Discovers software engineers on Twitter/X via tech stack keywords, open-to-work signals, and engineering community activity. Twitter surfaces engineers who are active in their tech community — a strong passive candidate signal.
Prerequisites
APIFY_TOKEN environment variable set- Optional: Apify MCP server installed
| Parameter | Type | Required | Default | Notes |
|---|
startUrls | array | Optional | [] | Twitter profile or tweet URLs |
twitterHandles | array | Optional | [] | Twitter usernames (without @) |
twitterUserIds | array | Optional | [] | Twitter user IDs |
getFollowers | boolean | Optional | false | Extract follower lists |
getFollowing | boolean | Optional | false | Extract following lists |
getRetweeters | boolean | Optional | false | Extract retweeters of a tweet URL |
includeUnavailableUsers | boolean | Optional | false | Include unavailable/suspended users |
maxItems | number | Optional | Unlimited | Maximum users to return |
customMapFunction | string | Optional | — | JavaScript function to transform each output object |
Workflow
Progress:
- [ ] Step 1: Search tweets by tech stack + role signals
- [ ] Step 2: Collect unique handles
- [ ] Step 3: Enrich profiles via twitter-user-scraper
- [ ] Step 4: Score candidate fit
- [ ] Step 5: Deliver candidate list
Step 1: Search Queries
Queries: ["[TECH_STACK] engineer", "[TECH_STACK] developer", "senior [TECH_STACK]",
"built with [TECH_STACK]", "[TECH_STACK] open to work", "[TECH_STACK] job search"]
For open-to-work pass: add "looking for [TECH_STACK] role", "[TECH_STACK] available"
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": ["[TECH_STACK] engineer", "senior [TECH_STACK] developer", "built with [TECH_STACK]"],
"maxItems": 300,
"tweetLanguage": "en"
}
REST API fallback:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"searchTerms": ["Python engineer", "senior Python developer", "built with Python"], "maxItems": 300}'
Collect unique author.username from results.
Step 2: Enrich Profiles
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input: {"usernames": ["[username1]", "[username2]", "..."]}
REST API fallback:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"usernames": ["handle1", "handle2"]}'
Step 3: Score Candidate Quality
Tech stack confirmation: bio or recent tweets mention the target tech stack → stack_confirmed = true
Role level proxy from bio:
- "senior", "staff", "principal", "lead", "CTO", "VP Eng" → senior+
- "mid", "3+ years", "5 years" → mid
- "junior", "new grad", "bootcamp" → junior
Open-to-work score:
candidate_score = (stack_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
Active = tweeted in last 30 days; Passive = 30–90 days; Dormant = > 90 days
Step 4: Edge Cases
- Results dominated by developer tools companies: Filter out accounts where
followerCount > 50K and bio mentions company/brand — these are likely dev tool marketing accounts
- Location filtering: Twitter bio location is free text — use
contains match; filter out profiles with ambiguous or non-geographic location entries
- < 20 results for niche stack: Broaden to language family (e.g. "Rust" → "systems programming") or remove role level filter
- Bot accounts: Flag profiles where
follower/following ratio < 0.05 AND tweetsCount < 10 as likely bots
# Software Engineer Candidates: [TECH_STACK]
Profiles found: [N] | Open-to-work: [N] | Senior: [N] | Mid: [N] | Active: [N] | Date: [DATE]
## Priority: Open-to-Work Candidates
| Name | @Handle | Role Level | Location | Stack Confirmed | Followers | Last Active | Score |
|------|---------|-----------|---------|----------------|-----------|------------|-------|
| [name] | @[handle] | Senior | [city] | ✓ | [N] | [X days ago] | [0.XX] |
## Passive Candidates (Not Actively Searching)
| Name | @Handle | Role Level | Location | Stack | Followers | Score |
|------|---------|-----------|---------|-------|-----------|-------|
## Bio Highlights
Top 5 candidates — summarized bios:
1. @[handle]: "[bio excerpt]" — [tech signals]
Troubleshooting
Results are all companies not individuals: Add "-company -official -team -agency" as negative search terms, or filter bio for first-person pronouns.
Tech stack too common returns too many results: Add a second filter — location OR seniority level — to reduce to a manageable size.
Few open-to-work signals: Most passive candidates don't signal openly; focus outreach on the passive tier with personalized messages referencing their recent tweets.