# Linkedin Jobs Fetch > Fetch saved jobs from LinkedIn's jobs tracker using Playwright with a persisted session. Optionally add or edit notes on jobs. Use when the user wants to retrieve their LinkedIn saved jobs or annotate them. - Skill: `rubenviolinha/linkedin-jobs-fetch` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add rubenviolinha/linkedin-jobs-fetch` - Raw SKILL.md: https://api.skillmd.com/api/skills/rubenviolinha/linkedin-jobs-fetch/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: rubenviolinha (https://skillmd.com/u/rubenviolinha) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/rubenviolinha/linkedin-jobs-fetch --- # LinkedIn Jobs Fetch Skill Automate fetching saved jobs from https://www.linkedin.com/jobs-tracker/?stage=saved using Playwright with a persisted session stored in `~/.claude/linkedin-session.json`. The working script is bundled with this skill as `linkedin_fetch.mjs` — don't rewrite it from scratch, it already handles LinkedIn's quirks (see "How the script handles LinkedIn's DOM" below). ## Two modes 1. **Scrape** (default) — fetch all saved jobs as clean JSON, then pass to job-analyzer 2. **Add/edit notes** (`--add-notes`) — write or update notes on specific jobs --- ## Scrape mode ### Step 1 — Ensure playwright + the bundled script are in place ```bash mkdir -p /tmp/pw-runner ls /tmp/pw-runner/node_modules/playwright 2>/dev/null || (cd /tmp/pw-runner && echo '{"type":"module"}' > package.json && npm install playwright --save && npx playwright install chromium) cp ~/.claude/skills/linkedin-jobs-fetch/linkedin_fetch.mjs /tmp/pw-runner/linkedin_fetch.mjs ``` ### Step 2 — Run the script ```bash cd /tmp/pw-runner && node linkedin_fetch.mjs 2>&1 ``` **First run (no session yet):** A browser window opens to the LinkedIn login page. Tell the user: > "A browser window has opened. Please log in to LinkedIn. Once you're on the home page (linkedin.com/feed), run this in a new terminal: `touch /tmp/linkedin-ready`" Wait for the user to confirm, then the script saves the session automatically. Future runs skip login. **Subsequent runs:** Uses `~/.claude/linkedin-session.json` — no interaction needed. If the session has expired, the script detects the login/authwall redirect and prints a clear message to delete the session file and re-run. ### Step 3 — Parse output and analyze The script prints a JSON block between `=== ALL JOBS BY TAB ===` and `=== END ===`, with each job parsed into `title`, `company`, `posted`, and `url`: ```json { "Saved": [ { "stage": "saved", "id": "123", "title": "...", "company": "... · Oslo", "posted": "Posted 2d ago", "url": "https://www.linkedin.com/jobs/view/123/" } ] } ``` Extract it and pass to the **job-analyzer** skill for full analysis. --- ## Add/edit notes mode Two sub-workflows: **A) AI analysis notes** — fetch all saved jobs, run job-analyzer on every JD, then write a one-line verdict (rating + reason) as a note on each job card. The user sees the assessment directly in their LinkedIn tracker. **B) Manual notes** — user specifies specific jobs and custom text. ### Step 1 — Build a notes JSON file Create `/tmp/job-notes.json` mapping job URLs to note text (max ~250 chars per note): ```json { "https://www.linkedin.com/jobs/view/1234567890/": "⭐⭐⭐⭐⭐ Strong fit. Domain and seniority align well. Apply.", "https://www.linkedin.com/jobs/view/9876543210/": "❌ Target language required. Hard blocker." } ``` For the AI-analysis workflow, fetch all jobs first (scrape mode), run job-analyzer on all JDs, then build this JSON with a short verdict per job. ### Step 2 — Run with --add-notes flag ```bash cd /tmp/pw-runner && node linkedin_fetch.mjs --add-notes /tmp/job-notes.json 2>&1 ``` For each job ID the script auto-detects which path to use: - **No note yet →** clicks the inline **"Add note"** link in the card's Notes cell. - **Note already exists →** there is NO inline link. The script clicks the **⋯ overflow menu** button (next to "Apply", `aria-label="Overflow menu"`), then clicks **"Edit note"** in the popover that appears. Both open the same modal; the script fills the textarea and clicks **Save**. Notes are idempotent — adding to a job that already has a note overwrites it via the edit path. A verification screenshot of the final tracker state is written to `/tmp/linkedin-notes-result.png` — read it to confirm the notes landed. **If it crashes mid-run:** create a partial JSON with only the remaining jobs and re-run. It's safe to re-run — re-writing/editing an existing note just overwrites it. --- ## How the script handles LinkedIn's DOM - **Each card is rendered twice** (a tile layout and a table-row layout); only one copy is visible at a given width. The script always targets the **visible** copy (checks `offsetParent` + client rects) — never a blind `.first()`, which would hang ~30s on a hidden element and can close the browser ("stuck"). Targeting is done by tagging the right element with a `data-pw-*` attribute inside `page.evaluate`, then acting on it with a Playwright locator (auto-scroll + actionability waits). - **The list virtualizes rows**, so the scraper accumulates cards while scrolling incrementally rather than taking a single snapshot. - **Scrape output is parsed**, not a raw blob: title / company / posted come from the card's `

` tags, preferring the visible (non-empty `innerText`) copy. --- ## Troubleshooting - **Session expired** — the script auto-detects the login/authwall redirect and tells you. Delete `~/.claude/linkedin-session.json` and re-run; the login flow will trigger again. - **"Add note" link / overflow menu not found** — the job may not be on the saved tab, or LinkedIn changed the DOM. The script walks up from the *visible* job-link anchor to its card, then looks for either an inline ``/`