153 lines
7.8 KiB
Markdown
153 lines
7.8 KiB
Markdown
# linkme
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A personal job-hunting pipeline for the Swiss market. It scrapes LinkedIn (plus a handful of Swiss boards), filters out everything that doesn't fit the profile in `cv/polo.md`, appends what survives to a CSV, and pushes the good ones to Telegram twice a day.
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Two things run on their own once installed:
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| automation | when | what it does |
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| **daily digest** | 08:00 + 18:00 | fetch every search, push new profile-matching jobs to Telegram |
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| **sciCORE watch** | 08:47 | alert if sciCORE's open-positions page lists a *software* role |
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## Quick start
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```bash
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bun fetch # scrape all searches, append new jobs to results/jobs.csv
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bun daily # fetch, then Telegram the new jobs that match the profile
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bun recheck # show what the filters are currently dropping + keyword suggestions
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```
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`bun fetch` needs no setup — reruns are safe, already-seen jobs are skipped by ID, and every search/source runs concurrently (a full run is well under 2 minutes). Telegram needs the two-line setup below.
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## Telegram setup
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```bash
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# 1. Message @BotFather → /newbot → copy the token into .env
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echo 'TELEGRAM_BOT=<token>' >> .env
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# 2. Send your bot any message ("hi"), then discover your chat ID
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bun run telegram.ts --setup # prints TELEGRAM_CHAT_ID=... — paste it into .env
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```
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Then `bun daily --dry-run` prints the digest instead of sending it, and `bun daily --resend` re-sends today's jobs from the CSV without re-fetching.
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`.env` also holds `LI_AT`, a LinkedIn session cookie used only for fetching individual *profile* pages. Never send it with a job search — with the cookie LinkedIn serves a React SPA instead of server-rendered HTML and the card parser gets nothing. `.env` is gitignored.
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## Structure
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```
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config.ts # keyword families, search areas, the sweep() generator
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fetch.ts # LinkedIn scraper, HTML parser, all title/content filters
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fetch-other.ts # extra sources: itjobs.ch, job-room.ch, stellen-basel.ch,
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# jobbasel.ch, englishjobsearch.ch, Basel intl orgs,
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# datacareer.ch (403, currently dead)
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index.ts # orchestrator: run searches → dedupe → content-check → CSV
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recheck.ts # raw-mode fetch: lists what the filters dropped
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daily.ts # digest: fetch, match against the profile, send to Telegram
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profile.ts # title-level scoring — decides what's worth a notification
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telegram.ts # Telegram delivery + --setup chat-ID discovery
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scripts/ # one-off searches (*-temp.ts) and scicore-watch.sh
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cv/polo.md # the profile everything is ranked against
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results/jobs.csv # output, one row per job
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results/sent.txt # links already pushed — never sent twice
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```
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## How a job gets through
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Two stages. Cheap title filters first, then one page fetch per survivor.
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**Title filters** (inside `fetchJobs`, no network cost):
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1. **Switzerland guard** — the card's location must say Switzerland. LinkedIn ignores `geoId` on unauthenticated requests and will happily return global results.
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2. `NON_ENGLISH_RE` — German gender markers (`m/w/d`, `/in`), umlauts, French `H/F` and accents.
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3. `JUNIOR_TITLE_RE` — junior, intern, trainee, werkstudent, entry-level.
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4. `JAVA_TITLE_RE` — Java-titled roles.
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5. `BIO_DOMAIN_RE` — bioinformatics, biostatistics, genomics, GMP/MSAT, bench-scientist titles. Filters on the **role, not the employer**: software and data jobs at Roche, Novartis or Swiss TPH stay in.
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6. `SOFTWARE_TITLE_RE` — requires a real tech/role term. Skipped in `raw` mode, which is what `bun recheck` uses.
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7. `GERMAN_TITLE_RE` — "(German-speaking)", "deutschsprachig".
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**Content check**: `isViableContent`/`isViableUrl` (LinkedIn, one page fetch + 350 ms per job in `index.ts`) and `isViableDescription` (`fetch-other.ts` sources that already carry the description inline, no extra fetch) all run the same two checks — DE/FR indicator words dropped at ≥ 6, then `requiresGerman()` drops English postings demanding German above B1.
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That last one is deliberately more than a keyword match. For each mention of German it takes the *nearest* qualifying marker, and ignores a marker when another language name sits between it and the mention — both rules came from real false results, like *"Fluent in English; German and/or French advantageous"*, where "Fluent" sits closer to "German" than "advantageous" does but qualifies English.
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**Profile match** (`profile.ts`, digest only) scores the title against the CV's stack and seniority signals, needing 4+ points to notify. Hard rejects encode standing decisions: Microsoft stack, Java/C++/SAP, life-science domains, non-software functions, and the blocked companies Jobgether (an aggregator, not an employer) and ALLPS (postings require German despite passing the English filter).
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Jobs are deduped globally by ID against `results/jobs.csv` on startup, so nothing is ever fetched or sent twice.
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## Output
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`results/jobs.csv` — `date_found`, `posted_at`, `search`, `title`, `company`, `link`, `source`. Open it in any spreadsheet and delete rows as you apply or dismiss.
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## Configuring searches
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`config.ts` holds four keyword families as separate OR-strings — `KEYWORDS`, `PLATFORM_KEYWORDS`, `RESEARCH_KEYWORDS`, `FIELD_KEYWORDS`. They're kept apart on purpose: LinkedIn ranks a focused query far better than one 50-term disjunction. `sweep()` generates one search per area per family (Research skips Bern; Platform and Field add a Switzerland-wide remote pass).
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```ts
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export const searches: SearchConfig[] = [
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{
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label: "Basel (city)",
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keywords: KEYWORDS,
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geoId: "103383283",
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workType: ["onsite", "hybrid"],
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timePosted: "month",
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},
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];
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```
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`SearchConfig` also takes `jobType`, `seniority`, and `location` (use `"City, Switzerland"` when the geoId is unknown — LinkedIn resolves by name). A `url:` field overrides all filter params, so you can paste a LinkedIn search URL directly.
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Known Swiss geoIds:
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| area | geoId |
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| Basel (city) | `103383283` |
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| Basel Metropolitan Area | `90009885` |
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| Basel-Country | `107117530` |
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| Zürich (city) | `102436504` |
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| Zürich Metropolitan Area | `107814425` |
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| Zürich Area | `90009888` |
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## One-off searches
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Don't edit `config.ts` for a single search — write a throwaway in `scripts/`:
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```ts
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import { KEYWORDS } from "../config.ts";
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import { fetchJobs } from "../fetch.ts";
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const jobs = await fetchJobs({
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label: "Luzern", keywords: KEYWORDS,
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location: "Luzern, Switzerland",
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workType: ["onsite", "hybrid"], timePosted: "month",
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});
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for (const j of jobs) console.log(j.title, j.company, j.link);
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```
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```bash
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bun run scripts/luzern-temp.ts
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```
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`*-temp.ts` scripts are disposable — delete them when the search is done.
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## Scheduling
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Both automations are launchd agents, not cron — this machine has no cron at all.
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```bash
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launchctl list | grep -E 'linkme|scicore' # are they loaded?
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launchctl unload ~/Library/LaunchAgents/com.rafapolo.linkme-daily.plist # stop the digest
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launchctl unload ~/Library/LaunchAgents/ch.polo.scicore-watch.plist # stop the watcher
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```
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If the Mac is asleep at the scheduled time, launchd runs the job once it wakes rather than skipping the day.
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Logs: `results/daily.log` for the digest, `results/.scicore-watch/watch.log` for the watcher. The watcher logs every run — `no change`, `change ignored`, or the diff it sent — so silence on your phone can always be told apart from a broken script. Test its alert path without waiting for the real page to change:
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```bash
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SCICORE_URL="file://$PWD/fixture.html" bash scripts/scicore-watch.sh
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```
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## recheck
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`bun recheck` re-runs every search with `SOFTWARE_TITLE_RE` bypassed and lists each job the filters would drop, plus a word-frequency table of those titles. Use it to spot false negatives; ignore the generic suggestions (`engineer`, `development`, `senior`) and look for real role terms worth adding to the regex.
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