finding-freelancers-by-skill-on-twitter

Finds freelancers and independent contractors to recruit using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find freelancers on Twitter for contract work, d…

API Dojo

@apidojo-io

Install

$ openclaw skills install @apidojo-io/finding-freelancers-by-skill-on-twitter

Finding Freelancers And Independent Contractors on Twitter

Discovers freelancers and independent contractors on Twitter via skill keywords, portfolio/project signals, and open-to-work indicators. Twitter surfaces professionals who actively discuss their craft — a strong passive candidate signal.

Prerequisites

  • APIFY_TOKEN environment variable set
  • Optional: Apify MCP server installed

Inputs

ParameterTypeRequiredDefaultNotes
startUrlsarrayOptional[]Twitter profile or tweet URLs
twitterHandlesarrayOptional[]Twitter usernames (without @)
twitterUserIdsarrayOptional[]Twitter user IDs
getFollowersbooleanOptionalfalseExtract follower lists
getFollowingbooleanOptionalfalseExtract following lists
getRetweetersbooleanOptionalfalseExtract retweeters of a tweet URL
includeUnavailableUsersbooleanOptionalfalseInclude unavailable/suspended users
maxItemsnumberOptionalUnlimitedMaximum users to return
customMapFunctionstringOptionalJavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Search for role-specific tweets
- [ ] Step 2: Collect unique handles
- [ ] Step 3: Enrich profiles
- [ ] Step 4: Score candidate fit
- [ ] Step 5: Deliver candidate list

Step 1: Search Queries

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~twitter-user-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
  "searchTerms": ["freelance [SKILL]", "[SKILL] for hire", "available for contract [SKILL]"],
  "maxItems": 300,
  "tweetLanguage": "en"
}

REST API fallback:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchTerms": ["freelance [SKILL]", "[SKILL] for hire", "available for contract [SKILL]"], "maxItems": 300}'

Collect unique author.username from results.

Step 2: Enrich Profiles

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input: {"usernames": ["[username1]", "[username2]", "..."]}

REST API fallback:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"usernames": ["handle1", "handle2"]}'

Step 3: Filter and Score

Skill confirmation: bio contains keywords: "freelance", "independent", "for hire", "consulting", "contract", "available", "hire me", "DM for work"

availability_signal = bio or recent tweets contain 'available', 'taking clients', 'open for projects', 'DM for rates'

Candidate score:

candidate_score = (skill_confirmed ? 1 : 0) * 0.35
                + (open_to_work_signal ? 1 : 0) * 0.30
                + (followerCount in 200..20000 ? 1 : 0.6) * 0.20
                + (tweeted_in_last_30_days ? 1 : 0) * 0.15

Activity: active (< 30 days) | passive (30–90 days) | dormant (> 90 days)

Step 4: Edge Cases

  • Company/brand accounts in results: Filter where followerCount > 50K AND bio contains no personal pronouns; these are likely brand accounts
  • < 20 candidates found: Broaden skill term; remove location or seniority filter; try adjacent skills
  • Bot detection: Flag followerCount / followingCount < 0.05 AND tweetsCount < 20 as potential bot
  • Location not matching: Bio location is free text — use fuzzy match; accept partial city/country names

Output Format

# Freelancers And Independent Contractors Candidates: [FREELANCE_SKILL]
Profiles found: [N] | Open-to-work: [N] | Active: [N] | Date: [DATE]

## Priority: Open-to-Work Candidates
| Name | @Handle | Specialty | Location | Followers | Last Active | Score |
|------|---------|----------|---------|-----------|------------|-------|

## Passive Candidates
| Name | @Handle | Specialty | Location | Followers | Score |
|------|---------|----------|---------|-----------|-------|

## Bio Highlights (Top 5)
1. @[handle]: "[bio excerpt]"

Troubleshooting

All results are agencies/companies not individuals: Add personal pronouns filter or search "I am a [role]", "I do [skill]". Role too generic returns too many results: Add location OR seniority qualifier. No open-to-work signals: Most candidates don't signal publicly — treat passive candidates as warm leads with personalized outreach referencing their recent content.

Top skills in this category

Using Superpowers

@zlc000190

Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions

6336k

novel-generator 是一个中文爽文小说生成技能。用户只需提供一句话方向(如"写个都市重生爽文"),AI 代理即可自动完善提示词、规划大纲、逐章创作并输出为独立 Markdown 文件。 核心特性: 智能提示词生成:从一句话方向自动补全世界观、人设、冲突、爽点设计 分章节创作:每章 2000-3000 字,层层递进,章章有爽点 记忆系统:通过 .learnings/ 记录角色、地点、情节、世界观,确保故事前后一致 情节图解:关键战斗、人物关系、势力分布自动生成 Mermaid 图 失败记录:穿帮、矛盾、崩塌等问题自动记录,持续优化 多题材支持:都市、修仙、玄幻、重生、系统流、末世、科幻、游戏 兼容 Claude Code、Cursor、OpenAI Codex、GitHub Copilot 等所有支持 Agent Skills 的工具。

@ityhg

根据用户提供的内容方向自动生成提示词并创作爽文小说。适用场景:(1) 用户提供小说方向/题材/关键词,(2) 需要生成章节连贯的长篇爽文,(3) 需要维护角色、地点、情节的连续性,(4) 需要为关键情节生成图解,(5) 需要记录生成失败场景以优化后续创作。支持都市、修仙、玄幻、重生、系统流等多种题材。Use wh...

7110k

simmer

@simmer

The prediction market interface for AI agents. Trade Polymarket and Kalshi through one API with self-custody wallets, safety rails, and smart context.

2310k

Smart Web Fetch

@leochens

智能网页抓取技能 - 替代内置 web_fetch,自动使用 Jina Reader / markdown.new / defuddle.md 清洗服务获取干净 Markdown。支持多级降级策略,大幅降低 Token 消耗。当 Agent 需要获取网页内容时使用本技能替代 web_fetch。

295.6k

AI Image Generation

@ivangdavila

Create AI images with GPT Image, Gemini Nano Banana, FLUX, Imagen, and top providers using prompt engineering, style control, and smart editing.

1212k