ClawHub

BDJobs Job Apply

Automation

BDJobs job search, matching, applying, undoing, and salary-update automation for OpenClaw. Use when the user wants to set up BDJobs credentials/preferences, search fresh non-applied jobs, inspect job details, auto-apply to matched jobs, cancel an application, or update expected salary.

Install

openclaw skills install bdjobs-job-apply

BDJobs Job Apply

Use this skill to help a user manage BDJobs job searching and applying without needing to know APIs or code.

What this skill does

  • Save resume text/LaTeX into data/resume.md
  • Save credentials and preferences into data/userDetails.json
  • Log in and save auth data into data/loggedInData.json
  • Refresh applied jobs into data/appliedJobIds.json
  • Keep a not-liked list in data/notLikedJobIds.json
  • Search jobs and return raw job details for AI matching
  • Auto-apply to matched jobs when asked
  • Undo/cancel an applied job when asked
  • Update expected salary for an already applied job

Onboarding flow

When the user first sets up this skill, ask for these values in plain language:

  1. BDJobs username
  2. BDJobs password
  3. Resume text or LaTeX
  4. everyDayApplyCount (default: 2)
  5. everyDayCronTime (default: 12:00 PM)
  6. isFresher (yes/no)
  7. Experience in years
  8. Preferred jobLocation (optional, leave blank for all)

Save them in:

  • data/userDetails.json
  • data/resume.md

If the user has extra search filters or matching rules, store them in data/preferences.json.

Login flow

Before search/apply work, always ensure data/loggedInData.json exists.

If missing:

  1. run ChecKUsername
  2. take guidId
  3. run Login
  4. save token, refreshToken, encryptId, decodeId, and guidId to data/loggedInData.json

If login returns 401 at any point:

  1. run ChecKUsername again
  2. run Login again
  3. overwrite data/loggedInData.json

Search flow

When the user asks for fresh jobs:

  1. Clear data/suggestedJobs.json
  2. Run scripts/bdjobs-refresh-applied.js to update data/appliedJobIds.json from GetApplyPositionInfoV1
  3. Run scripts/bdjobs-fetch-jobs.js with --keyword, --isFresher, --postedWithin, --pg, and --jobLocation to fetch raw job lists from GetJobSearch
  4. Run scripts/bdjobs-filter-jobs.js to exclude already-applied and not-liked job IDs before Job-Details
  5. For selected job IDs, run scripts/bdjobs-job-details.js to fetch full job details
  6. Let AI compare the raw job details with data/resume.md and data/preferences.json
  7. Run scripts/bdjobs-rank-jobs.js to save the top 5 matched jobs into data/suggestedJobs.json
  8. Show the contents of data/suggestedJobs.json to the user

Recommended match threshold:

  • show apply suggestions only if match is above 40%
  • if the user asks for a looser list, show 30%+

AI matching details:

  • Compare the raw job details with data/resume.md and data/preferences.json
  • Judge fit from skills, education, age, experience, responsibilities, location, and preferences
  • Output a suggested job list with match percentages

Auto-apply flow

When the user asks to auto-apply:

  1. Ensure login exists
  2. Refresh applied jobs first
  3. Search fresh jobs
  4. Let AI score the raw job details against the resume and preferences
  5. For each approved job:
    • call JobApply
    • read MinimumSalary
    • use it as expectedSalary in JobApplyPost
    • call JobApplyPost
    • if successful, always show matchingScore
    • if successful, add the job ID to data/appliedJobIds.json
    • save result in data/lastApplyResult.json
  6. Send Telegram notification only after success

Undo/cancel flow

When the user asks to undo an application:

  1. Ensure login exists
  2. Call UndoJobApply with job ID and FormValue
  3. If successful, remove the job ID from data/appliedJobIds.json
  4. Save result in data/lastUndoResult.json

Expected salary update flow

When the user asks to update salary for an applied job:

  1. Ensure login exists
  2. Call UpdateExpectedSalary
  3. Save result in data/lastSalaryUpdateResult.json

Files used by this skill

Important files live inside this skill folder:

  • data/resume.md
  • data/userDetails.json
  • data/loggedInData.json
  • data/appliedJobIds.json
  • data/notLikedJobIds.json
  • data/preferences.json
  • data/suggestedJobs.json
  • data/lastApplyResult.json
  • data/lastUndoResult.json
  • data/lastSalaryUpdateResult.json

Script entry points

Run scripts from the skill folder, not the workspace root:

  • scripts/init-job-profile.js
  • scripts/bdjobs-login.js
  • scripts/bdjobs-refresh-applied.js
  • scripts/bdjobs-fetch-jobs.js

bdjobs-fetch-jobs.js accepts:

  • --keyword=...
  • --isFresher=true|false
  • --postedWithin=... (optional day count, any number)
  • --pg=...
  • --jobLocation=...
  • scripts/bdjobs-filter-jobs.js
  • scripts/bdjobs-job-details.js accepts --jobId=... or a positional job id
  • scripts/bdjobs-rank-jobs.js
  • scripts/bdjobs-apply.js
  • scripts/bdjobs-undo.js
  • scripts/bdjobs-update-salary.js

Matching rules

Use the resume and these signals:

  • Job title
  • Company name
  • Job description
  • Education requirements
  • Age requirement
  • Experience requirement
  • Additional job requirements
  • Suggested skills
  • Job location
  • Job nature
  • User preferences from data/preferences.json

Helpful behavior

When presenting jobs to the user:

  • keep language simple
  • explain only what matters
  • include the direct job link
  • mention whether it looks worth applying

When the user wants automation, do the work directly with the scripts.

More in Automation