Job Posting Intent Agent Skill

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

• Searches LinkedIn Jobs for each title/location combination via Apify
• Groups results by company (deduplicates)
• Computes signal strength based on number of relevant postings + seniority
• Extracts personalization context from job descriptions (tech stack, growth signals, pain points)
• Suggests decision-maker title (one level above the hired role)
• Suggests outreach angle (accelerate / replace / multiply the hire)
• Creates a Google Sheet with all qualified leads
• 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 |

| Growth Signals | Growth indicators (first hire, scaling, series stage) |

| Pain Points | Pain indicators (automate, optimize, manual processes) |

| Description | Company description snippet |

AI Agent Integration

When using this skill as an agent, the typical flow is:

• User describes their product and the types of roles that signal intent
• Agent runs --estimate-only and confirms cost with user
• Agent runs the search (Google Sheet is created automatically)
• Agent shares the Google Sheet link with the user
• Agent provides a brief summary of top leads and why they're qualified

Example prompt:

> "Find companies hiring growth marketers and SDRs in the US this week. These are signals they need GTM help. We sell AI-powered GTM systems to Series A-C B2B SaaS companies with 20-200 employees."

The agent should NOT:

• Do any outreach
• Send any emails or messages
• Contact anyone

The agent SHOULD:

• Present cost estimate before running
• Run the search (sheet is created automatically)
• Share the Google Sheet link
• Provide a brief summary of the top leads with reasoning

Outreach Angle Templates

The script auto-assigns an angle based on job posting context:

"Accelerate while you hire" — Best when: posting is recent, role is junior/mid

> They're looking for someone to do X. Your product can deliver X outcomes while they ramp the hire.

"Replace the hire" — Best when: small company, "first hire" signals, building from scratch

> They want the output of a [role] but may not need a full-time person if they use your product.

"Multiply the hire" — Best when: company is clearly scaling, multiple related roles

> When their new hire starts, your product makes them 10x more effective from day one.

Troubleshooting

"No jobs found"

• Try broader titles (e.g., "marketing" instead of "demand generation specialist")
• Extend the time window: --posted-limit month
• Remove location filter to search globally

"Too many irrelevant results"

• Use --relevance-keywords to filter by title keywords
• LinkedIn's search is fuzzy — the grouping and qualification step helps filter

"Google Sheet creation failed"

• Check that Rube MCP is accessible (the token may have expired)
• Use --no-sheet --json --output results.json to save results without a sheet
• You can create the sheet later with scripts/create_sheet_mcp.py

High cost estimate

• Reduce --max-per-title (25 is usually enough)
• Search fewer titles
• Use --posted-limit 24h for a quick daily scan

Links

• Apify LinkedIn Job Search Actor
• Apify API Token
• Apify Billing Dashboard