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.
npx gooseworks install --claude # Then in your agent: /gooseworks <prompt> --skill job-posting-intent
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.
When a company posts a job, they've:
If your product helps solve that problem faster, cheaper, or better than a hire alone, the timing is perfect.
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
# Get your token at https://console.apify.com/account/integrations
export APIFY_API_TOKEN="apify_api_YOUR_TOKEN_HERE"pip3 install requestsGoogle 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.
Think about it this way: "If a company is hiring for [role], it means they're investing in [problem area you solve]."
Examples:
python3 scripts/search_jobs.py \
--titles "GTM Engineer,SDR Manager,Head of Demand Gen" \
--locations "United States" \
--max-per-title 25 \
--estimate-onlyThe 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 --jsonRequired:
--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| Column | Description |
|---|---|
| Signal | HIGH / MEDIUM / LOW based on # postings + seniority |
| Company | Company name |
| Employees | Employee count |
| Industry | Company industry |
| Website | Company website |
| 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 |
When using this skill as an agent, the typical flow is:
--estimate-only and confirms cost with userExample 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:
The agent SHOULD:
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.
--posted-limit month--relevance-keywords to filter by title keywords--no-sheet --json --output results.json to save results without a sheetscripts/create_sheet_mcp.py--max-per-title (25 is usually enough)--posted-limit 24h for a quick daily scanDiagnose Meta Ads campaign performance using Meta's actual system mechanics — Breakdown Effect, Learning Phase, Auction Overlap, Pacing, and Creative Fatigue — and produce structured, testable recommendations that avoid judging segments by average CPA instead of marginal efficiency.
Pre-flight policy check for Meta ads. Takes ad copy plus advertiser context, resolves and fetches the relevant Meta transparency-center policy pages at runtime, and returns a Pass / Fix Required / Block verdict with cited findings and rewrites.
For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.