Career-Ops Job Search Pipeline

Skill by ara.so — Daily 2026 Skills collection.

Career-Ops turns Claude Code into a full job search command center. It evaluates offers with A-F scoring, generates ATS-optimized PDFs, scans 45+ company portals, and tracks everything in a single source of truth — all powered by Claude AI agents.

Installation

bash

# 1. Clone the repo
git clone https://github.com/santifer/career-ops.git
cd career-ops

# 2. Install Node dependencies (for PDF generation via Playwright)
npm install
npx playwright install chromium

# 3. Configure your profile
cp config/profile.example.yml config/profile.yml
# Edit config/profile.yml with your name, target roles, location, comp range, etc.

# 4. Configure portal scanner
cp templates/portals.example.yml portals.yml
# Add/remove companies you want to track

# 5. Add your CV in Markdown
# Create cv.md in project root — this is what the AI reads to evaluate fit
cat > cv.md << 'EOF'
# Your Name

## Experience
...your CV content in markdown...
EOF

# 6. Build the Go dashboard (optional but recommended)
cd dashboard
go build -o career-dashboard .
cd ..

Prerequisites

Node.js 18+ (for Playwright/PDF)
Go 1.21+ (for dashboard TUI)
Claude Code (
```
claude
```
CLI) with an active Anthropic API key

bash

# Verify Claude Code is installed
claude --version

# Open career-ops in Claude Code
claude   # run from the career-ops directory

Core Commands

All commands run inside Claude Code as slash commands. Paste into the Claude Code session:

/career-ops                     → Show all available modes
/career-ops {job URL or JD}     → Full auto-pipeline: evaluate + PDF + tracker entry
/career-ops scan                → Scan pre-configured portals for new offers
/career-ops pdf                 → Generate ATS-optimized CV for last evaluated offer
/career-ops batch               → Batch evaluate multiple offers in parallel
/career-ops tracker             → View application pipeline status
/career-ops apply               → AI-assisted application form filling
/career-ops pipeline            → Process all pending URLs in queue
/career-ops contacto            → Generate LinkedIn outreach message
/career-ops deep                → Deep company research report
/career-ops training            → Evaluate a course or certification
/career-ops project             → Evaluate a portfolio project fit

Auto-detection shortcut

Just paste a raw job URL or job description text — career-ops detects it and runs the full pipeline automatically:

https://boards.greenhouse.io/anthropic/jobs/12345

# Or paste the full JD text — Claude auto-routes it

Configuration Files

config/profile.yml

This is your candidate profile. Claude reads this for every evaluation.

yaml

# config/profile.yml
name: "Your Name"
title: "Head of Applied AI"
location: "Madrid, Spain"
timezone: "CET"
remote_preference: "remote-first"

target_roles:
  - "Head of AI"
  - "AI Engineer"
  - "LLMOps Engineer"
  - "Solutions Architect (AI)"

compensation:
  currency: "EUR"
  minimum: 120000
  target: 150000
  equity: true

languages:
  - "English (C2)"
  - "Spanish (Native)"

archetypes:
  - "LLMOps"
  - "Agentic"
  - "PM-AI"
  - "Solutions Architect"

portals.yml

Configure which company job boards to scan:

yaml

# portals.yml (copied from templates/portals.example.yml)
companies:
  - name: "Anthropic"
    url: "https://www.anthropic.com/careers"
    board: "greenhouse"
    
  - name: "ElevenLabs"
    url: "https://elevenlabs.io/careers"
    board: "ashby"
    
  - name: "n8n"
    url: "https://n8n.io/careers"
    board: "custom"

job_boards:
  ashby:
    base_url: "https://jobs.ashbyhq.com"
  greenhouse:
    base_url: "https://boards.greenhouse.io"
  lever:
    base_url: "https://jobs.lever.co"

search_queries:
  - "AI engineer remote"
  - "LLMOps"
  - "Head of AI Europe"

templates/states.yml

Canonical pipeline statuses (edit to match your workflow):

yaml

# templates/states.yml
statuses:
  - id: "pending"
    label: "Pending Review"
  - id: "evaluating"
    label: "Under Evaluation"
  - id: "applied"
    label: "Applied"
  - id: "screening"
    label: "HR Screening"
  - id: "interview"
    label: "Interviewing"
  - id: "offer"
    label: "Offer Received"
  - id: "rejected"
    label: "Rejected"
  - id: "withdrawn"
    label: "Withdrawn"

Modes Directory

Each file in

modes/

is a Claude skill that defines behavior for one command:

modes/
├── _shared.md      # Shared context injected into every mode — customize this first
├── oferta.md       # /career-ops {JD} — full evaluation pipeline
├── pdf.md          # /career-ops pdf — PDF CV generation
├── scan.md         # /career-ops scan — portal scanner
├── batch.md        # /career-ops batch — parallel evaluation
├── tracker.md      # /career-ops tracker — pipeline viewer
├── apply.md        # /career-ops apply — form filling
├── pipeline.md     # /career-ops pipeline — process queue
├── contacto.md     # /career-ops contacto — LinkedIn outreach
├── deep.md         # /career-ops deep — company research
├── training.md     # /career-ops training — cert evaluation
└── project.md      # /career-ops project — portfolio project fit

Customizing modes via Claude

Ask Claude to modify the system from within Claude Code:

# In your Claude Code session:
"Change the archetypes in _shared.md to focus on backend engineering roles"
"Translate all modes to English"
"Add Mistral and Cohere to portals.yml"
"Update the scoring weights in oferta.md to weight compensation at 20%"
"Add a new mode called 'referral' for tracking employee referrals"

Go Dashboard TUI

The terminal dashboard provides a visual pipeline browser with filtering and sorting.

Building and running

bash

cd dashboard
go build -o career-dashboard .
./career-dashboard

Dashboard features

6 filter tabs: All, Pending, Applied, Interviewing, Offer, Rejected
4 sort modes: Date, Score, Company, Status
Grouped/flat view: Toggle between company groups and flat list
Lazy-loaded previews: Press Enter to read the full evaluation report
Inline status changes: Update status without leaving the TUI

Go module structure

// dashboard/main.go — entry point
package main

import (
    tea "github.com/charmbracelet/bubbletea"
    "github.com/charmbracelet/lipgloss"
)

func main() {
    p := tea.NewProgram(initialModel(), tea.WithAltScreen())
    if _, err := p.Run(); err != nil {
        log.Fatal(err)
    }
}

// dashboard/model.go — core data model
package main

import "time"

type Application struct {
    ID          string    `json:"id"`
    Company     string    `json:"company"`
    Role        string    `json:"role"`
    Score       string    `json:"score"` // A, B+, B, C, D, F
    Status      string    `json:"status"`
    URL         string    `json:"url"`
    ReportPath  string    `json:"report_path"`
    PDFPath     string    `json:"pdf_path"`
    CreatedAt   time.Time `json:"created_at"`
    UpdatedAt   time.Time `json:"updated_at"`
    Archetype   string    `json:"archetype"` // LLMOps, Agentic, PM, SA...
    CompRange   string    `json:"comp_range"`
    Notes       string    `json:"notes"`
}

type Model struct {
    applications []Application
    filtered     []Application
    cursor       int
    activeTab    int
    sortMode     int
    grouped      bool
    preview      string
    showPreview  bool
    width        int
    height       int
}

Reading pipeline data from TSV

// dashboard/data.go
package main

import (
    "encoding/csv"
    "os"
    "path/filepath"
)

func loadApplications(dataDir string) ([]Application, error) {
    tsvPath := filepath.Join(dataDir, "pipeline.tsv")
    f, err := os.Open(tsvPath)
    if err != nil {
        return nil, err
    }
    defer f.Close()

    r := csv.NewReader(f)
    r.Comma = '\t'
    r.LazyQuotes = true

    records, err := r.ReadAll()
    if err != nil {
        return nil, err
    }

    var apps []Application
    for _, record := range records[1:] { // skip header
        if len(record) < 8 {
            continue
        }
        apps = append(apps, Application{
            ID:      record[0],
            Company: record[1],
            Role:    record[2],
            Score:   record[3],
            Status:  record[4],
            URL:     record[5],
        })
    }
    return apps, nil
}

Batch Processing

Batch mode evaluates multiple offers in parallel using

claude -p

sub-agents.

Setup batch queue

bash

# Create a batch input file — one URL per line
cat > batch/queue.txt << 'EOF'
https://boards.greenhouse.io/company/jobs/123
https://jobs.lever.co/company/456
https://jobs.ashbyhq.com/company/789
EOF

Run batch evaluation

bash

# From Claude Code session:
/career-ops batch

# Or directly from terminal using the runner script:
cd batch
./batch-runner.sh queue.txt

batch/batch-runner.sh

bash

#!/usr/bin/env bash
# batch-runner.sh — orchestrates parallel claude -p workers

QUEUE_FILE="${1:-queue.txt}"
MAX_PARALLEL=4
PROMPT_FILE="batch-prompt.md"

while IFS= read -r url; do
    [[ -z "$url" || "$url" == \#* ]] && continue
    
    # Launch sub-agent for each URL
    claude -p "$(cat $PROMPT_FILE)\n\nEvaluate this offer: $url" \
        --output-format json \
        >> ../data/batch-results.jsonl &
    
    # Throttle parallelism
    while [[ $(jobs -r | wc -l) -ge $MAX_PARALLEL ]]; do
        sleep 2
    done
done < "$QUEUE_FILE"

wait
echo "Batch complete. Results in data/batch-results.jsonl"

PDF Generation

PDFs are generated via Playwright rendering an HTML template with injected keywords.

Triggering PDF generation

# In Claude Code — after an evaluation:
/career-ops pdf

# Claude will:
# 1. Read the last evaluation report
# 2. Extract keywords from the job description
# 3. Inject them into templates/cv-template.html
# 4. Render with Playwright to output/{company}-{role}.pdf

Manual Playwright PDF render (Node.js)

javascript

// scripts/generate-pdf.js
const { chromium } = require('playwright');
const fs = require('fs');
const path = require('path');

async function generatePDF(htmlContent, outputPath) {
  const browser = await chromium.launch();
  const page = await browser.newPage();
  
  await page.setContent(htmlContent, { waitUntil: 'networkidle' });
  
  await page.pdf({
    path: outputPath,
    format: 'A4',
    margin: { top: '20mm', bottom: '20mm', left: '15mm', right: '15mm' },
    printBackground: true,
  });
  
  await browser.close();
  console.log(`PDF generated: ${outputPath}`);
}

// Usage
const template = fs.readFileSync('templates/cv-template.html', 'utf8');
const company = process.argv[2] || 'company';
const role = process.argv[3] || 'role';
const outputPath = path.join('output', `${company}-${role}.pdf`);

generatePDF(template, outputPath);

Pipeline Data Structure

Career-ops stores data in

data/

(gitignored):

data/
├── pipeline.tsv          # Main tracker — all applications
├── batch-results.jsonl   # Batch evaluation outputs
└── urls-pending.txt      # Queue for /career-ops pipeline

reports/
└── {company}-{role}-{date}.md   # Full evaluation reports

output/
└── {company}-{role}.pdf         # Generated CVs

Pipeline TSV format

tsv

id	company	role	score	status	url	archetype	comp_range	created_at	updated_at	report_path	pdf_path
abc123	Anthropic	AI Engineer	A	applied	https://...	LLMOps	$150k-$200k	2026-04-05	2026-04-05	reports/anthropic-ai-engineer.md	output/anthropic-ai-engineer.pdf

Evaluation Scoring System

Career-ops scores offers on 10 weighted dimensions producing an A-F grade:

Dimension	Weight	What it measures
Role fit	20%	Match between JD requirements and your CV
Level alignment	15%	Seniority match
Compensation	15%	Comp vs your target range
Tech stack	15%	Stack overlap with your skills
Company stage	10%	Startup/scale-up/enterprise fit
Remote policy	10%	Location/remote match
Growth potential	5%	Career trajectory opportunity
Mission alignment	5%	Personal interest in the domain
Interview signals	3%	Glassdoor/process quality signals
Recruiter quality	2%	JD quality, clarity, red flags

Grade thresholds: A ≥ 85, B+ ≥ 75, B ≥ 65, C ≥ 50, D ≥ 35, F < 35

Common Patterns

Evaluate a single offer end-to-end

# In Claude Code session (claude command in project root):
/career-ops https://boards.greenhouse.io/anthropic/jobs/4567890

# Claude will:
# 1. Scrape the job description
# 2. Detect archetype (LLMOps, Agentic, PM-AI, etc.)
# 3. Score against your cv.md and profile.yml
# 4. Generate 6-block evaluation report → reports/
# 5. Create ATS-optimized PDF → output/
# 6. Add entry to data/pipeline.tsv

Add a company to the scanner

yaml

# In portals.yml, add under companies:
  - name: "Langfuse"
    url: "https://langfuse.com/careers"
    board: "ashby"
    filter_keywords:
      - "AI"
      - "engineer"
      - "remote"

# Then run:
/career-ops scan

Build interview story bank

The STAR+R system accumulates stories across evaluations:

# After several evaluations, run:
/career-ops tracker

# Claude surfaces your strongest STAR stories and maps them
# to common behavioral questions. Stories accumulate in:
# reports/_story-bank.md

Salary negotiation script generation

# After receiving an offer:
/career-ops {paste the offer details}

# Claude generates:
# - Counter-offer script with specific numbers
# - Geographic discount pushback if applicable  
# - Competing offer leverage language
# - Email templates for each scenario

Troubleshooting

Playwright/PDF issues

bash

# Chromium not found
npx playwright install chromium

# PDF generation fails silently
node scripts/generate-pdf.js 2>&1 | head -50

# Font not loading in PDF (Space Grotesk / DM Sans)
# Ensure fonts/ directory has the .woff2 files
ls fonts/
# SpaceGrotesk-*.woff2  DMSans-*.woff2

Go dashboard won't build

bash

cd dashboard
go mod tidy
go build -o career-dashboard .

# Missing Bubble Tea dependency
go get github.com/charmbracelet/bubbletea
go get github.com/charmbracelet/lipgloss
go get github.com/charmbracelet/bubbles

TSV parsing errors

bash

# Check pipeline.tsv for malformed rows
awk -F'\t' 'NF != 12 {print NR": "NF" fields: "$0}' data/pipeline.tsv

# Re-run integrity check via Claude:
# "Run pipeline integrity check and fix any malformed rows in pipeline.tsv"

Claude Code not finding modes

bash

# Verify CLAUDE.md is in project root
ls CLAUDE.md  # Must exist

# Verify modes directory
ls modes/     # Should show *.md files

# If Claude doesn't recognize /career-ops, re-open from project root:
cd /path/to/career-ops
claude

Scanner blocked by bot detection

yaml

# In portals.yml, add delays for rate-limited sites:
  - name: "CompanyName"
    url: "https://company.com/careers"
    board: "greenhouse"
    scrape_delay_ms: 3000
    user_agent: "Mozilla/5.0 (compatible)"

Project Structure Reference

career-ops/
├── CLAUDE.md                    # Agent instructions (read by Claude Code)
├── cv.md                        # YOUR CV in markdown — create this
├── article-digest.md            # Your proof points / portfolio (optional)
├── config/
│   └── profile.example.yml      # Copy to profile.yml and fill out
├── modes/                       # 14 Claude skill definitions
│   ├── _shared.md               # Shared context — customize first
│   └── *.md                     # One file per /career-ops command
├── templates/
│   ├── cv-template.html         # ATS CV template (Space Grotesk + DM Sans)
│   ├── portals.example.yml      # Copy to portals.yml
│   └── states.yml               # Pipeline status definitions
├── batch/
│   ├── batch-prompt.md          # Self-contained worker prompt for sub-agents
│   └── batch-runner.sh          # Parallel orchestrator
├── dashboard/                   # Go TUI (Bubble Tea + Lipgloss)
│   ├── main.go
│   ├── model.go
│   ├── data.go
│   └── go.mod
├── fonts/                       # Space Grotesk + DM Sans woff2 files
├── data/                        # Runtime data — gitignored
├── reports/                     # Evaluation reports — gitignored
├── output/                      # Generated PDFs — gitignored
├── docs/
│   ├── SETUP.md
│   ├── CUSTOMIZATION.md
│   └── ARCHITECTURE.md
└── examples/                    # Sample CV, report, proof points

Key Design Principles

Quality over quantity — the scoring system is designed to filter out weak fits, not to maximize application volume
Claude customizes Claude — ask Claude to edit the modes, weights, and archetypes; it knows the file structure
Single source of truth —
```
data/pipeline.tsv
```
is the canonical record; all commands read/write it consistently
Gitignore your data —
```
data/
```
,
```
reports/
```
,
```
output/
```
, and
```
cv.md
```
are gitignored by default; your personal info stays local

career-ops-job-search

NPX Install

Tags

SKILL.md Content

Career-Ops Job Search Pipeline

Installation

Prerequisites

Core Commands

Auto-detection shortcut

Configuration Files

config/profile.yml

portals.yml

templates/states.yml

Modes Directory

Customizing modes via Claude

Go Dashboard TUI

Building and running

Dashboard features

Go module structure

Reading pipeline data from TSV

Batch Processing

Setup batch queue

Run batch evaluation

batch/batch-runner.sh

PDF Generation

Triggering PDF generation

Manual Playwright PDF render (Node.js)

Pipeline Data Structure

Pipeline TSV format

Evaluation Scoring System

Common Patterns

Evaluate a single offer end-to-end

Add a company to the scanner

Build interview story bank

Salary negotiation script generation

Troubleshooting

Playwright/PDF issues

Go dashboard won't build

TSV parsing errors

Claude Code not finding modes

Scanner blocked by bot detection

Project Structure Reference

Key Design Principles

`config/profile.yml`

`portals.yml`

`templates/states.yml`

`batch/batch-runner.sh`