Auto Job Applier Skill

How to set RESUMEX_API_KEY in OpenClaw:

1. Go to resumex.dev → Dashboard → Resumex API
2. Click Generate API Key
3. In OpenClaw, go to Settings → Environment Variables
4. Add RESUMEX_API_KEY with the copied key value

No other keys are needed. There is no Anthropic key, no OpenAI key, no other third-party service.

Privacy & Data Handling

Read this before using the skill.

What data leaves your device

Data sent to resumex.dev is governed by the resumex.dev Privacy Policy.

What stays local on your device

⚠️ user_preferences.json may contain sensitive personal data including date of birth, gender, ethnicity, veteran status, and disability status (only if you choose to save these when prompted during a form fill). This file is stored locally only and is never sent to resumex.dev or any other server. Review and restrict its filesystem permissions if needed:

chmod 600 data/user_preferences.json

To clear all saved preferences at any time:

python3 scripts/manage_preferences.py reset

Sensitive fields in preferences

The following fields are only saved if you explicitly provide them when the agent encounters a form field that requires them. You can decline to answer, skip the field, or delete a saved value at any time:

• gender — only for diversity/EEO forms
• ethnicity — only for diversity/EEO forms (optional, you may leave blank)
• veteran_status — only for U.S. government/contractor compliance forms
• disability_status — only for compliance forms (optional)
• date_of_birth — only for forms that legally require it

The agent will always tell you which form requires a sensitive field before asking for the value.

Auto-submit safeguard

The agent NEVER submits an application without your explicit approval. Step 6 of the workflow always presents a formatted approval list and waits for your response before any browser interaction begins. If you are testing, set MAX_APPLICATIONS=1 in your environment.

Workflow Overview

1. Fetch resume data from resumex.dev API
2. Load saved user preferences (salary, visa, screening answers)
3. Build a job-match profile (skills, roles, seniority, preferences)
4. Search the web for matching jobs (3–5 query permutations)
5. Score & rank each job against the resume (0–100)
6. ⛔ APPROVAL GATE — Present formatted list → wait for user selection
7. For each approved job:
   a. Generate a tailored cover letter (via OpenClaw's built-in AI)
   b. Navigate to application page via browser
   c. Fill form fields using resume data + preferences
   d. If a required field is unknown → ask the user → save to preferences
   e. Submit the application
   f. Log to resumex.dev job tracker
8. Present final summary with statuses

Step 1 — Fetch Resume from resumex.dev

Use the agent endpoint. All calls require Authorization: Bearer $RESUMEX_API_KEY.

# Fetch full resume data (GET /api/v1/agent — the correct endpoint)
curl -s -X GET "https://resumex.dev/api/v1/agent" \
  -H "Authorization: Bearer $RESUMEX_API_KEY" \
  -H "Content-Type: application/json"

Note: The endpoint is /api/v1/agent (NOT /api/v1/agent/resume, which is deprecated). The helper scripts handle retries with exponential backoff automatically. Install dependencies first: pip3 install -r requirements.txt

Or via the helper script:

# Full resume JSON
python3 scripts/fetch_resume.py

# Extract a specific field for form filling
python3 scripts/fetch_resume.py --field email
python3 scripts/fetch_resume.py --json-path profile.phone

Expected response shape:

{
  "success": true,
  "data": {
    "activeResumeId": "...",
    "resumes": [{
      "id": "...",
      "data": {
        "profile": {
          "fullName": "...", "email": "...", "phone": "...",
          "location": "...", "summary": "...",
          "linkedin": "...", "github": "...", "website": "..."
        },
        "skills": [{"category": "...", "skills": ["...", "..."]}],
        "experience": [
          {
            "role": "...", "company": "...", "location": "...",
            "startDate": "...", "endDate": "...", "description": "..."
          }
        ],
        "education": [{"degree": "...", "institution": "...", "endDate": "...", "score": "..."}],
        "projects": [{"name": "...", "description": "...", "tags": ["..."]}],
        "achievements": [{"title": "...", "year": "..."}]
      }
    }]
  }
}

Parse the active resume:

workspace = response.data
activeResume = workspace.resumes.find(r => r.id === workspace.activeResumeId)
resumeData = activeResume.data

Error handling:

Step 2 — Load Saved User Preferences

Check for previously saved preferences that supplement the resume data:

python3 scripts/manage_preferences.py list

This returns any saved answers like salary expectation, visa status, notice period, etc. If user_preferences.json doesn't exist yet, that's fine — it will be created when the user is first asked for missing information.

Preference fields to look for:

• salary_expectation — e.g. "8-12 LPA" or "$80,000-$100,000"
• currency — e.g. "INR" or "USD"
• notice_period — e.g. "30 days" or "Immediate"
• visa_status — e.g. "No visa required (Indian citizen)"
• work_authorization — e.g. "Authorized to work in India"
• willing_to_relocate — true/false
• preferred_work_type — "remote" | "hybrid" | "onsite"
• screening_answers — dict of previously answered screening questions

Step 3 — Build Job-Match Profile

From the resume JSON and user preferences, extract and infer:

Example derived profile:

Roles: Software Engineer, Backend Developer, Full Stack Developer
Skills: Python, Django, React, PostgreSQL, Docker, AWS
Seniority: Mid-level (3 years)
Location: Pune, India
Type: Full-time
Remote: No preference
Salary: 8-12 LPA (from preferences)

Step 4 — Search for Matching Jobs

Use web_search to find real, current job postings. Run 3–5 targeted searches using different query permutations to maximize coverage.

Query templates:

"{role}" "{top_skill}" jobs "{location}" site:linkedin.com OR site:naukri.com OR site:indeed.com
"{role}" "{top_skill}" "{second_skill}" hiring 2026
"{role}" remote jobs "{top_skill}" "{seniority}"
"{role}" "{top_skill}" jobs "{location}" "apply now" site:wellfound.com OR site:internshala.com

See references/job_boards.md for complete query patterns per board.

For each search result URL, use web_fetch to extract:

• Job title
• Company name
• Location (or Remote)
• Job URL (apply link)
• Required skills (from description)
• Nice-to-have skills
• Experience required
• Salary range (if visible)
• Application method: form | easy-apply | email | redirect

Aim to collect 10–20 raw job postings before scoring.

Step 5 — Score & Rank Jobs

Score each job 0–100 against the resume profile:

Formula:

score = (skills_matched / skills_required) * 40
      + role_title_match * 20    # 20 if exact, 10 if adjacent, 0 if unrelated
      + seniority_match * 15     # 15 if exact, 8 if ±1 level, 0 if 2+ off
      + location_match * 15      # 15 if match, 8 if remote, 0 if mismatch
      + industry_match * 10      # 10 if same industry, 5 if adjacent

Step 6 — Present Approval List to User ⛔

Present the top 10 matches in a formatted table. The user MUST approve before any applications are submitted. Never auto-apply without explicit approval. Never skip this step.

Format:

🎯 Job Match Results for [Name]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

#   Score  Company          Role                     Location        Apply Method
──  ─────  ───────          ────                     ────────        ────────────
1   92     Acme Corp        Software Engineer        Pune (On-site)  🤖 Auto-apply
2   87     TechStartup      Backend Developer        Remote          🤖 Auto-apply
3   81     MegaCorp India   Full Stack Engineer      Mumbai          🤖 Auto-apply
4   76     DevShop          Python Developer         Pune (Hybrid)   🤖 Auto-apply
5   73     CloudCo          API Engineer             Remote          🤖 Auto-apply
6   70     DataInc          Backend Engineer         Bangalore       🔗 Manual (LinkedIn)
7   68     StartupXYZ       Software Developer       Remote          🤖 Auto-apply
8   65     BigTech          Junior SWE               Hyderabad       🤖 Auto-apply
9   62     ConsultFirm      Technical Consultant     Pune            📧 Email apply
10  58     SmallCo          Full Stack Developer     Remote          🤖 Auto-apply

🤖 = Agent will fill and submit the application automatically
🔗 = LinkedIn — agent will open the page, you submit manually
📧 = Email — agent will draft the email, you review and send

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Which jobs would you like to apply to?
Options: "all", "1,3,5", "1-5", "none", or "1-5 except 3"

Apply method classification:

• 🤖 Auto-apply — Standard form-based application. Agent fills and submits.
• 🔗 Manual (LinkedIn) — LinkedIn Easy Apply. Agent navigates to the page but user must submit. (LinkedIn automated submission is disabled by default due to ToS concerns.)
• 📧 Email apply — Agent drafts the application email for user review.
• 🔗 Manual (redirect) — Redirects to external ATS. Agent navigates, user may need to complete.

Wait for the user to respond with their selection before proceeding.

Step 7 — Auto-Apply to Approved Jobs

For each job the user approved, execute the following sub-steps:

7a. Generate Cover Letter (via OpenClaw's built-in AI)

No external AI API is used. Cover letters are generated by OpenClaw's own LLM.

The draft_cover_letter.py script reads resume data and job details, then outputs a structured prompt. OpenClaw's agent uses that prompt with its built-in AI to generate the cover letter.

python3 scripts/draft_cover_letter.py \
  --resume /tmp/resume.json \
  --job_title "Software Engineer" \
  --company "Acme Corp" \
  --job_description "We are looking for..." \
  --output /tmp/cover_letter_acme.txt

The script outputs the generation prompt to stdout. The agent then:

1. Reads the prompt
2. Uses its built-in LLM to generate the cover letter text
3. Saves the result to the --output path if specified

Cover letter structure:

1. Hook (1 sentence): why this specific company/role excites the candidate
2. Match (2–3 sentences): specific skills/experiences that directly map to job requirements
3. Value add (1–2 sentences): a concrete result from their past work
4. Close (1 sentence): call to action

Keep it under 200 words. Professional but human tone.

7b. Navigate to Application Page

Use the browser tool to navigate to the job's application URL: