Find Twitter Influencers

Find Twitter Influencers

Setup

Choose the available runtime before doing any credential setup:

• Terminal-free client: skip the shell commands below. Use connected MCP tools. For ScrapeCreators operations, read scrapecreators-api and prefer call_data_provider. For another provider step without a connected tool, use an equivalent built-in public-web method when it preserves the workflow, or clearly report that step as unavailable.
• Local terminal: use the GooseWorks credentials and proxy commands below.

Read your credentials from ~/.gooseworks/credentials.json:

export GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])")
export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))")

If ~/.gooseworks/credentials.json does not exist, tell the user to run: npx gooseworks login

The local proxy endpoints use Bearer auth: -H "Authorization: Bearer $GOOSEWORKS_API_KEY". ScrapeCreators operation descriptions below remain environment-neutral in both runtimes.

Discover, score, and enrich Twitter/X influencers relevant to a company, product, or niche. Returns a ranked list with engagement metrics, relevance reasoning, and contact info.

Workflow

1. Parse the Request

Extract from the user's query:

• Company name or domain (required) — the brand seeking influencers
• Niche/vertical (optional) — e.g., "fintech Twitter", "AI/ML creators", "DTC beauty"
• Size preference (optional) — mid-tier (10K-100K), macro (100K+), or mixed (default: 10K+ minimum)
• Max results (optional, default 20 — scale up or down if the user asks)

2. Resolve the Company

Use Brand.dev to get domain, industry, description, target audience, and keywords. This context drives all subsequent searches.

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"brand-dev","path":"/v1/brand/retrieve-by-name","query":{"name":"Acme","Corp":""}}'

If a domain is provided directly:

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"brand-dev","path":"/v1/brand/retrieve","query":{"domain":"acme.com"}}'

From the result, build a company context string: company name, domain, industry, description, and target audience keywords. Example: "Acme Corp acme.com developer tools API platform engineering teams". Use this in all search queries.

3. Discover Candidates

Run three strategies in parallel to maximize coverage:

Strategy A — Exa search for curated influencer lists (primary, highest signal):

IMPORTANT: x.com and twitter.com profiles are NOT in Exa's search index, so includeDomains: ["x.com"] will return zero Twitter results. Instead, search for curated list pages, blog posts, and articles about influencers in the niche. Use contents.text to get the page content so you can extract Twitter handles from it.

Run 5+ query variations to maximize coverage. Include the core niche AND adjacent niches — many influencers span related topics. Each query returns 10 results with text content:

# Query 1: Core niche — curated lists
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"exa","path":"/search"}'
  "query": "best {niche} Twitter accounts to follow",
  "numResults": 10,
  "contents": {"text": {"maxCharacters": 5000}}
}'

# Query 2: Core niche — different phrasing
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"exa","path":"/search"}'
  "query": "top {industry} influencers on X Twitter must follow",
  "numResults": 10,
  "contents": {"text": {"maxCharacters": 5000}}
}'

# Query 3: Core niche — thought leaders
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"exa","path":"/search"}'
  "query": "{niche} thought leaders creators Twitter handles",
  "numResults": 10,
  "contents": {"text": {"maxCharacters": 5000}}
}'

# Query 4: Adjacent niche 1 (e.g., if niche is "identity verification", try "fraud prevention")
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"exa","path":"/search"}'
  "query": "top {adjacent_niche_1} influencers Twitter accounts to follow",
  "numResults": 10,
  "contents": {"text": {"maxCharacters": 5000}}
}'

# Query 5: Adjacent niche 2 (e.g., "cybersecurity", "regtech", "biometrics")
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"exa","path":"/search"}'
  "query": "best {adjacent_niche_2} experts creators on X Twitter",
  "numResults": 10,
  "contents": {"text": {"maxCharacters": 5000}}
}'

Choosing adjacent niches: From the company context (Step 2), identify 2-3 related verticals. Examples:

• Identity verification → fraud prevention, cybersecurity, regtech, biometrics
• Fintech → banking, payments, DeFi, financial regulation
• AI/ML → data science, MLOps, developer tools
• DTC beauty → skincare, wellness, lifestyle, clean beauty

These queries return listicle pages (e.g., "Top 60 Fintech Influencers", "FinTwit Accounts to Follow") whose text content contains Twitter handles, bios, and follower counts. Parse these in Step 4.

Strategy B — Exa findSimilar (expand from strong listicle finds):

After Strategy A returns results, pick 1-2 of the best curated list URLs and find similar pages:

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"exa","path":"/findSimilar"}'
  "url": "https://example.com/top-fintech-twitter-influencers",
  "numResults": 5,
  "contents": {"text": {"maxCharacters": 5000}}
}'

This surfaces additional curated lists that keyword search may miss. Do NOT use Twitter/X profile URLs for findSimilar — they are not in Exa's index and will return empty results.

Strategy C — Fiber natural-language-search (catch LinkedIn-heavy professionals):

Some influencers are better indexed on LinkedIn but have active Twitter accounts. Fiber can surface these. Run 2-3 queries covering the core niche and adjacent topics:

# Core niche
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/profiles"}'
  "query": "{niche} thought leader content creator with Twitter presence at {industry} companies",
  "pageSize": 20
}'

# Adjacent niche — broader coverage
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/profiles"}'
  "query": "{adjacent_niche} influencer expert with large social media following",
  "pageSize": 20
}'

Cross-reference Fiber results with Twitter in Step 5 — only keep people with active Twitter accounts. Also extract LinkedIn URLs from Fiber results — these are critical for contact enrichment in Step 7.

4. Extract & Deduplicate Usernames

From the text content of Exa listicle pages, parse Twitter handles using multiple patterns:

• @username mentions in the article text
• URLs matching x.com/username or twitter.com/username
• Strip trailing URL paths (e.g., /status/123, /followers) — only keep base profile usernames
• Discard non-profile patterns (search pages, hashtag pages, x.com/home, x.com/search)
• Deduplicate by username (case-insensitive)
• Flag and remove obvious brand/company accounts (e.g., @stripe, @shopify) — focus on individual creators

From Fiber results, extract any Twitter/X URLs from social profiles. Add new handles to the candidate pool.

Also extract LinkedIn URLs mentioned alongside Twitter handles in listicle pages — save these for contact enrichment in Step 7.

Target: ~100-150 unique handles after dedup. Curated list pages typically mention 20-50 handles each, so 5+ good listicle results across core and adjacent niches yield a large candidate pool.

5. Get Twitter Profiles + Engagement

Use Scrape Creators to fetch structured Twitter data. This is a two-step process: fetch profiles for ALL candidates (cheap, 1 credit each), then fetch tweets for the top ~2x the target result count after profile filtering (e.g., ~40 if targeting 20 results).

Step 1 — Fetch profiles for all candidates:

provider: scrapecreators
method: GET
path: /v1/twitter/profile
query:
  handle: examplehandle

Returns nested JSON. Key fields are inside core and legacy objects:

• core.screen_name, core.name — handle and display name
• legacy.description — bio text
• legacy.followers_count, legacy.friends_count, legacy.statuses_count — counts
• legacy.location, legacy.created_at — location and account age
• is_blue_verified — verification status

The profile URL is https://x.com/{screen_name}.

IMPORTANT — Parallelize profile fetches: Do NOT fetch profiles one-by-one in a sequential loop. Instead, launch ALL profile fetch calls in parallel (multiple tool calls in a single message). This dramatically reduces total wait time from minutes to seconds. Group into batches of 10-15 parallel calls if needed.

Apply hard filters to narrow the pool:

• Fewer than 10,000 followers (default minimum — adjustable if user specifies a different threshold)
• Empty bio or bio completely unrelated to the niche
• Suspended or not-found accounts (API returns error)
• Bio indicates they've left X (e.g., "find me on Mastodon/Bluesky", "abandoned this site")
• Too broad/generic accounts — Remove accounts whose content is not specifically relevant to the target niche. For example, a general "tech evangelist" account with 500K+ followers that rarely tweets about the niche is less valuable than a 20K-follower specialist. If an account's bio and recent tweets show no specific connection to the target niche or adjacent niches, exclude it regardless of follower count

Step 2 — Fetch tweets for top candidates (after profile filtering — fetch ~2x the target result count to allow for filtering, e.g., ~40 if targeting 20 results):

provider: scrapecreators
method: GET
path: /v1/twitter/user-tweets
query:
  handle: examplehandle

Returns an array of tweet objects. IMPORTANT — The data is nested inside each tweet object:

• legacy.full_text — tweet text
• legacy.favorite_count — likes (integer)
• legacy.retweet_count — retweets (integer)
• legacy.reply_count — replies (integer)
• legacy.created_at — timestamp
• views.count — view count (may not always be present)
• url — direct link to the tweet (top-level field)

Note: favorite_count, retweet_count, reply_count are inside the legacy object, NOT at the top level of each tweet.

IMPORTANT — Parallelize tweet fetches: Just like profile fetches, launch ALL tweet fetch calls in parallel (multiple tool calls in a single message). Do NOT use sequential for-loops. This is the single biggest speed optimization.

For each candidate, identify the top 3 tweets by engagement (likes + retweets + replies). Save the best one with its URL for inclusion in the final results table.

From the tweet data, calculate:

• Average likes per tweet
• Average retweets per tweet
• Average replies per tweet
• Engagement rate: (avg likes + avg retweets + avg replies) / followers * 100
• Post frequency: inferred from created_at dates
• Content themes: what topics they tweet about most (from full_text)

Additional hard filters (applied after tweet fetch):

• No tweets in the last 30 days (inactive)
• Protected/private accounts

Skip reply-only accounts (>80% of tweets are replies to others with minimal engagement).

6. Score & Rank

Apply a composite scoring model:

Scoring guidelines:

• Relevance: Compare bio and recent tweet topics against the company context string from Step 2. Exact niche match = high score. Adjacent niche = medium. Generic/unrelated = low.
• Engagement rate benchmarks: >3% excellent, 1-3% good, <1% below average (varies by follower tier)
• Content quality: Penalize accounts that are >50% retweets. Reward original threads, insights, and media-rich posts.

Rank all candidates by composite score. Select the top N (default 20) for the final list. 20 is a good default — enough to give the user real options without overwhelming them. Scale up if the user asks for more, but always include at least the top 20 if that many qualify.

7. Enrich Contacts

For the final list, find email addresses and LinkedIn profiles. The key insight: discover LinkedIn URLs first — they dramatically improve match rates for all enrichment APIs.

Step 1 — Collect what you already have: From previous steps, gather:

• Emails visible in Twitter bios (many influencers list them directly)
• LinkedIn URLs extracted from Exa listicle page text (Step 4)
• LinkedIn URLs from Fiber NL search results (Step 3, Strategy C)
• Website URLs from Twitter profile data (Step 5)
• "DM for collabs" or "Open DMs" notes from bios

Step 2 — Discover LinkedIn URLs for candidates missing them:

# Exa search to find LinkedIn profiles by name
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"exa","path":"/search"}'
  "query": "Jane Smith site:linkedin.com/in",
  "numResults": 3,
  "includeDomains": ["linkedin.com"]
}'

Launch ALL these Exa searches in parallel (multiple tool calls in a single message). Match by name + job title/company from their Twitter bio. LinkedIn URLs are indexed by Exa, unlike Twitter profiles.

Step 3 — Fiber kitchen-sink (best coverage for professionals):

# With LinkedIn URL (best match rate — ALWAYS prefer this):
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/kitchen-sink/person"}'
  "profileIdentifier": "https://linkedin.com/in/janesmith"
}'

Note: Fiber kitchen-sink does NOT accept a Twitter URL parameter. Use profileIdentifier (LinkedIn URL) for best results. The name+company fallback has low match rates for influencers — invest in finding LinkedIn URLs in Step 2 instead.

Step 4 — Hunter email-finder (if you have their name + domain from their website/LinkedIn):

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"hunter","path":"/v2/email-finder","query":{"domain":"janesmithcreative.com"}}'

Step 5 — Tomba LinkedIn-to-email (if LinkedIn URL was discovered):

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"tomba","path":"/v1/linkedin","query":{"url":"https://linkedin.com/in/janesmith"}}'

Also extract from enrichment results:

• LinkedIn URL (for outreach or further research)
• Personal website or newsletter link
• Other social profiles