claw-apply/SPEC.md

claw-apply — Technical Spec

Automated job search and application engine. Searches LinkedIn and Wellfound for matching roles, AI-filters and scores them, applies automatically using Playwright + Kernel stealth browsers, and self-learns from unknown questions via Telegram.

---

Architecture

Four agents, shared queue

JobSearcher (job_searcher.mjs)

• Runs on schedule (default: every 12 hours)
• Searches configured platforms with configured keywords
• LinkedIn: paginates through up to 40 pages of results
• Wellfound: infinite-scrolls up to 10 times to load all results
• Classifies each job: Easy Apply, external ATS (with platform detection), recruiter-only
• Filters out excluded roles/companies
• Deduplicates by job ID and URL against existing queue
• Cross-track duplicate IDs get composite IDs ({id}_{track})
• Writes new jobs to jobs_queue.json with status new
• Sends Telegram summary

JobFilter (job_filter.mjs)

• Runs on schedule (default: every hour at :30)
• Two-phase: submit batch → collect results (designed for cron)
• Submits unscored jobs to Claude AI via Anthropic Batch API (50% cost savings)
• One batch per search track for prompt caching efficiency
• Scores each job 1-10 based on match to profile and search track
• Jobs below threshold (default 5) marked filtered
• Cross-track deduplication: groups by URL, keeps highest score
• State persisted in data/filter_state.json between phases

JobApplier (job_applier.mjs)

• Runs on schedule (disabled by default until ready)
• Processes Telegram replies at start (safety net for answer learning)
• Reloads answers.json before each job (picks up mid-run Telegram replies)
• Reads queue for status new + needs_answer, sorted by priority
• Respects max_applications_per_run cap and enabled_apply_types filter
• Groups jobs by platform to share browser sessions
• LinkedIn Easy Apply: multi-step modal with shadow DOM handling
• Wellfound: form fill and submit
• On unknown required fields: generates AI answer, messages user via Telegram, marks needs_answer
• Browser crash recovery: detects dead page, creates fresh browser session
• Per-job timeout: 10 minutes. Overall run timeout: 45 minutes
• On error: retries up to max_retries (default 2) before marking failed
• Sends summary with granular skip reasons

TelegramPoller (telegram_poller.mjs)

• Runs every minute via OpenClaw cron
• Polls Telegram getUpdates for replies to question messages
• Matches replies via reply_to_message_id stored on jobs
• "ACCEPT" → use AI-suggested answer. Anything else → use reply text
• Saves answer to answers.json (reused for ALL future jobs)
• Flips job back to new for retry
• Sends confirmation reply on Telegram
• Lightweight: single HTTP call, exits immediately if no updates

Preview mode (--preview): shows queued jobs without applying.

Shared modules

Module	Responsibility
lib/constants.mjs	All timeouts, selectors, defaults — no magic numbers in code
lib/browser.mjs	Browser factory — Kernel stealth (default) with local Playwright fallback
lib/session.mjs	Kernel Managed Auth session refresh
lib/env.mjs	.env loader (no dotenv dependency)
lib/form_filler.mjs	Form filling — custom answers, built-in profile matching, fuzzy select matching
lib/ai_answer.mjs	AI answer generation via Claude (profile + resume context)
lib/filter.mjs	AI job scoring via Anthropic Batch API
lib/keywords.mjs	AI-generated search keywords via Claude
lib/queue.mjs	Queue CRUD with in-memory caching, atomic writes, config validation
lib/notify.mjs	Telegram Bot API — send, getUpdates, reply (with rate limiting)
lib/telegram_answers.mjs	Telegram reply processing — matches to jobs, saves answers
lib/search_progress.mjs	Per-platform search resume tracking
lib/lock.mjs	PID-based lockfile with graceful shutdown
lib/apply/index.mjs	Apply handler registry with status normalization
lib/apply/easy_apply.mjs	LinkedIn Easy Apply — shadow DOM, multi-step modal, post-submit detection

---

LinkedIn Easy Apply — Technical Details

LinkedIn renders the Easy Apply modal inside shadow DOM. This means:

• document.querySelector() inside page.evaluate() cannot find modal elements
• page.$() and ElementHandle methods pierce shadow DOM and work correctly
• All modal operations use ElementHandle-based operations, never evaluate with document.querySelector

Button detection

findModalButton() uses three strategies in order:

1. CSS selector via page.$() — aria-label exact match (pierces shadow DOM)
2. CSS selector via page.$() — aria-label substring match
3. modal.$$('button') + btn.evaluate() — text content matching

Check order per step: Next → Review → Submit (submit only when no forward nav exists).

Modal flow

Easy Apply click → [fill fields → Next] × N → Review → Submit application

• Progress tracked via <progress> element (not [role="progressbar"])
• Stuck detection: re-reads progress value after clicking Next, triggers after 3 unchanged clicks
• Submit verification: waitForSelector(state: 'detached', timeout: 8s) — event-driven, not fixed sleep
• Post-submit: checks for success text, absent Submit button, or validation errors
• Multiple [role="dialog"] elements: findApplyModal() identifies the apply modal and tags it with data-claw-apply-modal

Form filling

• Labels found by walking up ancestor DOM (LinkedIn doesn't use label[for="id"])
• Label deduplication for doubled text (e.g. "Phone country codePhone country code")
• Resume selection: selects first radio if none checked, falls back to file upload
• Select matching: selectOptionFuzzy() — exact → case-insensitive → substring → value
• Phone always overwritten (LinkedIn pre-fills wrong numbers)
• EEO/voluntary fields auto-select "Prefer not to disclose"
• Honeypot detection: questions containing "digit code", "secret word", etc.

Dismiss flow

Always discards — never leaves drafts:

1. Click Dismiss/Close button or press Escape
2. Wait for Discard confirmation dialog
3. Click Discard (by data-test-dialog-primary-btn or text scan scoped to dialogs)

---

Config files

All user config is gitignored. Example templates are committed.

profile.json

{
  "name": { "first": "Jane", "last": "Smith" },
  "email": "jane@example.com",
  "phone": "555-123-4567",
  "location": {
    "city": "San Francisco",
    "state": "California",
    "zip": "94102",
    "country": "United States"
  },
  "linkedin_url": "https://linkedin.com/in/janesmith",
  "resume_path": "/home/user/resume.pdf",
  "years_experience": 7,
  "work_authorization": {
    "authorized": true,
    "requires_sponsorship": false
  },
  "willing_to_relocate": false,
  "desired_salary": 150000,
  "cover_letter": "Your cover letter text here."
}

search_config.json

{
  "first_run_days": 90,
  "searches": [
    {
      "name": "Founding GTM",
      "track": "gtm",
      "keywords": ["founding account executive", "first sales hire"],
      "platforms": ["linkedin", "wellfound"],
      "filters": {
        "remote": true,
        "posted_within_days": 2,
        "easy_apply_only": false
      },
      "exclude_keywords": ["BDR", "SDR", "staffing", "insurance"]
    }
  ]
}

settings.json

{
  "max_applications_per_run": 50,
  "max_retries": 2,
  "enabled_apply_types": ["easy_apply"],
  "notifications": {
    "telegram_user_id": "YOUR_TELEGRAM_USER_ID",
    "bot_token": "YOUR_TELEGRAM_BOT_TOKEN"
  },
  "kernel": {
    "proxy_id": "YOUR_KERNEL_PROXY_ID",
    "profiles": {
      "linkedin": "LinkedIn-YourName",
      "wellfound": "WellFound-YourName"
    },
    "connection_ids": {
      "linkedin": "YOUR_LINKEDIN_CONNECTION_ID",
      "wellfound": "YOUR_WELLFOUND_CONNECTION_ID"
    }
  },
  "browser": {
    "provider": "kernel",
    "playwright_path": null
  }
}

answers.json

Flat array of pattern-answer pairs. Patterns are matched case-insensitively and support regex. First match wins.

[
  { "pattern": "quota attainment", "answer": "1.12" },
  { "pattern": "years.*enterprise", "answer": "5" },
  { "pattern": "1.*10.*scale", "answer": "9" }
]

---

Data files (auto-managed)

jobs_queue.json

[
  {
    "id": "li_4381658809",
    "platform": "linkedin",
    "track": "ae",
    "apply_type": "easy_apply",
    "title": "Senior Account Executive",
    "company": "Acme Corp",
    "url": "https://linkedin.com/jobs/view/4381658809/",
    "found_at": "2026-03-05T22:00:00Z",
    "status": "new",
    "status_updated_at": "2026-03-05T22:00:00Z",
    "retry_count": 0,
    "pending_question": null,
    "ai_suggested_answer": null,
    "telegram_message_id": null,
    "applied_at": null,
    "notes": null
  }
]

Job statuses

Status	Meaning	Next action
new	Found, waiting to apply	Applier picks it up
applied	Successfully submitted	Done
needs_answer	Blocked on unknown question	Telegram poller saves reply, flips to new
failed	Failed after max retries	Manual review
already_applied	Duplicate detected	Permanent skip
filtered	Below AI score threshold	Permanent skip
duplicate	Cross-track duplicate (lower score)	Permanent skip
skipped_honeypot	Honeypot question detected	Permanent skip
skipped_recruiter_only	LinkedIn recruiter-only	Permanent skip
skipped_external_unsupported	External ATS	Saved for future ATS support
skipped_easy_apply_unsupported	No Easy Apply button	Permanent skip
skipped_no_apply	No apply button found	Permanent skip
no_modal	Button found but modal didn't open	Retried
stuck	Modal progress stalled	Retried
incomplete	Modal didn't reach submit	Retried

applications_log.json

Append-only history of every application attempt with outcome, timestamps, and metadata.

telegram_offset.json

Stores the Telegram getUpdates offset to avoid reprocessing old messages.

filter_state.json

Persists batch IDs between filter submit and collect phases.

---

Self-learning answer flow

1. Applier encounters a required field with no matching answer
2. Claude generates a suggested answer using profile + resume context
3. Telegram message sent: question text, options (if select), AI suggestion
4. Job marked needs_answer with telegram_message_id stored
5. User replies on Telegram: their answer, or "ACCEPT" for the AI suggestion
6. Telegram poller (every minute) picks up the reply:
   • Matches via reply_to_message_id → job
   • Saves answer to answers.json as pattern match
   • Flips job status back to new
   • Sends confirmation reply
7. Next applier run: reloads answers, retries the job, fills the field automatically
8. All future jobs with the same question pattern are answered automatically

Safety net: applier also calls processTelegramReplies() at start of each run.

---

Retry logic

When an application fails due to a transient error (timeout, network issue, page didn't load):

1. retry_count is incremented on the job
2. Job status is reset to new so the next run picks it up
3. After max_retries (default 2) failures, job is marked failed permanently
4. Failed jobs are logged to applications_log.json with error details

Browser crash recovery: after an error, the applier checks if the page is still alive via page.evaluate(() => true). If dead, it creates a fresh browser session and continues with the remaining jobs.

---

File structure

claw-apply/
├── README.md                  Documentation
├── SKILL.md                   OpenClaw skill manifest
├── SPEC.md                    This file
├── claw.json                  OpenClaw skill metadata
├── package.json               npm manifest
├── job_searcher.mjs           Search agent
├── job_filter.mjs             AI filter + scoring agent
├── job_applier.mjs            Apply agent
├── telegram_poller.mjs        Telegram answer reply processor
├── setup.mjs                  Setup wizard
├── status.mjs                 Queue + run status report
├── lib/
│   ├── constants.mjs          Shared constants and defaults
│   ├── browser.mjs            Kernel/Playwright browser factory
│   ├── session.mjs            Kernel Managed Auth session refresh
│   ├── env.mjs                .env loader
│   ├── form_filler.mjs        Form filling with fuzzy select matching
│   ├── ai_answer.mjs          AI answer generation via Claude
│   ├── filter.mjs             AI job scoring via Anthropic Batch API
│   ├── keywords.mjs           AI-generated search keywords
│   ├── linkedin.mjs           LinkedIn search + job classification
│   ├── wellfound.mjs          Wellfound search + apply
│   ├── queue.mjs              Queue management with atomic writes
│   ├── lock.mjs               PID lockfile + graceful shutdown
│   ├── notify.mjs             Telegram Bot API (send, getUpdates, reply)
│   ├── telegram_answers.mjs   Telegram reply → answers.json processing
│   ├── search_progress.mjs    Per-platform search resume tracking
│   └── apply/
│       ├── index.mjs          Handler registry + status normalization
│       ├── easy_apply.mjs     LinkedIn Easy Apply (shadow DOM, multi-step)
│       ├── wellfound.mjs      Wellfound apply
│       ├── greenhouse.mjs     Greenhouse ATS (stub)
│       ├── lever.mjs          Lever ATS (stub)
│       ├── workday.mjs        Workday ATS (stub)
│       ├── ashby.mjs          Ashby ATS (stub)
│       └── jobvite.mjs        Jobvite ATS (stub)
├── config/
│   ├── *.example.json         Templates (committed)
│   └── *.json                 User config (gitignored)
└── data/                      Runtime data (gitignored, auto-managed)

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Roadmap

v1 (current)

• LinkedIn Easy Apply (multi-step modal, shadow DOM)
• Wellfound apply (infinite scroll)
• Kernel stealth browsers + residential proxy
• AI job filtering via Anthropic Batch API
• Self-learning answer bank with Telegram Q&A loop
• AI-suggested answers via Claude
• Telegram answer polling (instant save + applier safety net)
• Browser crash recovery
• Retry logic with configurable max retries
• Preview mode (--preview)
• Configurable application caps and retry limits
• Constants extracted — no magic numbers in code
• Atomic file writes for queue corruption prevention
• Cross-track deduplication after AI scoring

v2 (planned)

• External ATS support (Greenhouse, Lever, Workday, Ashby, Jobvite)
• Per-job cover letter generation via LLM
• Indeed support