careerclaw 0.7.1

CareerClaw: AI-powered job search assistant (ClawHub/OpenClaw skill)

CareerClaw

Privacy-first job search automation for OpenClaw.

CareerClaw turns your AI agent into a structured daily workflow: fetch listings → rank matches → draft outreach → track applications.

• Local-first: your resume and results stay on your machine
• No subscription: one-time purchase for Pro
• Bring your own LLM API key (optional): use OpenAI/Anthropic to enhance drafts

Works best for roles where job boards and public listings are common (engineering, product, design, ops, marketing, finance).

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How It Works

1. Fetches job listings from supported sources (baseline sources are always available; integrations expand over time)
2. Ranks them against your profile using keyword overlap, experience alignment, salary fit, and work-mode preference
3. Drafts outreach for each top match (deterministic template in Free; optional LLM enhancement in Pro)
4. Tracks your application pipeline locally (JSON files under .careerclaw/)

One command. Everything local.

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Quickstart

1. Install

git clone https://github.com/orestes-garcia-martinez/careerclaw
cd careerclaw
python -m venv .venv

# Activate (macOS/Linux)
source .venv/bin/activate

# Activate (Windows PowerShell)
.\.venv\Scripts\Activate

# Install with dev dependencies
pip install -e ".[dev]"

2. Set up via OpenClaw (recommended)

If you are running CareerClaw through OpenClaw/ClawHub, the agent can guide you through setup. Provide your resume and it will create your profile, ask a couple of preference questions (work mode + salary), and run your first briefing.

3. Set up manually

Create the runtime directory and your profile:

mkdir -p .careerclaw

Create .careerclaw/profile.json:

{
  "skills": ["python", "react", "sql"],
  "target_roles": ["data analyst", "backend engineer"],
  "experience_years": 5,
  "work_mode": "remote",
  "resume_summary": "Experienced analyst with 5 years delivering data pipelines and dashboards.",
  "location": "Austin, TX",
  "salary_min": 90000
}

4. Run your first briefing

# Dry run first — no files written, safe to preview
python -m careerclaw.briefing --dry-run

# With your resume for better match quality (recommended)
python -m careerclaw.briefing --resume-pdf .careerclaw/resume.pdf --dry-run

# Full run when you're happy with the results
python -m careerclaw.briefing --resume-pdf .careerclaw/resume.pdf

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Sample Output

=== CareerClaw Daily Briefing ===
User: local-user
Fetched jobs: 244 | After dedupe: 244
Duration: 29482ms

Top Matches:
...

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Free vs Pro

Feature	Free	Pro
Job ingestion (baseline supported sources)	✅	✅
Additional job sources / integrations	❌	✅ (as released)
Top matches with score breakdown	✅	✅
Outreach email draft (deterministic)	✅	✅
Application tracking (local JSON)	✅	✅
Manual briefing trigger	✅	✅
JSON output for agent integration	✅	✅
Gap analysis (ATS shadowing)	❌	✅
LLM-enhanced outreach (your LLM API key)	❌	✅
Resume intelligence (section-aware weighting)	❌	✅
Scheduled / automated daily briefings	❌	✅ (roadmap)
CSV / Sheets export	❌	✅ (roadmap)

Pro tier: $39 one-time (lifetime license).

Purchase on Gumroad: https://ogm.gumroad.com/l/careerclaw-pro

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Optional: Pro + Setup & Configuration (1:1)

If you want it running quickly, Gumroad also offers “Pro + Setup & Configuration (1:1)” (limited slots). It includes:

• Install CareerClaw into your OpenClaw workspace
• Configure env vars + optional LLM API keys
• Get your first daily briefing running successfully
• Async troubleshooting included

(Select that version at checkout.)

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Pro: Upgrading

Purchase a license key on Gumroad. Gumroad delivers the key by email immediately after payment.

Activating — Docker / self-hosted users

docker compose run --rm openclaw-cli \
  config set agents.defaults.sandbox.docker.env.CAREERCLAW_PRO_KEY "YOUR-KEY-HERE"

Or add it to your .env file:

CAREERCLAW_PRO_KEY=YOUR-KEY-HERE

The key is activated on first use and cached locally as a SHA-256 hash. Re-validation happens every 7 days (requires internet access).

Activating — MyClaw managed users

Tell your OpenClaw agent:

"Set my CAREERCLAW_PRO_KEY to YOUR-KEY-HERE"

The agent stores the key in your OpenClaw config and activates it on the next CareerClaw run.

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Pro: LLM-Enhanced Drafts

With a valid Pro license, you can supply your own LLM API key to receive personalized outreach emails. CareerClaw falls back to the deterministic template silently on any failure.

export CAREERCLAW_PRO_KEY=YOUR-KEY-HERE
export CAREERCLAW_LLM_KEY=sk-...
python -m careerclaw.briefing --resume-pdf .careerclaw/resume.pdf

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Application Tracking

Tracking is written automatically on each non-dry-run. Status options:

saved → applied → interview → rejected

Runtime files — all stored under .careerclaw/ (gitignored by default):

File	Contents
profile.json	Your profile
resume.txt / resume.pdf	Your resume
tracking.json	Saved jobs keyed by stable job_id
runs.jsonl	Append-only run log (one line per run)
resume_intel.json	Cached resume intelligence (Pro)
.license_cache	Pro license validation cache (SHA-256 hash only)

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Security & Privacy

CareerClaw is built on a local-first architecture. Your data never leaves your machine unless you configure an LLM API key.

• No backend. No telemetry. No analytics endpoint.
• API keys never stored. LLM keys are read from the environment at runtime and never written to disk or logs.
• License cache is hash-only. Only a SHA-256 hash of the license key is written locally — the raw key is never stored.
• No PII transmission. Your resume, profile, and application history are stored only in .careerclaw/ on your local machine.

See SECURITY.md for the vulnerability disclosure policy.

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Changelog

See CHANGELOG.md for the full version history.

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License

• Free tier: MIT License — see LICENSE
• Pro tier: Commercial license

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Support

• GitHub Issues: for bug reports and feature requests
• Response SLA: critical bugs < 48h · general questions < 72h
• Security disclosures: see SECURITY.md
• Pro inquiries: [e-mail]