demo

Overview

The Freelancer Jobs Scraper is a custom web application designed to automate the collection of job postings from Freelancer.com. It allows users to search for freelance projects based on keywords and filter criteria, and instantly view, download, and analyze results—all in one place.

Features

• Keyword Search
  Users can input multiple keywords to search for relevant projects in real-time.

• Advanced Filters
  Customize your search by:

  • Budget range (minBudget and maxBudget)
  • Project type (fixed or hourly)
  • Country of project origin

• Real-Time Display
  Scraped jobs are displayed immediately on the page, showing:

  • Job title and description
  • Required skills
  • Budget and currency
  • Project type
  • Location
  • Direct link to the project

• CSV & JSON Export
  Download results for offline analysis in CSV or JSON formats.

• Pagination Support
  Browse large sets of results efficiently with built-in pagination controls.

• Logging & Status Feedback
  Shows real-time scraping progress and total number of jobs found.

Technical Details

• Stack: Next.js (app router), TypeScript, Vercel serverless functions

• Data Source: Freelancer.com API

• Libraries:

  • axios for HTTP requests
  • json2csv for CSV export
  • React & TailwindCSS for frontend UI

• Vercel Compatibility
  Fully compatible with Vercel deployments, allowing serverless scraping without the need for external servers.

Use Case

Perfect for freelancers, project managers, or data analysts who want to:

• Quickly discover new projects matching their skills
• Analyze market demand for specific services
• Automate repetitive job searches

Here’s a summary of “Prompt Engineering Cheat Sheet for GPT-5” from freeCodeCamp, covering the key ideas and patterns.

Click here to try

More prompts from github

What It Covers

• An overview of GPT-5 capabilities: longer context, better reasoning, handling more instructions, tools & agentic tasks like calling APIs or searching files.
• Why prompt engineering matters: clearer prompts lead to more relevant, accurate, efficient code; fewer bugs & less debugging.

Prompting Patterns to Know

Here are the main patterns the article recommends, with what they do:

Common Pitfalls to Avoid

• Being vague or ambiguous in a prompt — e.g. “write some code” without language, style, or output format.
• Overloading a prompt with multiple unrelated tasks at once. Better to break tasks up.
• Failing to iterate: first output may not be perfect; refining, giving feedback, using follow-ups are important.

Bottom-line Takeaways

• GPT-5 is powerful, but still works best when you structure your prompt carefully.
• Using the right pattern (persona, few-shot, structured output, etc.) can make a big difference in quality.
• Clarity, constraints, and context are your friends.
• Build prompts like software: test, refine, version.

If you want, I can send you a shorter cheat-sheet you can keep at hand when writing prompts, or even generate “prompt templates” based on these patterns.

https://www.freelancer.com/projects/data-scraping/ALPA-Agent-Data-Scrape/details

https://www.freelancer.com/projects/data-scraping/ALPA-Agent-Data-Scrape/proposals

github (private)

🎉 SCRAPING COMPLETED SUCCESSFULLY!

🎯 Total agents found: 1179 📍 States processed: 8/8 📄 Total pages processed: 131 ⏰ Scraped at: 9/20/2025, 1:41:19 PM

📈 Agents by State:

Australian Capital Territory | 0 |

New South Wales | 270 | ████████████████████████████████████████

Northern Territory | 0 |

Queensland | 270 | ████████████████████████████████████████

South Australia | 270 | ████████████████████████████████████████

Tasmania | 0 |

Victoria | 135 | ████████████████████

Western Australia | 234 | ██████████████████████████████████

📁 Generated Files:

📊 alpa-agents-complete.csv (1179 records) ← MAIN FILE 📋 alpa-agents-complete.json (1179 records) 📈 alpa-scrape-summary.json

📍 alpa-agents-nsw.csv (270 agents)

📍 alpa-agents-qld.csv (270 agents)

📍 alpa-agents-sa.csv (270 agents)

📍 alpa-agents-vic.csv (135 agents)

📍 alpa-agents-wa.csv (234 agents)

🚀 Ready for Analysis!

Project Details $10.00 – 30.00 AUD

Bidding ends in 5 days, 23 hours I have no time right now to build a crawler, yet I urgently need a clean CSV that captures every agency listed on https://alpa.net.au/membership/find-agent. Please visit each entry, open the “Member Details” page, and pull every piece of information shown. At minimum the CSV must include:

• full contact information (address, phone, email, website) • the agency description text • all membership-related details

A one-time scrape is all that’s required; no scheduling or future runs are needed. Feel free to use ParseHub, Python + BeautifulSoup, Selenium, or any other approach you’re comfortable with—speed and accuracy matter more to me than the specific toolset.

Deliverable: a single, well-structured CSV, UTF-8 encoded, ready for immediate use. Skills Required Python Web Scraping Django Data Mining Data Scraping BeautifulSoup Selenium Data Collection Project ID: 39804324

https://babyshare.vercel.app/nlp

freelancer.com

My Proposal

Hi! I am a professor who will retire in the near future. I can assist you on your excellent requirements.

Project Details

$30.00 – 250.00 USD Bidding ends in 6 days, 8 hours

I need assistance with a university assignment focused on developing a business idea. The assignment is part of a business course and requires a comprehensive approach to conceptualizing and detailing a new business idea.

• Key Requirements:

1)Identify a business idea 2)Identify the competition 3)Identify approximately the needed capital 4)Identify the legal form 5)Identify the available funding options

it can't be something basic like a coffee shop or restaurant should be something innovative

• Skills Required

Business Plans Research Writing Business Analysis Market Research Entrepreneurship Business Writing Business Strategy Business Consulting Business Plan Writing Business Development Project ID: 39781310

fastapi-ecommerce-scraper: github.com

freelancer.com

Project Details $2.00 – 8.00 USD per hour Bidding ends in 6 days, 22 hours

Scrape & clean data from sites (Python, Scrapy/Selenium). Create Excel reports with price comparisons and charts Skills Required Python Data Processing Web Scraping Web Development Automation Project ID: 39266799

NMPA database scraper

fastapi-nmpa

Initial results

序号,批准文号,产品名称,生产单位,药品本位码,详情 1,国药准字H20065611,维生素B1,江苏巨邦制药有限公司,86901495001148,详情 2,国药准字H20061133,注射用甲硫氨酸维B1,昆明龙津药业股份有限公司,86905595000117,详情 3,国药准字H20060965,注射用甲硫氨酸维B1,西南药业股份有限公司,86900978004300,详情 4,国药准字H20060520,注射用甲硫氨酸维B1,瑞阳制药股份有限公司,86904152003097,详情 5,国药准字H20064155,甲硫氨酸维B1注射液,瑞阳制药股份有限公司,86904152002250,详情 6,国药准字H37023585,复方肝浸膏片,武陟维尔康生化制药有限公司,86903984000175,详情 7,国药准字H21021560,鱼肝油,大连水产药业有限公司,86901131000252,详情 8,国药准字Z20063908,更年灵胶囊,,86903560000773,详情 9,国药准字H14023436,甲硝唑维B6片,山西太原药业有限公司,86902944000828,详情 10,国药准字Z20063572,更年灵胶囊,陕西汉唐制药有限公司,86902423000110,详情

freelancer.com

nfl-1:github.com

I am excited to submit my proposal for building your NFL Handicapping Pipeline. I specialize in Python data pipelines and predictive modeling, and I have already prototyped an initial pipeline, which you can preview here: https://babyshare.vercel.app/case/6. The prototype demonstrates automated data ingestion, feature computation, and basic projections for upcoming games, showing the feasibility and speed of delivering actionable insights.

Initial Results

2025-09-07 11:00:01,844 | INFO | db | ✅ Created MySQL connection pool (size=10) Wrote: ./out/board.json ./out/board.md ./out/board_2025_W1.json ./out/board_2025_W1.md

----- 📊 NFL Board Preview (Season=2025, Week=1) -----

NFL Board

Generated: 2025-09-07T11:00:01.901014

• 2025_W1_ATL_TB | Synthetic | team_total_home | ATL | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_BUF_BAL | Synthetic | team_total_home | BUF | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_CHI_MIN | Synthetic | team_total_home | CHI | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_CLE_CIN | Synthetic | team_total_home | CLE | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_DEN_TEN | Synthetic | team_total_home | DEN | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_GB_DET | Synthetic | team_total_home | GB | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_IND_MIA | Synthetic | team_total_home | IND | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_JAX_CAR | Synthetic | team_total_home | JAX | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_LA_HOU | Synthetic | team_total_home | LA | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_LAC_KC | Synthetic | team_total_home | LAC | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_NE_LV | Synthetic | team_total_home | NE | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_NO_ARI | Synthetic | team_total_home | NO | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_NYJ_PIT | Synthetic | team_total_home | NYJ | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_PHI_DAL | Synthetic | team_total_home | PHI | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_SEA_SF | Synthetic | team_total_home | SEA | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• 2025_W1_WAS_NYG | Synthetic | team_total_home | WAS | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• GAME001 | Synthetic | team_total_home | NE | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0
• GAME002 | Synthetic | team_total_home | GB | line 22.5 @ -110.0 | proj=24.00 | edge=1.6% | fair=85 | kelly=0.034 | conf=0

Project Details

$250.00 – 750.00 USD

Bidding ends in 3 days, 12 hours Build an NFL Handicapping Pipeline (Python)

Goal: A Python system that ingests free/low-cost data, projects games/props, finds edges vs books, and outputs a clean “board” (JSON/Markdown) for posting in Whop/Discord.

Stack & Data

Python 3.10+, pandas, requests, scikit-learn, APScheduler, mysql-connector-python.

DB: MySQL.

Sources (free where possible):

Games/teams/players: nfl_data_py.

Odds & movement: The Odds API (free tier OK).

Public consensus % (proxy for bets/money): VegasInsider/Covers (scrape).

Weather (hourly, wind at stadium): National Weather Service API.

What to Build (modules)

Slate ingest

Pull weekly schedule (season/week, home/away, kickoff time).

Compute flags: divisional, rest days, travel/time-zone.

Odds snapshots

Every 10–15 min: store book lines (spread/total/moneyline; later props).

Track openers vs current per game/book; compute movement deltas.

Consensus scrape

Pull % bets/% money proxies for sides/totals; timestamp and store.

Weather

Stadium lat/lon → NWS hourly forecast around kickoff (wind/gust/precip).

Team & player features

From nfl_data_py: rolling EPA (off/def), success rate, neutral pace.

Player usage baselines (target share, rush share, red-zone).

Projections & edges

Convert spread/total → implied team totals; blend with EPA/pace + weather.

Simple ridge/elastic-net models for core props (QB pass yds, WR rec, RB rush att).

Compare projection vs line → edge %, fair price, Kelly fraction, confidence (0–100).

Parlay helper (rule-based)

Generate 1–2 correlated legs per game script (e.g., “home leads early” → QB over + WR rec over + opp QB att under).

Skills Required Python Software Architecture MySQL PostgreSQL Data Science JSON Data Analysis API Integration Project ID: 39759421

• freelancer.com

• scrape-schools: github.com

• Hi! I have worked on your requirements and get the initial results as follows. Waiting for your response.

school_name,phone,email,facebook,url Steps K-2 Nichols,N/A,N/A,https://www.facebook.com/greatschools,https://www.greatschools.org/connecticut/stratford/3972-Steps-K-2-Nichols/ Cheyenne's Dc & Learning Center 2,N/A,N/A,https://www.facebook.com/greatschools,https://www.greatschools.org/connecticut/stratford/2844-Cheyennes-Dc--Learning-Center-2/ Helen K Reynolds School,N/A,N/A,https://www.facebook.com/greatschools,https://www.greatschools.org/connecticut/stratford/2855-Helen-K-Reynolds-School/ K-3 Aft School Enrich Pgrm,N/A,N/A,https://www.facebook.com/greatschools,https://www.greatschools.org/connecticut/stratford/3506-K-3-Aft-School-Enrich-Pgrm/ Franklin STEPS K-2,N/A,N/A,https://www.facebook.com/greatschools,https://www.greatschools.org/connecticut/stratford/3822-Franklin-STEPS-K-2/ High School STEPS 9-12,N/A,N/A,https://www.facebook.com/greatschools,https://www.greatschools.org/connecticut/stratford/3733-High-School-STEPS-9-12/ K'tanim Pre-School,N/A,N/A,https://www.facebook.com/greatschools,https://www.greatschools.org/connecticut/fairfield/3596-Ktanim-Pre-School/ Trumbull Loves Children Child Care Center 2,N/A,N/A,https://www.facebook.com/greatschools,https://www.greatschools.org/connecticut/trumbull/3350-Trumbull-Loves-Children-Child-Care-Center-2/

Project Details

$10.00 – 30.00 USD

I need a freelancer to scrape data from greatschools.org. The data should include:

• Email
• Phone
• Facebook

I need the data delivered in an Excel file. The task only needs to be done once, no updates required. Ideal skills and experience include:

• Proficiency in web scraping tools
• Experience with data organization in Excel
• Attention to detail to ensure data accuracy Python Excel Web Scraping Pandas Project ID: 39741675