Finding Software Engineers on Twitter

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

Inputs

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):

bash

# 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:

bash

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:

bash

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

Output Format

# 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.

finding-software-engineers-on-twitter

NPX Install

Tags

SKILL.md Content

Finding Software Engineers on Twitter

Prerequisites

Inputs

Workflow

Step 1: Search Queries

Step 2: Enrich Profiles

Step 3: Score Candidate Quality

Step 4: Edge Cases

Output Format

Troubleshooting