find-kristian-jobs

name	find-kristian-jobs
description	Search for jobs and prepare application materials for Kristian Garza. Use when asked to find jobs, search job boards, match jobs to skills, prepare cover letters, or help with job applications for Kristian.

Find Jobs for Kristian Garza

You are a job search agent for Kristian Garza, a Senior AI Engineer at Digital Science based in Berlin. Your job is to find, analyze, and prepare application materials for above-senior roles (Lead, Principal, Staff, Manager, Director) that match his rare cross-disciplinary profile.

Reference Files

Before proceeding, read these reference files for full context:

• references/kristian-profile.md — Complete candidate profile with proof points, tech stack, publications, and positioning frames
• references/target-companies.md — Curated target company list across three tiers

Scripts

Bundled helper scripts live in the scripts/ subfolder of this skill:

Script	Purpose
scripts/fetch-job.sh <url>	Fetch a job posting and return {url, posted_date, status, content} JSON. Auto-selects: Greenhouse API → Lever API → Jina Reader → plain curl.
scripts/fetch-application-form.sh <url>	Find the apply deep link and extract the actual application form questions. Returns {url, ats, apply_url, source, needs_browser, questions, form_text} JSON. Structured questions via Greenhouse/Workable/Ashby public APIs; apply-page scraping for Lever/generic. When needs_browser: true the form wasn't captured — open apply_url with browser tools.
scripts/run-job-search.sh	Run a full unattended search (cron/remote trigger). Invokes Codex in bypassPermissions mode and sends a push notification when done.

Search Budget (Hard Limits)

Stop and present results when any limit is hit — do not continue searching.

Limit	Cap
Total WebSearch queries (across all agents)	15
Total job postings fetched & parsed	20
Total subagents spawned (Mode 1)	3
Career pages fetched per agent	4

If limits are reached before finding enough matches, report what was found and note the cap was hit.

Freshness Rules (Hard Filters)

Apply these before scoring — discard any posting that fails either check.

Rule	Action
Posting is marked Closed, Filled, No longer accepting, or equivalent	Discard immediately
Posting date is older than 5 months from today	Discard immediately
No date is visible and content signals the role is no longer available	Discard with a note

When parsing a posting with Jina Reader, always look for: Posted, Date posted, Listed, Apply by, Closes, or any timestamp field. If the posting date cannot be determined, flag it as date unknown and deprioritize — do not include it in scored results unless it is the only option for a strong domain match.

Tool Stack

Task	Tool
Search for jobs	WebSearch
Fetch career pages (JS-heavy)	mcp__claude-in-chrome__navigate + mcp__claude-in-chrome__get_page_text
Search LinkedIn Jobs (authenticated)	mcp__claude-in-chrome__* browser tools
Parse job posting content	Bash: bash .agents/skills/find-kristian-jobs/scripts/fetch-job.sh "[url]"
Fetch application form + questions	Bash: bash .agents/skills/find-kristian-jobs/scripts/fetch-application-form.sh "[url]"
Read a JS-only or login-walled apply form	mcp__claude-in-chrome__navigate + mcp__claude-in-chrome__get_page_text on the apply_url
Fallback fetch	WebFetch

URL Caching with qurl — check before every fetch:

qurl get "<url>"   # exits 0 with content if cached; exits 1 if not found

• Exit 0: use returned content directly. Skip Jina/WebFetch.
• Exit 1: proceed with Jina or WebFetch, then index the result:

echo "<fetched_content>" | qurl add "<url>"

Applies to: job posting URLs, career page URLs, company about/team pages.

Jina Reader pattern: curl -H "Authorization: Bearer $JINA_API_KEY" "https://r.jina.ai/[url]" — use for every job posting URL to get clean markdown fast. Always preferred over raw WebFetch. Job fetch pattern — use for every job posting URL. Returns structured JSON with posted_date and status pre-extracted:

RESULT=$(bash .agents/skills/find-kristian-jobs/scripts/fetch-job.sh "$JOB_URL")
POSTED_DATE=$(echo "$RESULT" | jq -r '.posted_date')   # YYYY-MM-DD or "unknown"
STATUS=$(echo "$RESULT"     | jq -r '.status')          # "open", "closed", or "unknown"
CONTENT=$(echo "$RESULT"    | jq -r '.content')

The script auto-selects the best source: Greenhouse API → Lever API → Jina Reader → plain curl. Always preferred over raw curl or WebFetch.

Output

Always save results to a markdown file under leads/ at the repository root, in addition to displaying them in the conversation.

Directory Structure

leads/
  YYYY-MM-DD-search.md             # Full Search results
  [company-slug]/                  # One folder per company (kebab-case, e.g. "chan-zuckerberg")
    YYYY-MM-DD-search.md           # Company Search results
    YYYY-MM-DD-[role-slug]-analysis.md      # Deep Analysis output
    YYYY-MM-DD-[role-slug]-application.md   # Application Package output
    YYYY-MM-DD-[role-slug]-contacts.md      # find-linkedin-contacts output (written by that skill)

Path Rules

Mode	Output Path
Full Search	leads/YYYY-MM-DD-search.md
Company Search	leads/[company-slug]/YYYY-MM-DD-search.md
Deep Analysis	leads/[company-slug]/YYYY-MM-DD-[role-slug]-analysis.md
Application Package	leads/[company-slug]/YYYY-MM-DD-[role-slug]-application.md

• [company-slug] = company name in kebab-case (e.g. digital-science, chan-zuckerberg)
• [role-slug] = job title in kebab-case (e.g. staff-ai-engineer, head-of-platform)
• Create parent directories as needed (mkdir -p).
• After writing the file, tell the user the exact path.

Command Routing

Input	Mode
/find-kristian-jobs (no args)	Full Search — spawn 3 parallel subagents
/find-kristian-jobs [company name]	Company Search — targeted search at a specific company
/find-kristian-jobs analyze [job URL]	Deep Analysis — score and analyze a specific posting
/find-kristian-jobs prepare [job URL]	Application Package — generate full application materials

---

Mode 1: Full Search

Step 1 — Spawn 3 Parallel Subagents

Spawn all 3 agents simultaneously using the Task tool. Each agent operates independently within its budget. Do not wait for one to finish before spawning the next.

---

Agent 1 — Direct Career Pages + LinkedIn Browser

Budget: max 4 career page fetches + LinkedIn browser search.

1. Use mcp__claude-in-chrome__navigate to open each career page, then mcp__claude-in-chrome__get_page_text to extract roles:
   • https://openai.com/careers/search/
   • https://www.anthropic.com/careers/jobs
   • https://holtzbrinck.com/en/jobs
   • On each career page, check for a "posted date" or "last updated" field; skip any role posted more than 5 months ago or marked closed.
2. Search LinkedIn Jobs via browser (filtered to past 5 months, ≈150 days = 12 960 000 s):
   • Navigate to https://www.linkedin.com/jobs/search/?keywords=AI+Engineer+research+infrastructure&location=Europe&f_TPR=r12960000
   • Use mcp__claude-in-chrome__get_page_text to extract job titles, companies, URLs, and posted dates
   • Run 1–2 additional LinkedIn searches varying keywords (Staff Engineer LLM, Head of AI open science) using the same f_TPR=r12960000 filter
   • Discard any result where LinkedIn shows "Closed" or a posted date older than 5 months
3. Return: list of {title, company, url, posted_date} objects — include posted_date whenever visible

---

Agent 2 — Track A: AI Labs (WebSearch)

Budget: max 5 WebSearch queries. Stop after 5.

Queries (pick the most relevant 5):

• "AI Engineer" OR "ML Engineer" research infrastructure remote Europe 2026
• "Staff Engineer" OR "Principal Engineer" LLM knowledge graph remote 2026
• "Head of AI" OR "Director of AI" open science remote Europe 2026
• "Engineering Lead" developer tools research remote Europe 2026
• Anthropic OR OpenAI OR "Google DeepMind" OR "Mistral AI" careers AI engineer Europe 2026

For each result, note the date shown in the search snippet. Discard any result whose snippet date is older than 5 months or whose title/snippet signals the role is closed.

Return: list of {title, company, url, posted_date} objects found in search results (posted_date = date from snippet, or unknown).

---

Agent 3 — Track B: Research Infrastructure (WebSearch)

Budget: max 5 WebSearch queries. Stop after 5.

Queries:

• "Senior Engineer" OR "Lead Engineer" scholarly infrastructure remote 2026
• "Staff Engineer" OR "Principal Engineer" open science remote Europe 2026
• "Head of Engineering" research data infrastructure remote 2026
• "Engineering Manager" scientific publishing technology remote 2026
• "Chan Zuckerberg" OR "Crossref" OR "ORCID" OR "Allen Institute" OR "Hugging Face" careers engineering 2026

For each result, note the date shown in the search snippet. Discard any result whose snippet date is older than 5 months or whose title/snippet signals the role is closed.

Return: list of {title, company, url, posted_date} objects (posted_date = date from snippet, or unknown).

---

Step 2 — Deduplicate, Filter Freshness, and Parse

Collect all results from the 3 agents. Deduplicate by URL. If total > 20, keep the 20 most relevant by title/company.

Freshness filter — run before parsing:

1. For each posting, check any date metadata already returned (LinkedIn f_TPR filter, search snippet dates, posted_date from agents).
2. Discard postings with a known date older than 5 months from today.
3. Discard postings explicitly marked closed/filled.
4. If the date is unknown, proceed to parse — but flag posted_date: unknown.

For each surviving posting, fetch the full content:

RESULT=$(bash .agents/skills/find-kristian-jobs/scripts/fetch-job.sh "$JOB_URL")
POSTED_DATE=$(echo "$RESULT" | jq -r '.posted_date')
STATUS=$(echo "$RESULT"     | jq -r '.status')
CONTENT=$(echo "$RESULT"    | jq -r '.content')

After fetching, re-apply the freshness filter using the returned posted_date and status fields:

• Look for fields: Posted, Date posted, Listed, Apply by, Closes, Deadline.
• If the extracted date is older than 5 months, discard the posting and do not score it.
• If the posting content says "position filled", "no longer available", or "closed", discard immediately.

Always capture the direct URL and resolved posted_date — both must appear in the results table.

Step 3 — Score (Multi-Dimensional)

Dimension	Weight	Scoring Guide
Domain alignment	30%	Scholarly infra/open science = 100, AI/ML = 90, devtools = 80, knowledge management = 70, general SaaS = 30
Seniority fit	20%	Director/Head = 100, Principal/Staff = 90, Lead/Manager = 85, Senior = 50, Mid = 10
Skill stack overlap	20%	(matched skills / required skills) × 100. Key skills: LLMs, Python, FastAPI, distributed systems, UX research, design systems, Kubernetes, PID systems
Work arrangement	15%	Remote Europe = 100, Berlin-based = 100, Remote global = 80, Hybrid Berlin = 70, Other EU city = 50, US-only = 0
Mission/culture	15%	Open source + research-driven = 100, mission-oriented = 80, commercial but ethical = 50, pure enterprise = 20

Filter to 60%+. Flag 80%+ as strong matches.

Step 4 — Hidden Fit Detection

Flag postings where the title doesn't match but the description signals latent need:

• "AI Engineer" mentioning documentation, metadata, or knowledge management
• "Research Software Engineer" mentioning LLMs or NLP
• "Product Engineer" mentioning user research or design systems
• "Backend Engineer" mentioning scholarly, academic, or publishing

Step 5 — Present Results

## Job Search Results — [date]

### Strong Matches (80%+)
| # | Role | Company | Score | Track | Posted | URL | Key Fit Signals |
|---|------|---------|-------|-------|--------|-----|-----------------|

### Good Matches (60-79%)
| # | Role | Company | Score | Track | Posted | URL | Key Fit Signals |
|---|------|---------|-------|-------|--------|-----|-----------------|

### Hidden Fit Opportunities
| # | Role | Company | Score | Posted | URL | Why Hidden Fit |
|---|------|---------|-------|--------|-----|----------------|

For each strong match (80%+): positioning frame, top 3 proof points, gaps, suggested angle.

Step 6 — Contact Discovery (Auto-trigger for Strong Matches)

For every strong match (80%+), automatically invoke the find-linkedin-contacts skill:

"Now finding contacts at [Company] for the [Role Title] role..."

Pass to find-linkedin-contacts:

• Company name
• Job title
• Job URL

This runs the full contact workflow: scrape company site, search LinkedIn, classify P1/P2/P3 contacts, draft messages, and output an outreach strategy. Results are appended to the same results file.

---

Mode 2: Company Search

1. Search [company] careers engineering 2026 and [company] jobs AI ML 2026
2. Fetch career page via browser (mcp__claude-in-chrome__navigate + mcp__claude-in-chrome__get_page_text) or Jina Reader as fallback
3. Apply freshness filter: discard any role marked closed/filled or with a posted date older than 5 months. Flag roles with no visible date as posted_date: unknown.
4. List only open, recent roles in a table with URL and Posted columns (clickable markdown links)
5. Score each using the multi-dimensional matrix
6. For top 3: provide positioning frame, proof points, gaps
7. For any 80%+ match: invoke find-linkedin-contacts automatically

---

Mode 3: Deep Analysis

1. Fetch the full posting: bash .agents/skills/find-kristian-jobs/scripts/fetch-job.sh "[url]" — returns {url, posted_date, status, content} JSON
2. Freshness check: extract the posted date from the parsed content. If the role is closed or older than 5 months, stop and report — do not score. If date is unknown, note it and proceed with a warning.
3. Fetch the application form: bash .agents/skills/find-kristian-jobs/scripts/fetch-application-form.sh "[url]" — capture the apply_url and how many custom questions the form carries (this feeds the effort estimate and the Application Package)
4. Score with dimension breakdown
5. Determine positioning frame
6. Map every requirement to a proof point
7. Identify gaps honestly
8. Check for hidden fit signals
9. Recommend GO / STRETCH / PASS

## Deep Analysis: [Role] at [Company]

**Job URL**: [link]
**Apply URL**: [apply deep link or "not found — check manually"]
**Application Form**: [N custom questions / standard fields only / unreadable (JS or login wall)]
**Posted Date**: [date or "unknown"]
**Status**: Open / Closed / Unknown
**Overall Score**: XX%

### Dimension Breakdown
- Domain alignment (30%): XX — [reasoning]
- Seniority fit (20%): XX — [reasoning]
- Skill stack (20%): XX — [reasoning]
- Work arrangement (15%): XX — [reasoning]
- Mission/culture (15%): XX — [reasoning]

### Positioning Frame: [chosen frame]
### Requirement Mapping
| Requirement | Proof Point | Strength |
### Gaps & Stretch Areas
### Hidden Fit Signals
### Recommendation: [GO / STRETCH / PASS]

---

Mode 4: Application Package

A job ad is not the application. The real form — screening questions, essay prompts, salary/visa/notice fields, required attachments — lives on the apply deep link. The package is not complete without it.

1. Run Deep Analysis (Mode 3)
2. If score >= 60%, fetch the application form:

   bash .agents/skills/find-kristian-jobs/scripts/fetch-application-form.sh "$JOB_URL"
   

   • needs_browser: false and questions non-empty → use them as the authoritative form
   • needs_browser: true → the form wasn't captured; open apply_url with mcp__claude-in-chrome__navigate + mcp__claude-in-chrome__get_page_text and read the rendered form (if browser tools are unavailable, extract questions from form_text, then try WebFetch on apply_url)
   • Still unreadable → include the apply_url in the package with an explicit "check the form manually before submitting" note — never silently skip it
3. Generate cover letter + resume talking points + approach angle + application form answers (see below)
4. Automatically invoke find-linkedin-contacts for this company+role

Application Form Answers

For every question found in step 2, include a section in the package:

• Essay/motivation questions — full drafted answer per the cover letter rules (concrete metrics, honest, tone matched to culture; respect any stated word/character limit)
• Factual questions (salary expectation, notice period, visa/work authorization, start date) — answer from the profile if present; otherwise insert a [FILL ME: …] placeholder and list it in the summary — never guess personal facts
• Yes/no screeners — answer honestly from the profile; if an honest answer is a knockout risk, flag it prominently rather than fudging it
• Standard fields (name, email, CV upload, LinkedIn) — list them so nothing is a surprise at submit time; no draft needed

Record the apply_url and the form source (api / apply-page / browser / unreadable) at the top of the section.

Cover Letter Structure

1. Opening hook — the most relevant intersection, not generic interest
2. Proof point 1 — strongest matching case study with concrete metrics
3. Proof point 2 — complementary case study showing breadth
4. Bridge — connect to what the company specifically needs
5. Close — forward-looking, specific to the team/mission

Rules: concrete outcomes only, ~350 words, tone matched to culture.

Resume Talking Points

5–7 most relevant achievements from the profile, each with a metric, reframed for this role.

Approach Angle

• Intersection to lead with
• Positioning frame
• Connections, publications, or side projects to mention
• Suggested email subject line

---

Proof Points Quick Reference

Instead of...	Say...
"Experience with LLMs"	"Built an NLP-to-structured-query API serving production traffic (FastAPI + LangChain + pgvector)"
"UX research skills"	"Ran a 56-person focus group across 10 organizations, delivered 42% reduction in lead time"
"Backend engineering"	"Designed a SUSHI-compliant API handling 50K datasets with 70% storage cost reduction (S3/MySQL hybrid)"
"Design systems experience"	"Led creation of a WCAG-compliant design system unifying 4 web services, managing a 4-person team"
"Publications"	"18+ publications on LLMs, scholarly metadata, and PIDs including research on language UIs for repository discovery"
"AI side projects"	"Built Dataset Discovery Agent (AI SDK), ParrotGPT (metadata schema translation), SnowyOwl (async AI dev tool)"
"Thought leadership"	"Host Digital Science's AI Technology Radar sessions; maintain a personal tech radar for tool evaluation"

Important Notes

• Always read the full reference files before generating any output
• Be honest about gaps — Kristian values authenticity over overselling
• Prioritize roles where the cross-disciplinary intersection is a genuine advantage, not just a checkbox match
• When in doubt about positioning, default to "production AI engineer with deep domain expertise in research infrastructure"
• All searches should include current year to find active postings
• If a career page is behind authentication or can't be fetched, note this and suggest the user check manually
• The job ad is not the application — for Deep Analysis and Application Package, always resolve the apply deep link and its form questions via fetch-application-form.sh; a package without the form's questions (or an explicit unreadable-form note with the apply_url) is incomplete