MCPInstall

jobsearch-telegram

Poll Telegram for job search messages — apply to jobs, search for roles, check status, all via chat

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/proficientlyjobs/proficiently-claude-skills --skill jobsearch-telegram
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads jobsearch-telegram automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Job Search Telegram Polling

Poll Telegram for incoming messages and route them to the appropriate Proficiently skill. Runs headlessly via /loop 1m /proficiently:jobsearch-telegram.

First-Time Setup

Before this skill can run, the user must create a Telegram bot and configure it. If DATA_DIR/telegram-config.md does not exist, walk the user through setup:

1. Create a Telegram Bot

Tell the user:

Let's set up your Telegram bot.

  1. Open Telegram and search for @BotFather
  2. Send /newbot
  3. Choose a name (e.g., "My Job Search Assistant")
  4. Choose a username (must end in bot, e.g., my_jobsearch_bot)
  5. BotFather will give you a bot token — copy it and paste it here

Then send your bot a message (anything) so I can find your chat ID.

2. Get the Chat ID

Once the user provides the bot token, fetch their chat ID:

curl -s "https://api.telegram.org/bot{TOKEN}/getUpdates"

Extract message.chat.id from the first result. If no results, remind the user to send a message to the bot first, then retry.

3. Save Config

Write DATA_DIR/telegram-config.md:

# Telegram Config

- Bot token: {TOKEN}
- Chat ID: {CHAT_ID}
- Bot username: @{USERNAME}

4. Verify

Send a test message:

curl -s -X POST "https://api.telegram.org/bot{TOKEN}/sendMessage" \
  -H "Content-Type: application/json" \
  -d '{"chat_id": "{CHAT_ID}", "text": "👋 Job search bot connected! Send me a job URL to apply, or say \"search\" to find jobs."}'

If successful, tell the user setup is complete and they can start the loop with /loop 1m /proficiently:jobsearch-telegram.


Config & State Files

Resolve the data directory using shared/references/data-directory.md.

Config — DATA_DIR/telegram-config.md (created during setup, contains bot token + chat ID). Never commit this file to git. Read this first on every poll cycle to get credentials.

State — DATA_DIR/telegram-state.md (tracks polling position). Create if missing:

# Telegram State

## Polling
- last_update_id: 0

## Pending Confirmations
<!-- Format: [msg_id: X] type/stage — description — waiting since DATE
     For apply confirmations, also store: job_url, form_url, field_mapping (JSON) -->

(none)

## Recent Actions
<!-- Last 20 actions taken -->

Workflow

Step 1: Load Context

  1. Read DATA_DIR/telegram-config.md — if missing, run First-Time Setup above and stop
  2. Read DATA_DIR/telegram-state.md — if missing, create from template above
  3. Read these if they exist: DATA_DIR/job-history.md, DATA_DIR/application-data.md, DATA_DIR/preferences.md

Step 2: Poll for Messages

curl -s "https://api.telegram.org/bot{TOKEN}/getUpdates?offset={LAST_UPDATE_ID+1}&timeout=5"

If no new messages → exit silently. Do not log, do not send anything.

Step 3: Classify Each Message

Parse each message and classify:

Message TypeDetectionRoute
Job URLContains greenhouse.io, lever.co, myworkdayjobs.com, ashbyhq.com, or other job board URLStep 4a: Apply
"apply last" / "apply"Text matches apply (with optional last/current)Step 4a: Apply
"search for ..."Text starts with search, find, look forStep 4b: Search
"tailor resume for ..."Text mentions tailor/resume + contextStep 4c: Tailor
"status" / "what's open"Text asks about application statusStep 4d: Status
"help"Text is exactly help or ?Step 4e: Help
Confirmation replyThreaded reply to a pending confirmation message, OR standalone confirm word (yes/y/go/no/cancel) when pending confirmations existStep 5: Confirm
Plain textAnything elseStep 6: Note

Step 4a: Handle Job URL / Apply Request

  1. Extract the URL or resolve "last"/"current"

  2. Check if a job folder already exists in DATA_DIR/jobs/ for this URL

  3. Send acknowledgment to Telegram:

    🎯 Got it — applying to [URL or "most recent job"].
    I'll scan the form, tailor your resume, and propose answers. Stand by...
    
  4. Execute the apply workflow from skills/apply/SKILL.md:

    • Follow Steps 0-6 (prerequisites → navigate → scout → generate materials → scan fields → propose answers)
    • Instead of using AskUserQuestion for approval, send the Step 6 proposal summary to Telegram and add to Pending Confirmations
    • Store in the pending confirmation: job_url, form_url (the direct ATS form URL navigated to), and field_mapping (the full approved field→value JSON)
    • Wait for user confirmation via Telegram (will arrive as a reply in a future poll cycle)
  5. When field-approval confirmation arrives (Step 5), re-navigate to form_url, fill all fields, then send a second confirmation (submit approval) with a screenshot description and ask: "Everything looks good — submit?"

    • Store this as a new pending confirmation with stage: "submit-approval"
  6. When submit-approval arrives, click Submit, then log the application (Step 9 of the apply skill).

Sending the proposal: Use the send message helper (Step 8) with the full field summary. Keep it under 4000 chars. If longer, split into: (1) auto-fill fields, (2) proposed answers, (3) needs input.

Two-phase confirmation flow:

  • Phase 1 (stage: field-approval): User approves the field→value mapping
  • Phase 2 (stage: submit-approval): User approves the final form before clicking Submit
  • Never skip phase 2 — submitting a job application is irreversible

Step 4b: Handle Search Request

  1. Extract search keywords from the message
  2. Send acknowledgment: 🔍 Searching for: [keywords]...
  3. Execute the job-search workflow from skills/job-search/SKILL.md
  4. Send results summary to Telegram:
    🔍 Found X matches for "[keywords]":
    
    1. [Role] at [Company] — [fit score]
       [URL]
    2. ...
    
    Reply with a number to apply, or "apply 1" / "apply 3" etc.
    
  5. Add to Pending Confirmations with the job list so replies can be matched

Step 4c: Handle Tailor Request

  1. Extract job reference (URL, "last", or job name)
  2. Send acknowledgment: 📝 Tailoring resume for [job]...
  3. Execute the tailor-resume workflow from skills/tailor-resume/SKILL.md
  4. Send result to Telegram with key changes made
  5. Note the file path where the tailored resume was saved

Step 4d: Handle Status Query

Compile from DATA_DIR/job-history.md and DATA_DIR/jobs/*/applied.md:

📋 Job Search Status

Applied (X):
- [Role] at [Company] — [date] — [status]
- ...

Saved but not applied (Y):
- [Role] at [Company] — [date saved]
- ...

Pending your confirmation:
- [any pending apply proposals]

Step 4e: Handle Help Request

Send:

👋 Here's what you can do:

<b>Apply</b>
• Send a job URL → I'll apply for you
• "apply last" → continue with the most recent job

<b>Search</b>
• "search [keywords]" → find matching jobs
• "find AI product jobs" → same thing

<b>Resume</b>
• "tailor resume for [job URL or name]"

<b>Status</b>
• "status" → see all applications and what's pending

<b>Other</b>
• "help" → this message
• Any other text is saved as a note

Step 5: Handle Confirmation Reply

A confirmation reply is either:

  • A threaded reply (Telegram's native reply feature): match via reply_to_message.message_id to a pending confirmation
  • A standalone message containing only a confirm/reject word (yes, y, go, send it, 👍, no, skip, cancel, ❌) when pending confirmations exist

Disambiguation when standalone:

  • If exactly one pending confirmation exists → apply it to that confirmation
  • If multiple pending confirmations exist → respond with a numbered list of what's pending and ask which one they mean:
    You have X things waiting. Which one?
    1. [description of pending 1]
    2. [description of pending 2]
    Reply with a number.
    

Processing:

  1. Look up the pending confirmation in telegram-state.md
  2. Parse the user's reply:
    • "yes" / "y" / "go" / "send it" / 👍 → approve
    • "no" / "skip" / "cancel" / ❌ → reject
    • "yes but [changes]" → approve with modifications
    • A number (e.g., "2") → select that option from a list
  3. Execute the approved action:
    • stage: field-approval → re-navigate to form_url, fill fields using field_mapping, then send submit-approval prompt
    • stage: submit-approval → click Submit, log application
    • Search result selection → start apply workflow for selected job
  4. Send confirmation of what was done
  5. Remove from Pending Confirmations, add to Recent Actions

Step 6: Handle Plain Text Note

  1. Log to DATA_DIR/telegram-inbox.md with timestamp
  2. If it looks like a company name or job title, suggest: "Want me to search for [text] jobs?"
  3. Otherwise confirm: "Noted 👍"

Step 7: Update State

After processing all messages:

  1. Update last_update_id in DATA_DIR/telegram-state.md
  2. Update Pending Confirmations (add new, remove resolved). For each apply confirmation, include:
    [msg_id: X] apply/field-approval — [Role] at [Company] — waiting since DATE
    job_url: https://...
    form_url: https://...
    field_mapping: {"First Name": "...", "Email": "...", ...}
    
  3. Update Recent Actions (keep last 20, newest first)

Step 8: Sending Messages

Read credentials from DATA_DIR/telegram-config.md, then send via curl:

curl -s -X POST "https://api.telegram.org/bot{TOKEN}/sendMessage" \
  -H "Content-Type: application/json" \
  -d '{"chat_id": "CHAT_ID", "text": "MESSAGE", "parse_mode": "HTML"}'

For replies to specific messages, add "reply_to_message_id": MSG_ID.

Formatting rules:

  • Use HTML: <b>bold</b>, <i>italic</i>, <code>code</code>
  • Keep messages under 4000 chars (Telegram limit is 4096)
  • Be concise — user reads on mobile
  • Use line breaks for readability, not walls of text

To capture the sent message's message_id (needed for tracking confirmations):

# Parse from response JSON
jq -r '.result.message_id'

Key Rules

  • HEADLESS. Never use AskUserQuestion. All interaction happens through Telegram.
  • Acknowledge fast. Send a quick reply before starting long operations (apply, search, tailor).
  • Suggest before acting. For apply: always send the proposal and wait for Telegram confirmation before filling fields.
  • Never skip submit confirmation. Submitting an application is irreversible — always require explicit approval.
  • Exit silently if no new messages. Don't log empty polls.
  • Concise messages. Mobile-first. No walls of text.
  • Track everything in telegram-state.md so confirmations persist across poll cycles.
  • No secrets in git. Credentials live only in DATA_DIR/telegram-config.md.
  • Log costs every cycle. See Cost Tracking below.

Cost Tracking

After every poll cycle that does actual work (not silent exits), you MUST:

  1. Count your token usage for this cycle. At the end of your response, estimate:

    • Input tokens: approximate total from all files read + tool results received
    • Output tokens: approximate total from all text + tool calls you generated
    • Use these rates: input = $3/M tokens, output = $15/M tokens (cache reads = $1.875/M)
  2. Append to DATA_DIR/telegram-cost-log.csv (create with header if missing):

    timestamp,action,input_tokens,output_tokens,estimated_cost_usd
    

    Example row:

    2026-03-11T14:30:00Z,apply-proposal,12000,3500,$0.09
    
  3. Include a cost footer in every Telegram reply:

    ---
    📊 ~12K in / ~3.5K out · ~$0.09
    
  4. On "status" queries, include a cost summary section:

    💰 Cost this session: $X.XX (Y interactions)
    💰 Cost all-time: $X.XX (Z interactions)
    

    Compute from the CSV log.

Permissions Required

Add to ~/.claude/settings.json:

{
  "permissions": {
    "allow": [
      "Bash(curl:*)",
      "Bash(jq:*)",
      "Read(~/.proficiently/**)",
      "Write(~/.proficiently/**)",
      "Edit(~/.proficiently/**)",
      "Read(~/.claude/skills/**)",
      "mcp__claude-in-chrome__*"
    ]
  }
}

FAQ

What does the jobsearch-telegram skill do?

Poll Telegram for job search messages — apply to jobs, search for roles, check status, all via chat

How do I install jobsearch-telegram?

Run npx skills add https://github.com/proficientlyjobs/proficiently-claude-skills --skill jobsearch-telegram in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/jobsearch-telegram/ (personal) or .claude/skills/jobsearch-telegram/ (project).

Which AI agents can use jobsearch-telegram?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is jobsearch-telegram safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains jobsearch-telegram?

It is published by proficientlyjobs in proficientlyjobs/proficiently-claude-skills. It has 84 recorded installs.

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