lead-generationoutreach

Job Posting Intent

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.

by Athina AI

Run in Gooseworks View skill in Github

Install

Terminal

npx gooseworks install --all

# then, in Claude Code, Cursor, or Codex:
/gooseworks use the job-posting-intent skill

About This 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

What's included

Allocated budget (headcount is expensive)

Acknowledged the problem exists

Started actively solving it

GTM agency: "Growth Marketing Manager", "SDR Manager", "RevOps Engineer", "GTM Engineer"

AI dev tools: "AI Engineer", "ML Ops Engineer", "Prompt Engineer", "LLM Engineer"

Render Model Comparison Grid

Render a 'model comparison grid' video from a config — a fal-style "same prompt, N contenders" showcase — a dark real-DOM stage where per beat a monospace prompt fades in centered, docks to a small top strip, then a labeled 2-4 panel grid (static images OR muted video clips, mixable per cell) staggers in and holds for comparison, plus a minimal end card — frame-stepped via Playwright (video cells are frame-seeked deterministically) and encoded with FFmpeg. Deterministic assembly, FREE (cell media comes from create-image-fal / create-video-fal, music from create-music-elevenlabs), text stays pixel-crisp. Use for the model-comparison-grid format.

Render Offer Ad

Render a punchy ~12s vertical (9:16) music-only direct-response OFFER ad as a 4-beat kinetic-typography film — HEADLINE slam → real PRODUCT drop → CLAIM/proof → CTA pill — from one config of copy slots, a real product photo, a brand palette, fonts, bpm, and beat split. DETERMINISTIC + FREE (a bundled Remotion project; springs + interpolate, no AI-gen for visuals). Backgrounds are engine gradient divs off the palette, props are inline SVG, the ONLY composited bitmap is the REAL product photo (objectFit:contain, never stretched), and ALL headline/claim/CTA/URL/wordmark text is typeset in the engine — never AI-rendered (the format's credibility guard). A driver binds the config to Remotion input props, renders the 9:16 master, and derives a 1:1 center-crop with ffmpeg. Two gating checks run before render (claim verbs must match the product's physical format; the claim beat needs an edge-entry mechanism prop). Use for the motion-graphics-offer-ad format.

Render Myth Vs Fact

Assemble a myth-vs-fact kinetic-typography explainer video ad (≈29.5s, 9:16) from N myth/fact pairs + hook / turn / punch copy + palette + a brand end-card PNG + a VO track — a hook, 3 red-strike MYTH cards that flip to teal-check FACT cards (per-line strikethrough that crosses EVERY wrapped line), a "what actually works" turn, an optional proof reveal, a punch line, and a static end card. DETERMINISTIC assembly with ZERO AI-gen visuals — HTML hyperframes rendered frame-exact via Playwright (`window.renderAt(t)`, animation a pure function of beat-local time), Whisper beat-snap to VO word onsets, concat at a uniform fps, karaoke `.ass` captions burned last (suppressed on the proof + end-card beats), and a VO + optional music mix (music −20 dB, `amix normalize=0`, tail fade). FREE (Python + Playwright + ffmpeg); the recipe supplies the copy / palette / end-card / VO and gates the paid VO / music / Whisper calls to their own capabilities. Use for the myth-vs-fact format.