scrape

Job Scraper

How It Works

This skill searches job portals using the installed portal-search CLIs in .agents/skills/ (plus WebSearch as a fallback), using queries from your profile. It deduplicates against previously seen jobs and the application tracker, and presents new matches with a quick fit assessment.

Invocation

The user triggers this skill by saying things like:

• "Find new jobs"
• "Scrape for jobs"
• "Any new positions?"
• "/scrape"

Optional arguments:

• A focus area, e.g. "/scrape data science" or "/scrape geophysics"
• "broad" to run all search categories, e.g. "/scrape broad"
• "health" to run the portal health check only (Step 4.75), without searching, deduplicating, or presenting jobs - e.g. "/scrape health", or "/scrape health jobnet" to probe one portal even if disabled

Execution Steps

Step 0: Load State

1. Read job_scraper/seen_jobs.json (create if missing - start with {"seen": {}})
2. Read job_search_tracker.csv to extract already-applied companies+roles
3. Read search-queries.md (this directory) for the search strategy

Step 1: Search

Read search-queries.md (this directory) for the search strategy. By default, run the top 3 priority query categories. If the user said "broad", run all categories. If the user specified a focus area (e.g. "data science"), prioritize queries from that category.

Use the installed CLI tools as the primary search mechanism. Fall back to WebSearch only for portals that do not have a CLI skill, or if bun is unavailable on the system.

1a. Check bun availability

bun --version

If this fails (bun not installed), skip to 1c (WebSearch fallback) for all portals and note the fallback in the Step 5 output.

1b. Run CLI tools (primary — run these in parallel where possible)

Discover all installed portal CLI skills by reading every SKILL.md found under .agents/skills/*/SKILL.md. Each file documents that portal's exact CLI flags and usage examples. Use each portal's own documented interface — do not guess flags. This approach automatically includes any new portals added via /add-portal without requiring changes to this file.

Honor the enabled toggle. A portal is enabled unless its SKILL.md frontmatter sets enabled: false (a missing key means enabled — the default). Skip each disabled portal and record it for the Step 5 summary. A fork can thus keep a portal installed but sit out a run without deleting its directory.

For each enabled portal skill:

1. Read its SKILL.md to find the correct bun run … invocation and supported flags.
2. Translate the query terms from search-queries.md into that portal's flag format (e.g. --key, --search-string, --query, filter codes — whatever the portal's SKILL.md specifies).
3. Scope to the last 14 days using the portal's supported recency flag (--jobage, --since <YYYY-MM-DD>, --order PublicationDate, etc. — as documented per portal).
4. Cap results to ~20 per call using the portal's limit flag.
5. Use --format json for machine-readable output.

Run all portal CLI calls in parallel where possible using the Agent tool. Collect all results arrays into a single pool for Step 2, keeping each result tagged with its source portal skill (for Step 2 detail lookups).

If a CLI tool exits with a non-zero code, log the error message and continue — do not abort the whole search.

1c. WebSearch fallback

Use WebSearch for:

• Portals listed in search-queries.md that do not have a corresponding directory under .agents/skills/
• Any portal whose CLI fails at runtime
• When bun is unavailable (Step 1a failed)

Use the site-specific query strings from search-queries.md directly as WebSearch queries for these portals.

Step 2: Fetch & Parse

For each promising result from Step 1:

From CLI results: Search output already includes title, company, location, date, and URL. For jobs worth a deeper look, fetch full detail with that portal's detail command (see its SKILL.md — do not guess flags) to extract key requirements, application deadline, and a brief description snippet.

From WebSearch results: Use WebFetch on the posting URL and extract the same fields manually.

For every candidate:

• Skip if the URL or company+title combo already exists in seen_jobs.json
• Skip if the company+role already appears in job_search_tracker.csv

Step 2.5: Mass-Posting Detection (within this run)

A distribution pattern worth flagging to the user as a caution signal, not as an accusation against the employer - it describes how a listing is being distributed, not a verdict on whether the company is legitimate. It alone proves nothing is wrong (companies do legitimately hire the same role across several cities); flag it so the user can factor it in when deciding whether to invest time, don't downgrade fit or silently exclude the result because of it.

If two or more results in this run's pool (from the same company, or sharing the same req/job ID visible in the URL or title) have substantially the same description and differ only in city/location/title, don't present them as separate rows. Consolidate into a single row and note the spread, e.g. "posted identically across 6 cities (BR, MX, GT)".

Step 3: Quick Fit Assessment

For each new job, do a rapid fit check (NOT the full evaluation from 04-job-evaluation.md - just a quick signal):

• High match: Role directly involves your core skills
• Medium match: Role is adjacent to your experience
• Low match: Role requires significant skills you lack

Language override: before assigning a match level, check the posting against 04-job-evaluation.md's Language Gate (a required language you haven't declared at all in your CLAUDE.md Languages table). A required language that's entirely undeclared overrides skill fit: mark it Low regardless of how well the skills align, and name it in the highlight bullets so it isn't buried under an otherwise-good-looking match. A declared language at a requirement that reads higher than your declared level is not an override — score fit normally, but add a red-flag bullet under that job's highlights (Step 5) quoting the posting's requirement next to your declared level, so the gap is visible without being auto-downgraded.

Step 4: Deduplicate & Store

1. Add ALL fetched jobs (new and skipped) to seen_jobs.json with structure:

{
  "seen": {
    "<url_or_company_title_key>": {
      "title": "...",
      "company": "...",
      "url": "...",
      "first_seen": "YYYY-MM-DD",
      "fit": "high/medium/low",
      "status": "new/skipped/evaluated/ranked/expired",
      "portal": "<source portal skill, e.g. jobindex-search>"
    }
  }
}

The portal field records which CLI skill produced the job (results are already tagged per portal in Step 1b - persist that tag here). Entries written before this field existed lack it; the health check (Step 4.75) attributes those by matching the URL's domain against each portal's base URL, so do not backfill.

/rank extends this schema additively: ranked entries also carry rank_score (0–100 overall score), rank_verdict (fit band, e.g. "strong fit"), rank_date (ISO date of ranking), and strengths/gaps (1-3 verbatim bullets each, copied from the scoring agent's findings). The status field is set to "ranked". Do not drop any of these fields when re-writing entries. Entries ranked before strengths/gaps existed simply lack them; readers tolerate their absence and never backfill by guessing.

2. Only present jobs NOT already in the seen list or tracker.

Step 4.5: Generate Referral Contact Links (High & Medium Fit Only)

For every job from this run with fit of high or medium (skip low-fit jobs), build two LinkedIn people-search URLs so the user can find a recruiter or team member to reach out to for a referral or a warm intro. This is deliberately a link-generation step, not an automated lookup: no scraping, no third-party API, zero runtime dependencies or credentials required.

A. Recruiters / Talent Acquisition (the referral path)

https://www.linkedin.com/search/results/people/?keywords=<url-encoded "<Company Name> recruiter">&origin=GLOBAL_SEARCH_HEADER

B. Role/team peers (informational-outreach / warm-intro path)

https://www.linkedin.com/search/results/people/?keywords=<url-encoded "<Company Name> <role keyword>">&origin=GLOBAL_SEARCH_HEADER

Use a short keyword drawn from the posting's title for <role keyword> - e.g. a posting titled "AI Program Manager" becomes "<Company Name> AI Program Manager".

Both links are for the user to open and browse themselves - never fetch or scrape the LinkedIn people-search result pages programmatically. Never fabricate contacts or claim a specific person was found; these are search links, not results.

Step 4.75: Portal Health Check

Scraper-based portal CLIs rot silently: when a portal changes its markup, the parser usually exits 0 with zero results or with null/garbled fields, and the Step 1c fallback never fires because it only triggers on hard failure. This step catches that from evidence the run already holds.

Free pass (no extra requests). For each enabled portal that ran in Step 1b:

• Degraded scan: inspect the results it returned this run. Flags: company null or empty on every result, empty titles, undecoded entities (&) or HTML fragments in titles, URLs that do not point at the portal. Any of these means the parser is half-working and /scrape is silently collecting junk.
• Yield history: if the portal returned zero results across all of this run's queries, check whether seen_jobs.json holds prior entries from it (via the portal field, or by matching URL domains for entries predating the field). A portal that produced jobs on earlier runs and produces nothing now is suspect - the same queries worked before.

Escalation (bounded, on suspicion only). A suspect portal gets one sentinel probe: run its documented search with the example query from its own SKILL.md (that query provably worked when the skill was registered), the portal's limit flag capped at 3, --format json. If that returns nothing, retry once with a single common word. Only then is the verdict broken. A 429 or block page is never evidence of breakage - record the portal as inconclusive (rate-limited), back off, and do not retry.

Verdicts. Healthy portals get silence - no table, no line. Anything else surfaces in the Step 5 summary as a health line.

Probe-only mode (/scrape health). Skip Steps 1-4 and this step's free pass (there is no fresh run to scan); instead probe every installed portal directly - enabled ones by default, a disabled one only when named explicitly (e.g. /scrape health jobnet). Each portal gets the sentinel probe above, the degraded criteria applied to whatever it returns, and - since the user explicitly asked for diagnosis - one detail fetch on the first result of each healthy portal (description must be readable decoded text; a failure downgrades to degraded). Report all statuses in this mode, including healthy. Volume stays bounded: one search, at most one retry, at most one detail per portal.

Step 5: Present Results

Present new jobs in a table sorted by fit (high first). When Step 1b skipped portals (enabled: false), report them with the skipped (disabled): line below so opting one out stays visible rather than silent; omit the line when nothing was skipped. When Step 4.75 found a portal degraded, broken, or inconclusive, add one health: line per suspect portal (healthy portals get no line); after the report, offer to set that portal's enabled: false so /scrape stops running it (and covers it via the Step 1c fallback) until it is fixed - only edit the toggle with the user's confirmation, and never edit anything else in the skill.

## New Job Matches - YYYY-MM-DD

Found X new positions (Y high, Z medium, W low match).

skipped (disabled): <portal-name>, <portal-name>

health: <portal-name> - degraded (company null on all 12 results); parsing anchors in .agents/skills/<portal-name>/url-reference.md
health: <portal-name> - broken (0 results for the SKILL.md test query and a broader retry); parsing anchors in .agents/skills/<portal-name>/url-reference.md

| # | Fit | Title | Company | Location | Deadline | URL |
|---|-----|-------|---------|----------|----------|-----|
| 1 | High | ... | ... | ... | ... | [Link](...) |

If Step 2.5 flagged a mass-posting pattern, note it in the Title cell (e.g. "Frontend Developer (posted in 6 cities)") rather than burying it. Do the same for a declared-language-insufficient-level flag from the Language Gate (e.g. "Backend Engineer ⚠ fluent English required") - both are signals the user should see at a glance, not just in the detail highlights below.

### High-Match Highlights
For each high-match job, add 2-3 bullet points:
- Why it matches your profile
- Key requirements to check
- Any red flags (including mass-posting signals from Step 2.5)

### Contacts
For each high/medium-fit job from Step 4.5, add a short contacts block with the two
LinkedIn search links:
- Recruiters/TA search link, for the referral path
- Role/team-peer search link, for the warm-intro / informational-outreach path

After presenting, ask:

"Want me to evaluate any of these in detail? Just give me the number(s)."

If the user picks a number, invoke the job-application-assistant skill workflow (fit evaluation first, then CV + cover letter if approved).

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.)

Step 6: Update Tracker (Optional)

If the user decides to apply to any job, add a row to job_search_tracker.csv.

Important Rules

1. Never fabricate job postings. Only present jobs from actual CLI search/detail output or WebSearch/WebFetch results.
2. Respect deduplication. Always check seen_jobs.json AND job_search_tracker.csv before presenting.
3. Focus on configured geographic area. Skip jobs that require relocation or are clearly outside commute range.
4. Only open positions. Skip postings with expired deadlines or those marked as closed.
5. Be efficient with detail fetches. Don't run detail or WebFetch on every search hit — pre-filter by title/snippet, then fetch only promising matches.
6. Parallel searches. Run portal CLI searches in parallel; use WebSearch only for gaps the CLIs don't cover.
7. No automated people lookups. Referral contacts (Step 4.5) are LinkedIn search links only - never fetch or scrape LinkedIn people-search result pages programmatically.
8. Health checks are bounded and honest. Step 4.75 spends at most one probe, one retry, and (in health mode) one detail fetch per portal - a diagnosis, not a crawl. A rate-limit is never evidence of breakage. Health verdicts come only from observed CLI output; a portal that could not be tested is reported as inconclusive, never guessed. The enabled toggle is the only thing the health check may edit, and only with confirmation.
9. Flag distribution patterns, never accuse. The mass-posting signal (Step 2.5) describes how a listing is being distributed, not a claim that the employer is a scam. Never name a company as fraudulent or untrustworthy - present the observation and let the user decide.