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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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 requests
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.
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-only
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
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
| 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 scan아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.