached page or the newly rendered radar page back to the webhook caller as HTML." }, "typeVersion": 1 }, { "id": "view", "name": "Webhook for Radar View", "type": "n8n-nodes-base.webhook", "onError": "continueRegularOutput", "position": [ 48, 144 ], "parameters": { "path": "mind-the-gap", "options": {}, "responseMode": "responseNode" }, "typeVersion": 2.1 }, { "id": "manual", "name": "Trigger Manually", "type": "n8n-nodes-base.manualTrigger", "position": [ 48, 320 ], "parameters": {}, "typeVersion": 1 }, { "id": "gate", "name": "Manage Cache Stability", "type": "n8n-nodes-base.code", "position": [ 880, 224 ], "parameters": { "jsCode": "// Serve the cached page so refreshes are STABLE \u2014 but recompute whenever the inputs change.\n// Cache is reused only if: same keywords + same skills text, younger than cacheHours, and the\n// caller didn't pass ?refresh=1. Editing Config or Registry busts the cache automatically.\nconst cacheHours = 24;\nconst store = $getWorkflowStaticData('global');\nconst c = store.radar;\nconst sig = JSON.stringify({\n k: $('Set Job Search Criteria').first().json.keywords || [],\n s: $('Record Your Skills Data').first().json.skillsText || '',\n});\nlet force = false, viaWebhook = true;\ntry { force = String(($('Webhook for Radar View').first().json.query || {}).refresh || '') === '1'; }\ncatch (e) { viaWebhook = false; } // manual runs always recompute (handy for testing)\nconst fresh = viaWebhook && !force && c && c.html && c.sig === sig && (Date.now() - (c.ts || 0) < cacheHours * 3600 * 1000);\nreturn [{ json: fresh ? { cached: true, html: c.html } : { cached: false } }];" }, "typeVersion": 2 }, { "id": "iffresh", "name": "Check Cache Freshness", "type": "n8n-nodes-base.if", "position": [ 1120, 224 ], "parameters": { "options": {}, "conditions": { "options": { "version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict" }, "combinator": "and", "conditions": [ { "id": "c1", "operator": { "type": "boolean", "operation": "true", "singleValue": true }, "leftValue": "={{ $json.cached }}", "rightValue": true } ] } }, "typeVersion": 2.2 }, { "id": "fetch", "name": "Fetch Recent Job Listings", "type": "n8n-nodes-base.code", "position": [ 1408, 480 ], "parameters": { "jsCode": "// One broad, no-auth source (Himalayas \u2014 recent remote jobs across every field). No company list.\n// Free job APIs don't offer real server-side search, so we pull a wide recent window and let the\n// next node filter/rank by your keywords (with a never-empty fallback).\nconst cfg = $('Set Job Search Criteria').first().json;\nconst H = (typeof $helpers !== 'undefined' && $helpers && $helpers.httpRequest) ? $helpers : this.helpers;\nconst s = (v) => (v == null ? '' : String(v));\nconst strip = (h) => s(h).replace(/<[^>]*>/g, ' ').replace(/&[a-z#0-9]+;/g, ' ').replace(/\\s+/g, ' ').trim().slice(0, 1500);\nconst per = cfg.feedPerPage || 100, pages = cfg.feedPages || 4;\nconst out = [];\nfor (let p = 0; p < pages; p++) {\n let hb;\n try { hb = await H.httpRequest({ method: 'GET', url: 'https://himalayas.app/jobs/api?limit=' + per + '&offset=' + (p * per), json: true, timeout: 20000 }); }\n catch (e) { break; }\n for (const j of ((hb && hb.jobs) || [])) {\n out.push({ json: {\n company: s(j.companyName) || 'unknown',\n title: s(j.title),\n description: strip(j.description || j.excerpt),\n posted_at: j.pubDate ? new Date(j.pubDate * 1000).toISOString() : '',\n external_id: 'himalayas:' + s(j.guid || j.title),\n } });\n }\n}\nreturn out;" }, "typeVersion": 2 }, { "id": "filter", "name": "Rank Job Listings", "type": "n8n-nodes-base.code", "position": [ 1648, 480 ], "parameters": { "jsCode": "// Keyword filter + relevance rank + cap. Relevance = title hits x3 + description hits x1,\n// tie-broken by recency. Empty keywords => keep the newest 'maxJobs' across the whole feed.\nconst cfg = $('Set Job Search Criteria').first().json;\nconst kw = (cfg.keywords || []).map(k => String(k).toLowerCase()).filter(Boolean);\nconst cap = cfg.maxJobs || 25;\nconst scored = $input.all().map(it => {\n const j = it.json;\n const t = (j.title || '').toLowerCase(), d = (j.description || '').toLowerCase();\n let rel = 0;\n for (const k of kw) { if (t.includes(k)) rel += 3; if (d.includes(k)) rel += 1; }\n return { j, rel };\n});\nlet keep = kw.length ? scored.filter(x => x.rel > 0) : scored;\nif (keep.length === 0) keep = scored; // never drop everything \u2014 keep the feed if nothing matched\nkeep.sort((a, b) => (b.rel - a.rel) || String(b.j.posted_at).localeCompare(String(a.j.posted_at)));\nreturn keep.slice(0, cap).map(x => ({ json: x.j }));" }, "typeVersion": 2 }, { "id": "prompt", "name": "Build Extraction Request", "type": "n8n-nodes-base.code", "position": [ 1888, 480 ], "parameters": { "jsCode": "// One extraction request per job (small models handle this fine; calls fail independently).\nconst cfg = $('Set Job Search Criteria').first().json;\nconst clip = (v, n) => String(v == null ? '' : v).replace(/\\s+/g, ' ').trim().slice(0, n);\nconst sys = [\n 'You extract structured skill requirements from a job posting.',\n 'Return ONLY a JSON object: {\"seniority\":\"\",\"skills\":[{\"name\":\"\",\"importance\":\"\"}]}.',\n '- skills are concrete technologies, tools, languages, methods, or domain competencies named in the posting.',\n '- canonical lowercase names: \"kubernetes\" not K8s, \"go\" not golang, \"ci/cd\" not CICD.',\n '- max 15 skills, most important first. Skip soft skills (communication, teamwork) and filler (engineering, software).',\n '- importance \"must\" if required/essential, \"nice\" if preferred/bonus.',\n].join('\\n');\nreturn $input.all().map(it => {\n const j = it.json;\n const user = 'Job posting:\\n' + JSON.stringify({ title: j.title, company: j.company, description: clip(j.description, 1500) });\n const body = { model: cfg.llmModel, messages: [{ role: 'system', content: sys }, { role: 'user', content: user }], temperature: 0, seed: 7, max_tokens: 500 };\n return { json: { external_id: j.external_id, company: j.company, title: j.title, bodyJson: JSON.stringify(body) } };\n});" }, "typeVersion": 2 }, { "id": "llm", "name": "Post to LLM API", "type": "n8n-nodes-base.httpRequest", "onError": "continueRegularOutput", "position": [ 2128, 480 ], "parameters": { "url": "={{ $('Set Job Search Criteria').first().json.llmUrl }}", "method": "POST", "options": { "timeout": 60000 }, "jsonBody": "={{ $json.bodyJson }}", "sendBody": true, "specifyBody": "json", "authentication": "genericCredentialType", "genericAuthType": "httpHeaderAuth" }, "credentials": { "httpHeaderAuth": { "name": "" } }, "typeVersion": 4.4 }, { "id": "parse", "name": "Analyze Extracted Skills", "type": "n8n-nodes-base.code", "position": [ 2368, 480 ], "parameters": { "jsCode": "// Align prompts<->responses by index, parse JSON, canonicalize, emit one row per (job, skill).\nconst prompts = $('Build Extraction Request').all().map(i => i.json);\nconst responses = $input.all().map(i => i.json);\nconst CANON = {\n 'k8s': 'kubernetes', 'k3s': 'kubernetes', 'eks': 'aws', 'gke': 'google cloud', 'aks': 'azure',\n 'golang': 'go', 'gcp': 'google cloud', 'amazon web services': 'aws', 'microsoft azure': 'azure',\n 'cicd': 'ci/cd', 'ci cd': 'ci/cd', 'ci-cd': 'ci/cd', 'continuous integration': 'ci/cd',\n 'argo cd': 'argocd', 'iac': 'infrastructure as code', 'tf': 'terraform', 'postgresql': 'postgres',\n 'js': 'javascript', 'ts': 'typescript', 'monitoring': 'observability', 'helm charts': 'helm',\n};\nconst out = [];\nfor (let i = 0; i < responses.length; i++) {\n const p = prompts[i];\n if (!p) continue;\n let parsed = null;\n try {\n const r = responses[i];\n let c = (r && r.choices && r.choices[0] && r.choices[0].message) ? (r.choices[0].message.content || '') : '';\n const m = c.match(/\\{[\\s\\S]*\\}/); if (m) c = m[0];\n parsed = JSON.parse(c);\n } catch (e) { continue; }\n let seniority = String(parsed.seniority || 'unspecified').toLowerCase().trim();\n if (!['junior', 'mid', 'senior', 'staff', 'lead'].includes(seniority)) seniority = 'unspecified';\n const seen = new Set();\n for (const sk of (Array.isArray(parsed.skills) ? parsed.skills : [])) {\n let name = String((sk && sk.name) || '').toLowerCase().trim().replace(/\\s+/g, ' ');\n if (!name) continue;\n if (CANON[name]) name = CANON[name];\n if (name.length > 60 || seen.has(name)) continue;\n seen.add(name);\n out.push({ json: { external_id: p.external_id, company: p.company, title: p.title, skill: name, importance: (sk && String(sk.importance).toLowerCase() === 'must') ? 'must' : 'nice', seniority } });\n }\n}\nreturn out;" }, "typeVersion": 2 }, { "id": "render", "name": "Generate Radar View", "type": "n8n-nodes-base.code", "position": [ 2608, 480 ], "parameters": { "jsCode": "// Inlined radar renderer \u2014 aggregate market demand and join it against your free-text skills.\nconst rows = $('Analyze Extracted Skills').all().map(i => i.json);\nconst skillsText = String(($('Record Your Skills Data').first().json || {}).skillsText || '').toLowerCase();\n\nconst demand = {};\nfor (const r of rows) {\n const s = (r.skill || '').toLowerCase().trim();\n if (!s || s === '__none') continue;\n if (!demand[s]) demand[s] = { mentions: 0, must: 0 };\n demand[s].mentions++;\n if (r.importance === 'must') demand[s].must++;\n}\nconst totalJobs = new Set(rows.map(r => r.external_id)).size;\n\n// loose synonym match so \"k8s\" in your text covers demand for \"kubernetes\"\nconst ALIAS = {\n kubernetes: ['k8s', 'k3s', 'eks', 'gke', 'aks'], aws: ['amazon web services'],\n 'google cloud': ['gcp'], azure: ['aks'], 'ci/cd': ['cicd', 'jenkins', 'github actions', 'gitlab ci'],\n go: ['golang'], postgres: ['postgresql'], observability: ['monitoring', 'prometheus', 'grafana'],\n terraform: ['iac'], javascript: ['js'], typescript: ['ts'],\n};\nconst has = (name) => skillsText.includes(name) || (ALIAS[name] || []).some(a => skillsText.includes(a));\n\nconst ranked = Object.entries(demand)\n .map(([name, d]) => ({ name, mentions: d.mentions, must: d.must, weight: d.mentions, covered: has(name) }))\n .sort((a, b) => b.weight - a.weight || b.must - a.must);\n\n// score over the full demand; show only the most-wanted skills so the page stays readable\nlet wSum = 0, wGot = 0;\nfor (const r of ranked) { wSum += r.weight; if (r.covered) wGot += r.weight; }\nconst list = ranked.slice(0, 30);\nconst score = wSum ? Math.round((wGot / wSum) * 100) : 0;\nconst maxW = Math.max(1, ...list.map(r => r.weight));\n\nconst esc = v => String(v ?? '').replace(/&/g, '&').replace(//g, '>');\nconst bar = w => `
`;\nconst tr = r => {\n const [c, l] = r.covered ? ['#22c55e', 'have'] : ['#ef4444', 'gap'];\n return `${esc(r.name)}${bar(r.weight)}${r.mentions} of ${totalJobs} jobs${r.must ? ` \u00b7 ${r.must} must` : ''}${l}`;\n};\nconst empty = list.length === 0;\n\nconst html = `\n\nmind the gap
\n

mind the gap

\n
market demand vs. your skills \u2014 ${totalJobs} job${totalJobs === 1 ? '' : 's'} scanned${empty ? '' : ', skills ranked by how many list them'}
\n${empty ? '

No skills extracted yet. Check the keywords in the Config node and that your LLM credential is set on Extract (LLM).

' : `\n
${score}%
\n
share of in-demand skills your text covers \u00b7 updated ${new Date().toISOString().slice(0, 16).replace('T', ' ')} UTC
\n

demand radar

\n${list.map(tr).join('\\n')}
`}\n\n
`;\n\n// cache the finished page in the workflow's static data so refreshes are STABLE\n// (served by the cache gate) until the TTL expires or ?refresh=1 forces a recompute.\nconst store = $getWorkflowStaticData('global');\nconst sig = JSON.stringify({\n k: $('Set Job Search Criteria').first().json.keywords || [],\n s: $('Record Your Skills Data').first().json.skillsText || '',\n});\nstore.radar = { html, ts: Date.now(), sig };\n\nreturn [{ json: { html } }];\n" }, "typeVersion": 2 }, { "id": "resp", "name": "Provide HTML Response", "type": "n8n-nodes-base.respondToWebhook", "position": [ 2896, 208 ], "parameters": { "options": { "responseHeaders": { "entries": [ { "name": "Content-Type", "value": "text/html; charset=utf-8" } ] } }, "respondWith": "text", "responseBody": "={{ $json.html }}" }, "typeVersion": 1.5 }, { "id": "config", "name": "Set Job Search Criteria", "type": "n8n-nodes-base.code", "position": [ 368, 224 ], "parameters": { "jsCode": "// EDIT THIS \u2014 what role / skills are you scanning the market for?\nconst config = {\n keywords: [\"engineer\"], // ANY role/skills \u2014 e.g. [\"product manager\"], [\"nurse\"]. Empty = whole feed.\n maxJobs: 25, // one LLM call per job in a single pass \u2014 keep this modest.\n feedPages: 4, feedPerPage: 100,\n llmUrl: \"https://integrate.api.nvidia.com/v1/chat/completions\", // any OpenAI-compatible endpoint\n llmModel: \"meta/llama-3.1-8b-instruct\",\n};\nreturn [{ json: config }];" }, "typeVersion": 2 }, { "id": "registry", "name": "Record Your Skills Data", "type": "n8n-nodes-base.code", "position": [ 608, 224 ], "parameters": { "jsCode": "// EDIT THIS \u2014 paste anything that describes what you know: a CV paragraph, a list of tools,\n// prose, comma-separated keywords. No format and no proof required. Any skill the market\n// wants that also appears in this text counts as one you \"have\"; the rest are gaps.\nconst skillsText = `python, javascript, typescript, react, node, sql, postgres, aws, docker, kubernetes,\nterraform, ci/cd, linux, git, rest api, communication, agile, project management\nI've built and run Grafana/Prometheus monitoring in production and write a lot of Bash and Go.`;\n\nreturn [{ json: { skillsText } }];\n" }, "typeVersion": 2 } ], "active": true, "settings": { "binaryMode": "separate", "callerPolicy": "workflowsFromSameOwner", "availableInMCP": false, "executionOrder": "v1" }, "versionId": "3ae46e86-9065-4ff8-9f6e-b295382eccf0", "connections": { "Post to LLM API": { "main": [ [ { "node": "Analyze Extracted Skills", "type": "main", "index": 0 } ] ] }, "Trigger Manually": { "main": [ [ { "node": "Set Job Search Criteria", "type": "main", "index": 0 } ] ] }, "Rank Job Listings": { "main": [ [ { "node": "Build Extraction Request", "type": "main", "index": 0 } ] ] }, "Generate Radar View": { "main": [ [ { "node": "Provide HTML Response", "type": "main", "index": 0 } ] ] }, "Check Cache Freshness": { "main": [ [ { "node": "Provide HTML Response", "type": "main", "index": 0 } ], [ { "node": "Fetch Recent Job Listings", "type": "main", "index": 0 } ] ] }, "Manage Cache Stability": { "main": [ [ { "node": "Check Cache Freshness", "type": "main", "index": 0 } ] ] }, "Webhook for Radar View": { "main": [ [ { "node": "Set Job Search Criteria", "type": "main", "index": 0 } ] ] }, "Record Your Skills Data": { "main": [ [ { "node": "Manage Cache Stability", "type": "main", "index": 0 } ] ] }, "Set Job Search Criteria": { "main": [ [ { "node": "Record Your Skills Data", "type": "main", "index": 0 } ] ] }, "Analyze Extracted Skills": { "main": [ [ { "node": "Generate Radar View", "type": "main", "index": 0 } ] ] }, "Build Extraction Request": { "main": [ [ { "node": "Post to LLM API", "type": "main", "index": 0 } ] ] }, "Fetch Recent Job Listings": { "main": [ [ { "node": "Rank Job Listings", "type": "main", "index": 0 } ] ] } } }