mcpbeat

Apify Influencer Discovery

lingxling/apify-influencer-discovery

Find and evaluate influencers for brand partnerships, verify authenticity, and track collaboration performance across Instagram, Facebook, YouTube, and TikTok.

4k tokens
context cost
the whole folder, loaded on every use
2
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
223
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/lingxling/awesome-skills-cn --skill apify-influencer-discovery

The instruction itself

11 sections, as written by the author

Influencer Discovery

Discover and analyze influencers across multiple platforms using Apify Actors.

When to Use

  • You need to discover creators or influencers for outreach, partnerships, or campaign planning.
  • The task is to evaluate authenticity, engagement, niche fit, or audience signals across social platforms.
  • You need Apify-based extraction plus a shortlist or summary of suitable influencer candidates.

Prerequisites

(No need to check it upfront)

  • .env file with APIFY_TOKEN
  • Node.js 20.6+ (for native --env-file support)
  • mcpc CLI tool: npm install -g @apify/mcpc

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Determine discovery source (select Actor)
- [ ] Step 2: Fetch Actor schema via mcpc
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the discovery script
- [ ] Step 5: Summarize results

Step 1: Determine Discovery Source

Select the appropriate Actor based on user needs:

| User Need | Actor ID | Best For |

|-----------|----------|----------|

| Influencer profiles | apify/instagram-profile-scraper | Profile metrics, bio, follower counts |

| Find by hashtag | apify/instagram-hashtag-scraper | Discover influencers using specific hashtags |

| Reel engagement | apify/instagram-reel-scraper | Analyze reel performance and engagement |

| Discovery by niche | apify/instagram-search-scraper | Search for influencers by keyword/niche |

| Brand mentions | apify/instagram-tagged-scraper | Track who tags brands/products |

| Comprehensive data | apify/instagram-scraper | Full profile, posts, comments analysis |

| API-based discovery | apify/instagram-api-scraper | Fast API-based data extraction |

| Engagement analysis | apify/export-instagram-comments-posts | Export comments for sentiment analysis |

| Facebook content | apify/facebook-posts-scraper | Analyze Facebook post performance |

| Micro-influencers | apify/facebook-groups-scraper | Find influencers in niche groups |

| Influential pages | apify/facebook-search-scraper | Search for influential pages |

| YouTube creators | streamers/youtube-channel-scraper | Channel metrics and subscriber data |

| TikTok influencers | clockworks/tiktok-scraper | Comprehensive TikTok data extraction |

| TikTok (free) | clockworks/free-tiktok-scraper | Free TikTok data extractor |

| Live streamers | clockworks/tiktok-live-scraper | Discover live streaming influencers |

Step 2: Fetch Actor Schema

Fetch the Actor's input schema and details dynamically using mcpc:

export $(grep APIFY_TOKEN .env | xargs) && mcpc --json mcp.apify.com --header "Authorization: Bearer $APIFY_TOKEN" tools-call fetch-actor-details actor:="ACTOR_ID" | jq -r ".content"

Replace ACTOR_ID with the selected Actor (e.g., apify/instagram-profile-scraper).

This returns:

  • Actor description and README
  • Required and optional input parameters
  • Output fields (if available)

Step 3: Ask User Preferences

Before running, ask:

  • Output format:
  • Quick answer - Display top few results in chat (no file saved)
  • CSV - Full export with all fields
  • JSON - Full export in JSON format
  • Number of results: Based on character of use case

Step 4: Run the Script

Quick answer (display in chat, no file):

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT'

CSV:

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.csv \
  --format csv

JSON:

node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.json \
  --format json

Step 5: Summarize Results

After completion, report:

  • Number of influencers found
  • File location and name
  • Key metrics available (followers, engagement rate, etc.)
  • Suggested next steps (filtering, outreach, deeper analysis)

Error Handling

APIFY_TOKEN not found - Ask user to create .env with APIFY_TOKEN=your_token

mcpc not found - Ask user to install npm install -g @apify/mcpc

Actor not found - Check Actor ID spelling

Run FAILED - Ask user to check Apify console link in error output

Timeout - Reduce input size or increase --timeout

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

How to use it

Copy the folder

Take lingxling/apify-influencer-discovery from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference npm. Without those the skill loads but fails at the first command.