building-twitter-prospect-lists

Q: How do I install building-twitter-prospect-lists?

Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill building-twitter-prospect-lists in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/building-twitter-prospect-lists/ (personal) or .claude/skills/building-twitter-prospect-lists/ (project).

by apidojo-ioMarketing & SEO

Builds targeted B2B prospect lists from Twitter/X profiles and posts using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find Twitter users with a specific job title or keyword in bio, build a list of founders or executives on Twitter, find people tweeting about a topic for outreach, identify potential customers on X, scrape Twitter profiles matching an ICP description, find decision-makers in a specific industry on Twitter, or export a list of leads from Twitter bios. Returns name, username, bio, follower count, location, and recent tweet samples per prospect. Ideal for B2B SDRs, growth hackers, founder-led sales teams, and partnership managers.

Installs: 766
Last 24h: +11
Trending rank: #8330
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/apidojo-io/apidojo-skills --skill building-twitter-prospect-lists

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads building-twitter-prospect-lists automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Building Twitter Prospect Lists

Searches Twitter/X for profiles matching a target ICP (Ideal Customer Profile) using bio keywords and topic-based tweet search. Delivers a contact-ready list with engagement signals and bio context.

Prerequisites

APIFY_TOKEN environment variable set
Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
`searchTerms`	array	✅	`[]`	Twitter advanced search queries (e.g. `["#AI lang:en", "from:NASA"]`)
`sort`	string	Optional	`Top`	Sort order: `Latest`, `Top`, or `Latest+Top`
`tweetLanguage`	string	Optional	—	ISO 639-1 language code (e.g. `en`)
`maxItems`	number	Optional	Unlimited	Maximum tweets to return
`onlyVerifiedUsers`	boolean	Optional	`false`	Only tweets from verified users
`onlyTwitterBlue`	boolean	Optional	`false`	Only Twitter Blue subscribers
`onlyImage`	boolean	Optional	`false`	Only tweets with images
`onlyVideo`	boolean	Optional	`false`	Only tweets with videos
`onlyQuote`	boolean	Optional	`false`	Only quote tweets
`author`	string	Optional	—	Filter to a specific author handle
`inReplyTo`	string	Optional	—	Tweets replying to a specific handle
`mentioning`	string	Optional	—	Tweets mentioning a specific handle
`geotaggedNear`	string	Optional	—	Tweets near a location
`withinRadius`	string	Optional	—	Radius around geotaggedNear
`geocode`	string	Optional	—	Lat/lng + radius string
`placeObjectId`	string	Optional	—	Tweets tagged with a place
`minimumRetweets`	number	Optional	—	Minimum retweet count
`minimumFavorites`	number	Optional	—	Minimum like count
`minimumReplies`	number	Optional	—	Minimum reply count
`start`	string	Optional	—	Tweets after this date (YYYY-MM-DD)
`end`	string	Optional	—	Tweets before this date (YYYY-MM-DD)
`includeSearchTerms`	boolean	Optional	`false`	Add the matched search term to each tweet
`customMapFunction`	string	Optional	—	JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Define ICP and search strategy
- [ ] Step 2: Run tweet-scraper for keyword/topic tweets
- [ ] Step 3: Extract unique authors from results
- [ ] Step 4: Enrich with twitter-user-scraper for bio + follower data
- [ ] Step 5: Filter, rank, and deliver prospect list

Step 1: Define ICP and Strategy

Ask the user:

Job title keywords for Twitter bio search (e.g., "Head of Growth", "Founder", "CTO")
Topic keywords — what topics does the ICP tweet about? (e.g., "SaaS metrics", "PLG", "RevOps")
Industry signals — keywords that suggest the right industry in bio (e.g., "SaaS", "fintech", "healthcare")
Follower range (optional) — e.g., 1,000–50,000 (avoids both nobodies and celebrities)
Location (optional) — e.g., "San Francisco", "London"
List size — how many prospects needed?

Step 2: Search for Topic-Based Tweets

Search Twitter for tweets about topics your ICP cares about. People who actively tweet about a topic are warmer prospects.

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

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

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

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~tweet-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": ["[TOPIC_KEYWORD_1]", "[TOPIC_KEYWORD_2]"],
  "maxItems": 200,
  "tweetLanguage": "en"
}

If Apify MCP is not available:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchTerms": ["[TOPIC_KEYWORD]"],
    "maxItems": 200
  }'

Run for each topic keyword. Collect all author.username values. Deduplicate. This gives you a candidate pool.

Step 3: Enrich Candidates with Profile Data

Take the top 100-200 unique usernames from Step 2. Fetch full profile data to filter by bio keywords and follower count.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~twitter-user-scraper"
Input:
{
  "usernames": ["[username1]", "[username2]", "..."],
  "maxItems": 100
}

If Apify MCP is not available:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~twitter-user-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "usernames": ["[username1]", "[username2]"]
  }'

Step 4: Filter Against ICP Criteria

From profile data, keep only users where ALL of these are true:

Bio contains at least one job title keyword OR industry signal keyword
Follower count is within the specified range (if given)
Location matches (if specified) — check location field
Account is not a bot (has profile picture, has >10 tweets, account age >6 months)

Remove:

Accounts with default profile images
Accounts with 0 tweets
Verified mega-influencers (follower count above range)
Obviously automated accounts

Step 5: Rank and Format

Rank filtered prospects by:

Relevance score = number of ICP keywords matched in bio
Engagement proxy = (likes + retweets on recent tweets) / follower count

Output Format

# Twitter Prospect List: [ICP DESCRIPTION]
Generated: [N] prospects | Filters applied: [summary] | Date: [DATE]

| # | Name | Handle | Followers | Job / Bio | Location | Last Active | Profile |
|---|------|--------|-----------|-----------|----------|-------------|---------|
| 1 | [name] | @[handle] | [N] | [bio excerpt] | [city] | [date] | [url] |
| 2 | [name] | @[handle] | [N] | [bio excerpt] | [city] | [date] | [url] |

## Top 10 Highest-Priority Prospects
1. **@[handle]** — "[bio]" | [N] followers | Recently tweeted about: [topic]
2. **@[handle]** — "[bio]" | [N] followers | Recently tweeted about: [topic]
...

## Notes
- [N] candidates found in topic search
- [N] filtered out (didn't match ICP criteria)
- [N] final prospects delivered
- Engagement signals are 24-48h delayed

Personalizing Outreach

For each top prospect, the recent tweet sample can be used to personalize outreach. Note their recent topics to reference in a first message.

Troubleshooting

Too few results after filtering: Broaden bio keywords (use OR logic, not AND). Try more topic keywords in Step 2. Too many irrelevant accounts: Add industry-specific keywords to bio filter (e.g., require "SaaS" or "B2B" in bio). Location filter not working: Twitter location is self-reported and inconsistent — treat it as a soft signal, not a hard filter.

FAQ

What does the building-twitter-prospect-lists skill do?

How do I install building-twitter-prospect-lists?

Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill building-twitter-prospect-lists in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/building-twitter-prospect-lists/ (personal) or .claude/skills/building-twitter-prospect-lists/ (project).

Which AI agents can use building-twitter-prospect-lists?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is building-twitter-prospect-lists safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains building-twitter-prospect-lists?

It is published by apidojo-io in apidojo-io/apidojo-skills. It has 766 recorded installs.

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building-twitter-prospect-lists

Install

Install

Describe the task

Review the result

SKILL.md

Building Twitter Prospect Lists

Prerequisites

Inputs

Workflow

Step 1: Define ICP and Strategy

Step 2: Search for Topic-Based Tweets

Step 3: Enrich Candidates with Profile Data

Step 4: Filter Against ICP Criteria

Step 5: Rank and Format

Output Format

Personalizing Outreach

Troubleshooting

FAQ

More from apidojo-io/apidojo-skills

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