---
type: "post"
id: "scrapecreators-released-13-open-source-agent-skills-for-855a33"
slug: "scrapecreators-released-13-open-source-agent-skills-for-855a33"
title: "ScrapeCreators released 13 open-source agent skills for social media research across 10-plus platforms."
description: "ScrapeCreators published 13 open-source skills for social-media research, spanning tasks such as outlier-post discovery, comment mining, competitor analysis, ad-library teardown, trend discovery, and content repurposing."
retrieval_nugget: "ScrapeCreators published 13 open-source skills for social-media research, spanning tasks such as outlier-post discovery, comment mining, competitor analysis, ad-library teardown, trend discovery, and content repurposing."
published_at: "2026-10-17"
updated_at: "2026-10-17"
record_date: "2026-10-17"
date_kind: "published_at"
topics: ["skills"]
source_urls: ["https://github.com/ScrapeCreators/social-media-research-skills"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:scrapecreators-released-13-open-source-agent-skills-for-855a33"
status: "published"
visuals: []
editorial_provenance: {"schema_version":"newruntime-editorial-copy-v1","content_status":"source_grounded_final","final_copy_sha256":"sha256:dab5412b8506aff24c09208fb69865bb42eedc9d468fd7c1ec694f3c8a771f9c","reviewed_at":"2026-10-17T14:03:04.949Z","source_evidence_count":1,"verified_claim_count":3,"site_analysis_schema_version":"newruntime-site-analysis-v1","site_object_kind":"field_note","observed_fact_count":3,"implication_count":2,"watch_condition_count":1,"related_record_count":0}
analysis: {"schema_version":"newruntime-site-analysis-v1","object_kind":"field_note","thesis":"Research skills are becoming packaged as portable, task-specific capabilities rather than isolated API recipes.","observed_facts":[{"text":"The repository lists 13 research skills, including outlier-post discovery, comment mining, competitor analysis, ad-library teardown, trend discovery, and content repurposing.","source_urls":["https://github.com/ScrapeCreators/social-media-research-skills"]},{"text":"The repository names TikTok, Instagram, YouTube, Reddit, X, and LinkedIn among supported platforms and describes outputs including briefs, tables, and reports.","source_urls":["https://github.com/ScrapeCreators/social-media-research-skills"]},{"text":"The repository lists compatibility with Claude Code, Cursor, Codex, and Gemini CLI, requires a ScrapeCreators API key, and states an MIT license.","source_urls":["https://github.com/ScrapeCreators/social-media-research-skills"]}],"mechanism":"Each skill maps an intended research job to a workflow and a named output artifact, while the separate scrapecreators-api skill is described as the data layer for endpoint details.","why_now":"Agent environments increasingly accept reusable skill packages, making workflow portability a practical distribution surface.","implications":["The first implication is that users can select an outcome-oriented research workflow without designing endpoint sequences from scratch.","The practical significance therefore rests on whether the workflows deliver trustworthy work products, not on the number of skills alone."],"evidence_boundary":"The available source is the project’s GitHub repository and documentation; it provides no independent evaluation, usage data, or comparative performance results.","watch_conditions":["The first watch condition is whether maintainers publish examples or evaluations showing that generated artifacts remain sourced, reliable, and useful across platforms and agent environments."],"related_records":[],"new_branch_reason":"No prior New Runtime analytical record was supplied; this release starts a distinct record on task-specific social research skills as portable agent capabilities."}
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
routes: {"html":"https://newruntime.com/posts/scrapecreators-released-13-open-source-agent-skills-for-855a33/","markdown":"https://newruntime.com/posts/scrapecreators-released-13-open-source-agent-skills-for-855a33.md","json":"https://newruntime.com/posts/scrapecreators-released-13-open-source-agent-skills-for-855a33.json"}
related_patterns: ["skills-become-portable-capability-layer"]
---

# ScrapeCreators released 13 open-source agent skills for social media research across 10-plus platforms.

## Retrieval answer

ScrapeCreators published 13 open-source skills for social-media research, spanning tasks such as outlier-post discovery, comment mining, competitor analysis, ad-library teardown, trend discovery, and content repurposing.

ScrapeCreators published 13 open-source skills for social-media research, spanning tasks such as outlier-post discovery, comment mining, competitor analysis, ad-library teardown, trend discovery, and content repurposing. The repository names TikTok, Instagram, YouTube, Reddit, X, LinkedIn, and other platforms, and says workflows yield artifacts such as briefs, tables, and reports. The repo lists compatibility with Claude Code, Cursor, Codex, and Gemini CLI, requires a ScrapeCreators API key, and is licensed under MIT.

Thesis: Research skills are becoming packaged as portable, task-specific capabilities rather than isolated API recipes. Mechanism: Each skill maps an intended research job to a workflow and a named output artifact, while the separate scrapecreators-api skill is described as the data layer for endpoint details. Why now: Agent environments increasingly accept reusable skill packages, making workflow portability a practical distribution surface. The first implication is that users can select an outcome-oriented research workflow without designing endpoint sequences from scratch.

This release supports the pattern, but the repository description does not establish the quality, accuracy, or repeatability of results in real deployments. Evidence boundary: The available source is the project’s GitHub repository and documentation; it provides no independent evaluation, usage data, or comparative performance results. The first watch condition is whether maintainers publish examples or evaluations showing that generated artifacts remain sourced, reliable, and useful across platforms and agent environments. The practical significance therefore rests on whether the workflows deliver trustworthy work products, not on the number of skills alone.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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- topic hub: [Skills](https://newruntime.com/topics/skills.md): Explore the skills topic hub.
- collection: [Field Notes](https://newruntime.com/posts.json): Open the latest editorial analysis.
- collection: [Source Ledger](https://newruntime.com/source-ledger.json): Inspect the public source evidence graph.
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