# linkme

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.

Two things run on their own once installed:

| automation | when | what it does |
|---|---|---|
| **daily digest** | 08:00 + 18:00 | fetch every search, push new profile-matching jobs to Telegram |
| **sciCORE watch** | 08:47 | alert if sciCORE's open-positions page lists a *software* role |

## Quick start

```bash
bun fetch      # scrape all searches, append new jobs to results/jobs.csv
bun daily      # fetch, then Telegram the new jobs that match the profile
bun recheck    # show what the filters are currently dropping + keyword suggestions
```

`bun fetch` needs no setup — reruns are safe, already-seen jobs are skipped by ID. Telegram needs the two-line setup below.

## Telegram setup

```bash
# 1. Message @BotFather → /newbot → copy the token into .env
echo 'TELEGRAM_BOT=<token>' >> .env

# 2. Send your bot any message ("hi"), then discover your chat ID
bun run telegram.ts --setup      # prints TELEGRAM_CHAT_ID=... — paste it into .env
```

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.

`.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.

## Structure

```
config.ts          # keyword families, search areas, the sweep() generator
fetch.ts           # LinkedIn scraper, HTML parser, all title/content filters
fetch-other.ts     # extra sources: itjobs.ch, job-room.ch, stellen-basel.ch,
                   #   datacareer.ch (403, currently dead), Basel intl orgs
index.ts           # orchestrator: run searches → dedupe → content-check → CSV
recheck.ts         # raw-mode fetch: lists what the filters dropped
daily.ts           # digest: fetch, match against the profile, send to Telegram
profile.ts         # title-level scoring — decides what's worth a notification
telegram.ts        # Telegram delivery + --setup chat-ID discovery
scripts/           # one-off searches (*-temp.ts) and scicore-watch.sh
cv/polo.md         # the profile everything is ranked against
results/jobs.csv   # output, one row per job
results/sent.txt   # links already pushed — never sent twice
```

## How a job gets through

Two stages. Cheap title filters first, then one page fetch per survivor.

**Title filters** (inside `fetchJobs`, no network cost):

1. **Switzerland guard** — the card's location must say Switzerland. LinkedIn ignores `geoId` on unauthenticated requests and will happily return global results.
2. `NON_ENGLISH_RE` — German gender markers (`m/w/d`, `/in`), umlauts, French `H/F` and accents.
3. `JUNIOR_TITLE_RE` — junior, intern, trainee, werkstudent, entry-level.
4. `JAVA_TITLE_RE` — Java-titled roles.
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.
6. `SOFTWARE_TITLE_RE` — requires a real tech/role term. Skipped in `raw` mode, which is what `bun recheck` uses.
7. `GERMAN_TITLE_RE` — "(German-speaking)", "deutschsprachig".

**Content check** (`index.ts` only, one fetch + 350 ms per job): `isViableContent` counts German/French indicator words and drops at ≥ 6, then `requiresGerman()` drops English postings demanding German above B1.

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.

**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).

Jobs are deduped globally by ID against `results/jobs.csv` on startup, so nothing is ever fetched or sent twice.

## Output

`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.

## Configuring searches

`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).

```ts
export const searches: SearchConfig[] = [
  {
    label: "Basel (city)",
    keywords: KEYWORDS,
    geoId: "103383283",
    workType: ["onsite", "hybrid"],
    timePosted: "month",
  },
];
```

`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.

Known Swiss geoIds:

| area | geoId |
|---|---|
| Basel (city) | `103383283` |
| Basel Metropolitan Area | `90009885` |
| Basel-Country | `107117530` |
| Zürich (city) | `102436504` |
| Zürich Metropolitan Area | `107814425` |
| Zürich Area | `90009888` |

## One-off searches

Don't edit `config.ts` for a single search — write a throwaway in `scripts/`:

```ts
import { KEYWORDS } from "../config.ts";
import { fetchJobs } from "../fetch.ts";

const jobs = await fetchJobs({
  label: "Luzern", keywords: KEYWORDS,
  location: "Luzern, Switzerland",
  workType: ["onsite", "hybrid"], timePosted: "month",
});
for (const j of jobs) console.log(j.title, j.company, j.link);
```

```bash
bun run scripts/luzern-temp.ts
```

`*-temp.ts` scripts are disposable — delete them when the search is done.

## Scheduling

Both automations are launchd agents, not cron — this machine has no cron at all.

```bash
launchctl list | grep -E 'linkme|scicore'                     # are they loaded?
launchctl unload ~/Library/LaunchAgents/com.rafapolo.linkme-daily.plist   # stop the digest
launchctl unload ~/Library/LaunchAgents/ch.polo.scicore-watch.plist       # stop the watcher
```

If the Mac is asleep at the scheduled time, launchd runs the job once it wakes rather than skipping the day.

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:

```bash
SCICORE_URL="file://$PWD/fixture.html" bash scripts/scicore-watch.sh
```

## recheck

`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.