hirekit 0.2.0

AI-powered company analysis and interview preparation CLI for job seekers

HireKit

AI-powered company research & interview prep CLI for job seekers

Research companies. Match jobs. Ace interviews.

한국어 | English

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The Problem

You found a great job posting. Now you need to research the company before applying.

So you open 10+ tabs: DART filings, news sites, Glassdoor, GitHub, LinkedIn, salary databases...

4-8 hours later, you have scattered notes and still aren't sure if this company is right for you.

The Solution

pip install hirekit
hirekit analyze 카카오

2 minutes later: A structured report with a 0-100 score, covering financials, culture, tech stack, recent news, and interview tips — all from 8 data sources collected in parallel.

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Quick Start

1. Install

pip install hirekit

Requires Python 3.11+. That's it — no other setup needed for basic use.

2. Get API Keys (optional but recommended)

HireKit works with zero API keys (Google News + credible news sources are free). For richer data, get these free keys:

Key	Where to get it	What it unlocks
DART API Key	opendart.fss.or.kr	Korean company financials, salary, headcount
Naver Client ID/Secret	developers.naver.com	Korean news, blog reviews, interview tips
Brave API Key	brave.com/search/api	Web + news semantic search
Exa API Key	exa.ai	AI-powered deep search

3. Configure

hirekit configure

This creates ~/.hirekit/config.toml and ~/.hirekit/.env. Open the .env file and paste your API keys:

# ~/.hirekit/.env
DART_API_KEY=your_key_here
NAVER_CLIENT_ID=your_id_here
NAVER_CLIENT_SECRET=your_secret_here

4. Analyze a Company

hirekit analyze 카카오

You'll see a scorecard like this:

                     카카오 Scorecard
┌─────────────────────┬────────┬────────┬──────────────────┐
│ Dimension           │ Weight │  Score │ Evidence         │
├─────────────────────┼────────┼────────┼──────────────────┤
│ Job Fit             │    30% │  3.5/5 │ Tech stack data  │
│ Career Leverage     │    20% │  4.6/5 │ 15 data points   │
│ Growth Potential    │    20% │  4.5/5 │ Financials +     │
│                     │        │        │ active news      │
│ Compensation        │    15% │  3.5/5 │ DART salary data │
│ Culture Fit         │    15% │  4.5/5 │ Reviews + Exa    │
│ Total               │        │ 82/100 │ Grade S          │
└─────────────────────┴────────┴────────┴──────────────────┘

A Markdown report is saved to ./reports/카카오_analysis.md.

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All Commands

HireKit has 7 commands covering the full job preparation journey:

hirekit analyze — Research a company

# Basic analysis (saves Markdown report)
hirekit analyze 카카오

# Show results in terminal instead of saving
hirekit analyze 네이버 -o terminal

# JSON output (for scripting)
hirekit analyze 토스 -o json

# Quick analysis (fewer sections)
hirekit analyze 쿠팡 --tier 3

hirekit match — Match a job posting to your profile

# Paste a JD URL
hirekit match "https://www.wanted.co.kr/wd/12345"

# Or use a saved JD text file
hirekit match jd.txt

# With your career profile for personalized matching
hirekit match jd.txt --profile ~/.hirekit/profile.yaml

What you get: Match score (0-100), skill gaps, strengths, and application strategy.

hirekit interview — Prepare for interviews

# Generate interview questions for a company
hirekit interview 카카오

# Specify your target position
hirekit interview 카카오 --position "Backend Engineer"

# Show in terminal
hirekit interview 네이버 --position PM -o terminal

What you get: 5 common questions, role-specific questions, STAR story templates, and 5 reverse questions to ask the interviewer.

hirekit coverletter — Draft a Korean cover letter (자기소개서)

# Generate a 4-section Korean cover letter draft
hirekit coverletter 카카오 --position PM

# With your profile for personalized content
hirekit coverletter 토스 --position PM --profile profile.yaml

# Preview in terminal
hirekit coverletter 네이버 -o terminal

What you get: 4-section draft (성장과정, 지원동기, 직무역량, 장단점) with per-section scoring and improvement feedback.

hirekit resume — Review your resume

# Review a resume file
hirekit resume resume.md

# Review against a specific job description
hirekit resume resume.md --jd "https://wanted.co.kr/wd/12345"

# With career profile
hirekit resume resume.pdf --profile profile.yaml

What you get: ATS compatibility check, structure analysis, keyword gaps vs JD, content quality score, and improvement suggestions.

hirekit sources — Check data source status

hirekit sources

Shows which data sources are configured and ready:

                    Data Sources
┌────────────┬────────┬─────────────────┬────────────────┐
│ Name       │ Region │ API Key         │ Status         │
├────────────┼────────┼─────────────────┼────────────────┤
│ dart       │ KR     │ DART_API_KEY    │ Ready          │
│ github     │ GLOBAL │ -               │ Ready          │
│ google_news│ GLOBAL │ -               │ Ready          │
│ naver_news │ KR     │ NAVER_CLIENT_ID │ Not configured │
└────────────┴────────┴─────────────────┴────────────────┘

hirekit configure — Set up API keys and preferences

hirekit configure

Creates default config files. Edit ~/.hirekit/.env to add your API keys.

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Data Sources (8 built-in)

Source	Region	What it provides	API Key	Free?
DART	Korea	Financials, salary, headcount from official filings	DART_API_KEY	Yes
Naver News	Korea	Recent Korean news articles	NAVER_CLIENT_ID	Yes
Naver Search	Korea	Blog/cafe reviews, interview tips, culture info	NAVER_CLIENT_ID	Yes
GitHub	Global	Tech maturity score (repos, stars, languages)	gh CLI auth	Yes
Google News	Global	Latest news via RSS	None needed	Yes
Credible News	Global	Reuters, Bloomberg, FT, WSJ + Korean biz press	None needed	Yes
Brave Search	Global	Web + news semantic search	BRAVE_API_KEY	Free tier
Exa Search	Global	AI-powered semantic deep search	EXA_API_KEY	Free tier

You can start with zero API keys. Google News, Credible News, and GitHub (if you have gh CLI) work without any setup.

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AI Enhancement (Optional)

HireKit works perfectly without AI — reports are generated using templates and rules. To get deeper, AI-powered analysis:

# Install with OpenAI support
pip install "hirekit[openai]"

# Or Anthropic Claude
pip install "hirekit[anthropic]"

# Or local models via Ollama (fully offline, free)
pip install "hirekit[ollama]"

Then set your API key in ~/.hirekit/.env:

OPENAI_API_KEY=sk-...
# or
ANTHROPIC_API_KEY=sk-ant-...

And update ~/.hirekit/config.toml:

[llm]
provider = "openai"  # or "anthropic", "ollama"
model = "gpt-4o-mini"

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Career Profile (Optional)

Create ~/.hirekit/profile.yaml to get personalized matching:

name: "Your Name"
years_of_experience: 5

tracks:
  - name: "Product Manager"
    priority: 1

career_assets:
  - asset: "Built payment system"
    source: "Previous Company"
    applicable_industries: ["fintech", "ecommerce"]

skills:
  technical: ["Python", "SQL", "Data Analysis"]
  domain: ["Payment Systems", "E-commerce"]
  soft: ["Cross-functional Communication"]

preferences:
  regions: ["kr"]
  industries: ["fintech", "platform"]
  work_style: ["hybrid"]

When you pass --profile, HireKit matches your skills against job requirements and tailors interview questions to your experience.

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Adding Custom Data Sources

Want to add your own data source? It's just one Python class:

from hirekit.sources.base import BaseSource, SourceRegistry, SourceResult

@SourceRegistry.register
class GlassdoorSource(BaseSource):
    name = "glassdoor"
    region = "global"
    sections = ["culture"]

    def is_available(self) -> bool:
        return True  # or check for API key

    def collect(self, company, **kwargs):
        # Your scraping/API logic here
        return [SourceResult(
            source_name=self.name,
            section="culture",
            data={"rating": 4.2, "reviews": 150},
            raw="Glassdoor rating: 4.2/5 from 150 reviews",
        )]

See CONTRIBUTING.md for the full guide.

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Roadmap

• v0.1 — Company analysis, scorecard, 8 data sources
• v0.1 — JD matching, interview prep, resume review, cover letter coach
• v0.2 — US companies (SEC Edgar), improved report quality
• v0.3 — Web UI, community plugins, agent architecture

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Contributing

We welcome contributions! Here are some good starting points:

• Add a new data source (Glassdoor, LinkedIn, SEC Edgar)
• Improve Korean cover letter templates
• Add support for Japanese/Chinese job markets
• Improve the scoring algorithm

See CONTRIBUTING.md for setup instructions.

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License

MIT License. See LICENSE.

_{Built for every job seeker who deserves better tools.}