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https://github.com/MadsLorentzen/ai-job-search.git
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Ayobami Adegoke
fce2cf23c0
/scrape finds and dedupes postings; /apply evaluates one at a time in depth. Nothing connects the two ends: after a scrape returns 20 jobs, the user eyeballs a table to decide where to spend /apply effort. /rank is the bridge: batch-score every new posting against the fit framework and return a ranked shortlist. How it works: - Selects jobs with status "new" from job_scraper/seen_jobs.json (--all re-ranks everything unapplied; a focus argument filters), excluding anything already in job_search_tracker.csv - Dispatches parallel general-purpose agents (~5 jobs each) that WebFetch each posting and score the five dimensions from 04-job-evaluation.md. The rubric (skill match areas, career goals, deal-breakers) is passed inline per the same token-efficiency rules /apply uses; agents score only from actually fetched content and mark dead postings expired, never guessing from a title - Triage depth by design: posting text vs. profile only - no company research, no salary lookups. /apply's Step 1 evaluation stays authoritative and always re-runs on handoff - Aggregates with the framework's 30/25/15/30 weighting and verdict bands; location deal-breakers veto regardless of score; deadlines within 7 days get urgency flags and win ties - Updates seen_jobs.json additively (status "ranked"/"expired" plus rank_score/rank_verdict/rank_date) so /scrape dedup keeps working; the tracker is read-only. Re-running is idempotent Integration: job-scraper SKILL.md documents the new status values and suggests /rank after large scrape batches; README (commands list, file tree, quick-start step 4).
126 lines
4.5 KiB
Markdown
126 lines
4.5 KiB
Markdown
---
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name: job-scraper
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description: >
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Scrapes Danish job sites for new positions matching your profile. Deduplicates across runs.
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Triggers on: job scrape, find jobs, search jobs, new jobs, job search, scrape jobs, /scrape
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allowed-tools: Read, Write, Edit, Glob, Grep, WebFetch, WebSearch, Agent, AskUserQuestion
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---
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# Job Scraper
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---
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## How It Works
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This skill searches multiple Danish job sites using targeted queries based on your profile, deduplicates against previously seen jobs and the application tracker, and presents new matches with a quick fit assessment.
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## Invocation
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The user triggers this skill by saying things like:
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- "Find new jobs"
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- "Scrape for jobs"
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- "Any new positions?"
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- "/scrape"
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Optional arguments:
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- A focus area, e.g. "/scrape data science" or "/scrape geophysics"
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- "broad" to run all search categories, e.g. "/scrape broad"
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---
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## Execution Steps
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### Step 0: Load State
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1. Read `job_scraper/seen_jobs.json` (create if missing - start with `{"seen": {}}`)
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2. Read `job_search_tracker.csv` to extract already-applied companies+roles
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3. Read `search-queries.md` (this directory) for the search strategy
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### Step 1: Search
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Run **WebSearch** queries from `search-queries.md`. By default, run the top 3 priority categories. If the user said "broad", run all categories.
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If the user specified a focus area (e.g. "data science"), prioritize queries from that category.
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For each search:
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- Use `WebSearch` with site-specific queries (jobindex.dk, linkedin.com/jobs, karriere.dk, etc.)
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- Target your configured geographic area
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- Look for postings from the last 14 days
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### Step 2: Fetch & Parse
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For each promising result from Step 1:
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- Use `WebFetch` to retrieve the job posting page
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- Extract: **job title**, **company**, **location**, **posting date** (or "recent"), **URL**, **key requirements** (brief), **application deadline** (if listed)
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- Skip if the URL or company+title combo already exists in `seen_jobs.json`
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- Skip if the company+role already appears in `job_search_tracker.csv`
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### Step 3: Quick Fit Assessment
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For each new job, do a rapid fit check (NOT the full evaluation from `04-job-evaluation.md` - just a quick signal):
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- **High match**: Role directly involves your core skills
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- **Medium match**: Role is adjacent to your experience
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- **Low match**: Role requires significant skills you lack
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### Step 4: Deduplicate & Store
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1. Add ALL fetched jobs (new and skipped) to `seen_jobs.json` with structure:
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```json
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{
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"seen": {
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"<url_or_company_title_key>": {
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"title": "...",
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"company": "...",
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"url": "...",
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"first_seen": "YYYY-MM-DD",
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"fit": "high/medium/low",
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"status": "new/skipped/evaluated/ranked/expired"
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}
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}
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}
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```
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2. Only present jobs NOT already in the seen list or tracker.
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### Step 5: Present Results
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Present new jobs in a table sorted by fit (high first):
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```
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## New Job Matches - YYYY-MM-DD
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Found X new positions (Y high, Z medium, W low match).
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| # | Fit | Title | Company | Location | Deadline | URL |
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|---|-----|-------|---------|----------|----------|-----|
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| 1 | High | ... | ... | ... | ... | [Link](...) |
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### High-Match Highlights
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For each high-match job, add 2-3 bullet points:
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- Why it matches your profile
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- Key requirements to check
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- Any red flags
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```
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After presenting, ask:
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> "Want me to evaluate any of these in detail? Just give me the number(s)."
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If the user picks a number, invoke the **job-application-assistant** skill workflow (fit evaluation first, then CV + cover letter if approved).
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If the run found many new jobs (roughly 8+), also suggest `/rank` - it batch-scores all new postings against the full fit framework and returns a ranked shortlist, which beats eyeballing a long table. (`/rank` sets the `ranked` and `expired` status values in `seen_jobs.json`; treat both as already-seen for dedup purposes.)
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### Step 6: Update Tracker (Optional)
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If the user decides to apply to any job, add a row to `job_search_tracker.csv`.
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---
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## Important Rules
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1. **Never fabricate job postings.** Only present jobs found via actual WebSearch/WebFetch results.
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2. **Respect deduplication.** Always check seen_jobs.json AND job_search_tracker.csv before presenting.
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3. **Focus on configured geographic area.** Skip jobs that require relocation or are clearly outside commute range.
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4. **Only open positions.** Skip postings with expired deadlines or those marked as closed.
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5. **Be efficient with WebFetch.** Don't fetch every search result - use titles and snippets to pre-filter before fetching.
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6. **Parallel searches.** Use the Agent tool or parallel WebSearch calls to speed up the search phase.
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