/job-posting-intent

name: job-posting-intent
version: 1.1.0
description: >
  Detect buying intent from job postings. When a company posts a job in your problem area,
  they've allocated budget and are actively thinking about the problem. This skill finds those
  companies, qualifies them, extracts personalization context, and outputs everything to a
  Google Sheet. Does NOT do outreach — just delivers qualified leads with reasoning.
tags: [lead-generation, outreach]

Job Posting Intent Detection

Find companies that are hiring for roles related to the problem you solve. A job posting is a **budget signal** — the company has allocated money to solve a problem your product addresses.

Results are automatically exported to a **Google Sheet** with signal strength, decision-maker suggestions, outreach angles, and personalization context.

Why This Works

When a company posts a job, they've:

• Allocated budget (headcount is expensive)
• Acknowledged the problem exists
• Started actively solving it

If your product helps solve that problem faster, cheaper, or better than a hire alone, the timing is perfect.

Cost

**Apify Actor:** `harvestapi/linkedin-job-search` (pay-per-event)

| Component | Cost | |-----------|------| | Actor start (per run) | $0.001 | | Per job result | $0.001 | | Apify platform fee | +20% |

**Typical run costs:** | Scenario | Titles | Jobs/title | Runs | Est. Cost | |----------|--------|------------|------|-----------| | Quick scan | 3 | 25 | 3 | ~$0.09 | | Standard | 5 | 25 | 5 | ~$0.16 | | Deep search | 5 | 100 | 5 | ~$0.60 | | Multi-location | 5×3 | 25 | 15 | ~$0.47 |

Google Sheet creation is free (uses Rube/Composio integration).

Always run `--estimate-only` first to see the Apify cost before executing.

Track usage: https://console.apify.com/billing

Setup

1. Apify API Token

# Get your token at https://console.apify.com/account/integrations
export APIFY_API_TOKEN="apify_api_YOUR_TOKEN_HERE"

2. Install dependencies

pip3 install requests

3. Rube/Composio (for Google Sheets)

Google Sheet creation uses Rube MCP with Composio. The token is preconfigured. If it stops working, update the `RUBE_TOKEN` env var or the default in `search_jobs.py`.

Usage

Step 1: Define your ICP and target titles

Think about it this way: **"If a company is hiring for [role], it means they're investing in [problem area you solve]."**

Examples:

• GTM agency: "Growth Marketing Manager", "SDR Manager", "RevOps Engineer", "GTM Engineer"
• AI dev tools: "AI Engineer", "ML Ops Engineer", "Prompt Engineer", "LLM Engineer"
• Sales automation: "SDR", "BDR Manager", "Sales Ops", "Revenue Operations"

Step 2: Estimate cost

python3 scripts/search_jobs.py \
  --titles "GTM Engineer,SDR Manager,Head of Demand Gen" \
  --locations "United States" \
  --max-per-title 25 \
  --estimate-only

Step 3: Run the search

The script searches LinkedIn Jobs, groups results by company, qualifies leads, and creates a Google Sheet automatically.

# Standard search (creates Google Sheet)
python3 scripts/search_jobs.py \
  --titles "GTM Engineer,SDR Manager,RevOps Engineer" \
  --locations "United States" \
  --max-per-title 25

# Deep search with custom sheet name
python3 scripts/search_jobs.py \
  --titles "AI Engineer,ML Ops Engineer,Prompt Engineer" \
  --locations "United States" \
  --max-per-title 50 \
  --sheet-name "AI Hiring Signals - Feb 2026"

# Filter results to only relevant titles (LinkedIn search is fuzzy)
python3 scripts/search_jobs.py \
  --titles "GTM Engineer,Growth Marketing Manager,SDR Manager" \
  --locations "United States" \
  --relevance-keywords "gtm,growth,sdr,marketing,demand gen,revops"

# Also save raw JSON alongside the sheet
python3 scripts/search_jobs.py \
  --titles "GTM Engineer,SDR Manager" \
  --locations "United States" \
  --output results.json

# Skip Google Sheet, console + JSON only
python3 scripts/search_jobs.py \
  --titles "GTM Engineer" \
  --no-sheet --json

What the Script Does

1. **Searches** LinkedIn Jobs for each title/location combination via Apify 2. **Groups** results by company (deduplicates) 3. **Computes signal strength** based on number of relevant postings + seniority 4. **Extracts personalization context** from job descriptions (tech stack, growth signals, pain points) 5. **Suggests decision-maker title** (one level above the hired role) 6. **Suggests outreach angle** (accelerate / replace / multiply the hire) 7. **Creates a Google Sheet** with all qualified leads 8. **Prints a console summary** of all companies found

Options Reference

Required:
  --titles              Comma-separated job titles to search

Optional:
  --locations           Comma-separated locations (default: no filter)
  --max-per-title       Max jobs per title per location (default: 25)
  --posted-limit        Recency: 1h, 24h, week, month (default: week)
  --output, -o          Also save raw JSON to this file path
  --json                Print JSON output to console
  --estimate-only       Show cost estimate without running
  --no-sheet            Skip Google Sheet creation
  --sheet-name          Custom Google Sheet title (default: "Job Posting Intent Signals - {date}")
  --relevance-keywords  Comma-separated keywords to filter truly relevant postings

Google Sheet Columns

| Column | Description | |--------|-------------| | Signal | HIGH / MEDIUM / LOW based on # postings + seniority | | Company | Company name | | Employees | Employee count | | Industry | Company industry | | Website | Company website | | LinkedIn | Company LinkedIn URL | | # Postings | Number of relevant job postings found | | Job Titles | The actual job titles posted | | Job URL | Link to the primary job posting | | Location | Job location(s) | | Decision Maker | Suggested title of person to contact | | Outreach Angle | Accelerate / Replace / Multiply the hire | | Tech Stack | Technologies mentioned in job descriptions | |