Find TikTok influencers using Apify's Influencer Discovery Agent. Use when the user wants to discover TikTok creators or influencers in any niche.
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Search for TikTok influencers matching a specific niche using Apify's Influencer Discovery Agent.
Requires requests and either GOOSEWORKS_API_KEY or APIFY_API_TOKEN env var.
# Basic search
python3 skills/tiktok-influencer-finder/scripts/find_influencers.py \
--description "fitness coaches for women over 40"
# With follower filters
python3 skills/tiktok-influencer-finder/scripts/find_influencers.py \
--description "AI and tech reviewers" \
--min-followers 10000 --max-followers 500000
# Summary table output
python3 skills/tiktok-influencer-finder/scripts/find_influencers.py \
--description "vegan cooking creators" --output summary
# CSV export
python3 skills/tiktok-influencer-finder/scripts/find_influencers.py \
--description "sustainable fashion" --output csv
Before running the search, ask the user for their filtering criteria. Collect ALL of the following:
Ask all 5 criteria in a single question to minimize back-and-forth. Provide sensible default options but always allow custom input.
Compose a detailed --description combining the user's niche, content style preferences, and target audience. Be specific and descriptive.
python3 skills/tiktok-influencer-finder/scripts/find_influencers.py \
--description "fitness coaches targeting women over 40, focus on home workouts and healthy aging" \
--min-followers 5000 --max-followers 500000 \
--min-fit 0.6 \
--output json
The script uses apify~influencer-discovery-agent actor. It routes through the GooseWorks proxy when GOOSEWORKS_API_KEY is set, or directly to Apify when APIFY_API_TOKEN is set.
After receiving results, apply the user's criteria:
fit score and fitDescription to judge relevance; generally exclude fit < 0.6)Present filtered results in a clean markdown table:
| Creator | Handle | Followers | Engagement | Location | Focus | Fit Score |
Include:
After the table, include:
| Flag | Default | Description |
|---|---|---|
--description | required | Describe the type of influencer to find |
--keywords | 5 | Number of search keywords to generate (max 5) |
--profiles-per-keyword | 10 | Profiles per keyword (max 10) |
--min-followers | none | Minimum follower count filter |
--max-followers | none | Maximum follower count filter |
--min-fit | 0.0 | Minimum fit score (0.0-1.0) |
--output | json | Output format: json, csv, summary |
--token | env var | Apify token (overrides env vars) |
--timeout | 300 | Max seconds for Apify run |
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Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
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Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
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For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
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The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
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Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
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Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
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