Influencer Discovery

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/apify-influencer-discovery

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Influencer Discovery Discover and analyze influencers across multiple platforms using Apify Actors.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Prerequisites, Error Handling, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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.
• Use when the request clearly matches the imported source intent: Find and evaluate influencers for brand partnerships, verify authenticity, and track collaboration performance across Instagram, Facebook, YouTube, and TikTok.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.

Operating Table

metadata.json

ORIGIN.md

reference/scripts/run_actor.js

reference/scripts/run_actor.js

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Step 1: Determine discovery source (select Actor)
2. Step 2: Fetch Actor schema via mcpc
3. Step 3: Ask user preferences (format, filename)
4. Step 4: Run the discovery script
5. Step 5: Summarize results
6. User Need - Actor ID - Best For
7. Influencer profiles - apify/instagram-profile-scraper - Profile metrics, bio, follower counts

Imported Workflow Notes

Imported: 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:

apify/instagram-profile-scraper

apify/instagram-hashtag-scraper

apify/instagram-reel-scraper

apify/instagram-search-scraper

apify/instagram-tagged-scraper

apify/instagram-scraper

apify/instagram-api-scraper

apify/export-instagram-comments-posts

apify/facebook-posts-scraper

apify/facebook-groups-scraper

apify/facebook-search-scraper

streamers/youtube-channel-scraper

clockworks/tiktok-scraper

clockworks/free-tiktok-scraper

clockworks/tiktok-live-scraper

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

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):

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)

Imported: 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

Examples

Example 1: Ask for the upstream workflow directly

Use @apify-influencer-discovery to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @apify-influencer-discovery against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @apify-influencer-discovery for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @apify-influencer-discovery using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/apify-influencer-discovery

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @10-andruia-skill-smith

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @20-andruia-niche-intelligence

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @3d-web-experience

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

• scripts/run_actor.js

Imported Reference Notes

Imported: Error Handling

APIFY_TOKEN not found

.env

APIFY_TOKEN=your_token

mcpc not found

npm install -g @apify/mcpc

Actor not found

Run FAILED

Timeout

--timeout

Imported: 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.