Career Ops Job Search

作者 reason-machines2384a003145a無授權條款83 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫3 個月前更新

AI-powered job search pipeline built on Claude Code with 14 skill modes, Go dashboard, PDF generation, batch processing, and portal scanning.

僅含說明Productivity & WorkflowBusiness & Finance
AI 產生的概覽

一套 AI 求職流程，可為職缺評分、產生客製化履歷 PDF、掃描招募入口網站並追蹤應徵進度。

功能
此技能建立一個以 Claude Code 為基礎的求職指揮中心，包含多種模式。它會將職缺描述與候選人檔案及履歷比對，產出 A-F 評分的評估報告、ATS 最佳化的履歷 PDF、入口網站掃描、批次評估與應徵追蹤表。它也涵蓋 LinkedIn 外聯訊息、公司研究、課程評估與申請表填寫，資料保存在 TSV 追蹤表與 Go 終端機儀表板中。
適用情境
適合在進行有系統的求職時使用，希望借助 AI 評估職缺、產生客製化履歷並追蹤應徵。適合想依契合度篩選職缺並集中管理應徵流程的求職者。
執行需求
需要 Node.js 18+ 及 Playwright 與 Chromium 以產生 PDF，Go 1.21+ 用於選用的終端機儀表板，以及具 Anthropic API 金鑰的 Claude Code。需要設定檔（profile.yml、portals.yml、cv.md）與網路存取以抓取招募入口網站。文件中描述了 batch-runner.sh 與 generate-pdf.js 等指令碼，但本技能僅提供說明。

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 repogit clone https://github.com/santifer/career-ops.gitcd career-ops
# 2. Install Node dependencies (for PDF generation via Playwright)npm installnpx playwright install chromium
# 3. Configure your profilecp config/profile.example.yml config/profile.yml# Edit config/profile.yml with your name, target roles, location, comp range, etc.
# 4. Configure portal scannercp 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 fitcat > cv.md << 'EOF'# Your Name
## Experience...your CV content in markdown...EOF
# 6. Build the Go dashboard (optional but recommended)cd dashboardgo 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 installedclaude --version
# Open career-ops in Claude Codeclaude   # 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.ymlname: "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.ymlstatuses:  - 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 dashboardgo 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

go
// dashboard/main.go — entry pointpackage 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)    }}
go
// dashboard/model.go — core data modelpackage 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

go
// dashboard/data.gopackage 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 linecat > batch/queue.txt << 'EOF'https://boards.greenhouse.io/company/jobs/123https://jobs.lever.co/company/456https://jobs.ashbyhq.com/company/789EOF

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="${1:-queue.txt}"MAX_PARALLEL=4PROMPT_FILE="batch-prompt.md"
while IFS= read -r url; do    [[ -z "$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 $MAX_PARALLEL ]]; do        sleep 2    donedone < "$QUEUE_FILE"
waitecho "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.jsconst { 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: ${outputPath}`);}
// Usageconst template = fs.readFileSync('templates/cv-template.html', 'utf8');const company = process.argv[2] || 'company';const role = process.argv[3] || 'role';const outputPath = path.join('output', `${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_pathabc123	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:

DimensionWeightWhat it measures
Role fit20%Match between JD requirements and your CV
Level alignment15%Seniority match
Compensation15%Comp vs your target range
Tech stack15%Stack overlap with your skills
Company stage10%Startup/scale-up/enterprise fit
Remote policy10%Location/remote match
Growth potential5%Career trajectory opportunity
Mission alignment5%Personal interest in the domain
Interview signals3%Glassdoor/process quality signals
Recruiter quality2%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 foundnpx playwright install chromium
# PDF generation fails silentlynode scripts/generate-pdf.js 2>&1 | head -50
# Font not loading in PDF (Space Grotesk / DM Sans)# Ensure fonts/ directory has the .woff2 filesls fonts/# SpaceGrotesk-*.woff2  DMSans-*.woff2

Go dashboard won't build

bash
cd dashboardgo mod tidygo build -o career-dashboard .
# Missing Bubble Tea dependencygo get github.com/charmbracelet/bubbleteago get github.com/charmbracelet/lipglossgo get github.com/charmbracelet/bubbles

TSV parsing errors

bash
# Check pipeline.tsv for malformed rowsawk -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 rootls CLAUDE.md  # Must exist
# Verify modes directoryls modes/     # Should show *.md files
# If Claude doesn't recognize /career-ops, re-open from project root:cd /path/to/career-opsclaude

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

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

來源與署名

來源：reason-machines/trending-skills位於skills/career-ops-job-search提交2384a00

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