extracting-youtube-comments-for-research

Extracts and analyzes YouTube comments for audience research using apidojo's YouTube scraper on Apify.

What this skill does for you

Extracts and analyzes YouTube comments for audience research using apidojo's YouTube scraper on Apify. Triggers when the user asks to: extract YouTube comments for research, analyze what viewers say in YouTube comments, scrape comments from a YouTube video for sentiment analysis, find common questions in YouTube comments, research audience feedback from YouTube video comments, extract top comments from a YouTube channel for audience insights, or analyze viewer reactions from YouTube comment sections. Returns comment text, likes on comment, reply count, commenter username, and timestamp. Ideal for content creators, brand researchers, product teams, and audience insight analysts.

Quoted from the skill description in the pinned source

Who made it

Publisherapidojo-io

Repositoryapidojo-skills

Installs416as of Sep 27, 2026

Use it with Edge

Set up Edge for me: read getedge.cc/SKILL.md and follow it. Then use Edge to load the extracting-youtube-comments-for-research skill from apidojo-io/apidojo-skills.

Paste it into Claude, ChatGPT, Codex or Cursor.

Or read the skill source first.

Security evidence

Security state at 2026-09-30: reviewed. State definitions. This records static evidence for this revision; live load eligibility is checked again.

3 static scans, 0 counted findings. The listed scanners recorded the results shown for this package revision.

Scanner	Date	Result
Edge static checks	2026-09-27	Pass
Cisco skill-scanner	2026-09-27	Pass
Semgrep Edge rules	2026-09-27	Pass

Static scans check the code, not how well the skill works.

Scanner scope, raw findings and mirrored provider records

Edge static checks (Edge-run): edge-static/1.0.1. Scope: Static patterns for remote execution, credentials with network sends, obfuscation, prompt overrides, hidden Unicode, binaries and persistence. Revision: ffbdc00cbd01d09802e7f389ac4f61d4803972a7. Raw findings: . Counted findings: .

Cisco skill-scanner (Edge-run): cisco-skill-scanner/2.1.0. Scope: Local static, YARA, pipeline and behavioral analyzers; no LLM or AI Defense analysis. Revision: ffbdc00cbd01d09802e7f389ac4f61d4803972a7. Raw findings: . Counted findings: .

Semgrep Edge rules (Edge-run): semgrep/1.178.0+edge-rules.c15016a36fd3+offline-v1. Scope: Six Edge-authored static rules with Semgrep CE; offline execution and metrics disabled. Semgrep-maintained rules excluded. Revision: ffbdc00cbd01d09802e7f389ac4f61d4803972a7. Raw findings: . Counted findings: .

Source and licence

Repository: https://github.com/apidojo-io/apidojo-skills
Pinned skill file: View source at revision
Revision: ffbdc00cbd01d09802e7f389ac4f61d4803972a7
Package hash: sha256:4ff66c75a36c882a8043b4703f05bb931afc0e5131e025545b321000c67fd8f0
Licence: Apache-2.0