# Apify Influencer Discovery

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

- Skill: `techwavedev/apify-influencer-discovery` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add techwavedev/apify-influencer-discovery`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/apify-influencer-discovery/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/apify-influencer-discovery

---


# Influencer Discovery

Discover and analyze influencers across multiple platforms using Apify Actors.

## 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:

```bash
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:
1. **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
2. **Number of results**: Based on character of use case

### Step 4: Run the Script

**Quick answer (display in chat, no file):**
```bash
node --env-file=.env ${CLAUDE_PLUGIN_ROOT}/reference/scripts/run_actor.js \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT'
```

**CSV:**
```bash
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:**
```bash
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`

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Retrieve prior API design decisions, database schema choices, and error handling patterns. Cache API response templates for consistent error formatting.

```bash
# Check for prior backend/API context before starting
python3 execution/memory_manager.py auto --query "API design patterns and architecture decisions for Apify Influencer Discovery"
```

### Storing Results

After completing work, store backend/API decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "API architecture: REST with HATEOAS, JWT auth, rate limiting at 100 req/min per tenant" \
  --type decision --project <project> \
  --tags apify-influencer-discovery backend
```

### Multi-Agent Collaboration

Share API contract changes with frontend agents so they update their client code, and with QA agents for test coverage.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Implemented API endpoints — 5 new routes with OpenAPI spec and integration tests" \
  --project <project>
```

### Agent Team: Code Review

After implementation, dispatch `code_review_team` for two-stage review (spec compliance + code quality) before merging.

<!-- AGI-INTEGRATION-END -->