hirekit 0.1.0

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

HireKit

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

Research companies. Match jobs. Ace interviews.

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Why It Matters

You're in an interview. Your interviewer asks: "Why do you want to work here?"

You pause. You didn't research the company beyond the job posting.

90% of rejected candidates cite "unprepared" as feedback.

Meanwhile, company research takes 4-8 hours across fragmented sources:

• DART filings (Korean finance jargon, hard to parse)
• News articles (biased, scattered across 10+ sites)
• Glassdoor reviews (complaint-focused, outdated)
• GitHub (tech only, no culture insights)
• LinkedIn, internal wikis, salary data

No tool combines these into one actionable intelligence report.

HireKit solves this: One command, 2 minutes, 8 data sources, 1 decision-ready report.

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How It Works

# Step 1: Install (30 seconds)
$ pip install hirekit

# Step 2: Configure API keys (1 minute, one-time)
$ hirekit configure
> DART API Key: [paste]
> Naver Client ID: [paste]

# Step 3: Analyze a company (2 minutes)
$ hirekit analyze 카카오

# Output: 12-section decision report

Total time: 3 minutes vs. 4-8 hours of manual research

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What You Get

Single Actionable Score: 0-100 Job Fit Rating

82/100 = Strong Opportunity (Go for it)
75/100 = Competitive (Worth applying with prep)
60/100 = Caution (Red flags exist — investigate)

12-Section Report covering:

• Executive summary with key reasons
• Financial health (salary negotiation potential)
• Tech stack & interview depth questions
• Recent news & company trajectory
• Culture & team dynamics
• Risk flags & mitigation strategies
• Interview prep tips specific to this company

Multi-source cross-validation:

• 8+ data sources collected in parallel
• Conflicts highlighted ("News says growing, DART shows debt spike — investigate")
• Evidence-based scoring, not gut feeling
• All sources cited with dates

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Quick Start

# Install
pip install hirekit

# Configure (set up API keys)
hirekit configure

# Analyze a company
hirekit analyze 카카오

# View available data sources
hirekit sources

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What's Next?

After analyzing a company, you can:

# Compare companies side-by-side
$ hirekit compare 카카오 네이버 --focus salary,growth

# Match your resume to a job posting
$ hirekit match https://wanted.co.kr/job-123 resume.pdf

# Prepare interview questions specific to this company
$ hirekit interview 카카오 --role backend-engineer

# Get interview feedback on your resume
$ hirekit resume review resume.pdf --company 카카오

👉 Full Tutorial | CLI Reference | FAQ

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Features

• 8-source parallel collection — DART financials, Google/Naver/Brave/Exa news, Reuters, Korean biz press, GitHub tech scoring, Glassdoor reviews (all collected simultaneously)
• 12-section structured reports — Executive summary, financial health, tech stack, news/trajectory, culture, compensation, growth potential, risks, interview prep, scorecard, similar companies, action items
• Weighted 5-dimension scorecard — Job Fit (30%), Career Leverage (20%), Growth Potential (20%), Compensation (15%), Culture Fit (15%) — 100-point decision score, not subjective rating
• LLM-optional — Works without any AI (template mode for offline use), enhanced with OpenAI/Anthropic/Ollama for deeper analysis
• Plugin architecture — Add custom data sources in 20 lines of Python, no core changes needed
• Privacy-first — All data stays local, no external tracking, no cloud uploads

Quick Start

# Install
pip install hirekit

# Configure (set up API keys)
hirekit configure

# Analyze a company
hirekit analyze 카카오

# View available data sources
hirekit sources

Demo

$ hirekit analyze 카카오 --no-llm -o terminal

╭──────────────────── HireKit Analysis ────────────────────╮
│ Analyzing: 카카오                                        │
│ Region: kr  Tier: 1  LLM: off                            │
╰──────────────────────────────────────────────────────────╯

                     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          │
└─────────────────────┴────────┴────────┴──────────────────┘

8 data sources collected 15 results in parallel — DART financials, Google/Naver/Brave/Exa news, Reuters, Korean biz press, GitHub tech scoring, Glassdoor reviews.

Data Sources

Source	Region	Data	API Key
DART	KR	Financial filings, employee data	DART_API_KEY
Naver News	KR	Recent news articles	NAVER_CLIENT_ID
Naver Search	KR	Blog, cafe, web (culture/interview)	NAVER_CLIENT_ID
GitHub	Global	Tech maturity scoring	gh CLI
Google News	Global	RSS news (no key needed)	-
Credible News	Global	Reuters, Bloomberg, FT, WSJ + Korean biz press	-
Brave Search	Global	Web + news semantic search	BRAVE_API_KEY
Exa Search	Global	AI semantic deep search	EXA_API_KEY

Adding Custom Sources

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

@SourceRegistry.register
class MySource(BaseSource):
    name = "my_source"
    region = "global"
    sections = ["tech"]

    def is_available(self) -> bool:
        return True

    def collect(self, company, **kwargs):
        # Your data collection logic here
        return [SourceResult(
            source_name=self.name,
            section="tech",
            data={"key": "value"},
        )]

Configuration

Config lives in ~/.hirekit/config.toml:

[analysis]
default_region = "kr"
cache_ttl_hours = 168  # 7 days

[llm]
provider = "none"  # openai, anthropic, ollama, none
model = "gpt-4o-mini"

[sources]
enabled = ["dart", "github", "naver_news"]

[output]
format = "markdown"
directory = "./reports"

LLM Support

HireKit works without any LLM (template-based reports). For AI-enhanced analysis:

# OpenAI
pip install hirekit[openai]
# Set OPENAI_API_KEY in ~/.hirekit/.env

# Anthropic
pip install hirekit[anthropic]

# Local models via Ollama
pip install hirekit[ollama]

# Or use litellm for 100+ providers
pip install hirekit[llm]

Roadmap

• Phase 1: DART + GitHub + News analysis, scorecard, Markdown reports
• Phase 2: JD matching (hirekit match), interview prep (hirekit interview), resume review (hirekit resume)
• Phase 3: US companies (SEC Edgar), web UI, community plugins, PyPI publish

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

Good first issues:

• Add a new data source plugin
• Improve report templates
• Add i18n support

License

MIT License. See LICENSE for details.

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_{Built with care for every job seeker out there.}