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creator-screening创作者筛选

Agent Skill

creator-screening 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:creator-screening(创作者筛选)
来源仓库:https://github.com/shawnshenopeninterx/creator-screening
安装命令:
openclaw skills install creator-screening
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install creator-screening

简介

用于筛选和评估社交媒体创作者的质量与影响力。

  • 适合分析 Instagram、TikTok、YouTube 等平台创作者表现。
  • 支持配置质量框架并调用 Memories.ai V 进行深度画像。
  • 安装命令:openclaw skills install creator-screening,需验证 API 密钥有效性。
  • 注意遵守各平台数据抓取政策与用户隐私规定。

SKILL.md

name
creator-screening
description
Screen and evaluate social media creators/influencers using configurable quality frameworks. Analyzes Instagram, TikTok, YouTube creators using Memories.ai V2 Video Understanding API — fetches profile metadata, runs MAI visual+audio AI analysis on videos, and scores against production quality, audio, delivery, and positioning criteria. Use when asked to vet creators, screen influencers, evaluate content quality, or generate creator reports.
metadata
openclaw
emoji
🎯

Creator Screening Skill

Automated creator/influencer screening powered by Memories.ai V2 Video Understanding API.

Parameters

ParameterDefaultDescription
videos_per_creator5Number of top videos to analyze per creator
video_seconds30First N seconds of each video to analyze
platformsinstagramSupported: instagram, tiktok, youtube
analysis_modemaimai (visual+audio AI analysis) or transcript (audio-only fallback)
frameworkdefaultScreening framework to apply. See references/frameworks/
output_formatdiscorddiscord, pdf (Google Doc→PDF), or json
batch_size10Max creators per batch run

Quick Start

Screen these creators: @anshmehra.in, @nishkarshsharmaa
Parameters: videos_per_creator=3, framework=cac-crusher

Workflow

Step 1: Parse Input

Accept creator URLs in any format:

  • Profile: https://www.instagram.com/username/
  • Individual reel: https://www.instagram.com/reel/SHORTCODE/
  • YouTube: https://www.youtube.com/@channel or /shorts/ID

Step 2: Get Profile & Video Metadata

Memories.ai V2: POST /instagram/video/metadata

python3 scripts/scrape_profiles.py --urls "reel_url1,reel_url2" --channel rapid

Returns per video ($0.01/video):

  • Owner profile: username, full_name, followers, verified, profile_pic
  • Video stats: views, play_count, duration, caption, comments, dimensions, audio info

Step 3: Video Understanding (MAI)

Memories.ai V2 MAI: POST /instagram/video/mai/transcript

This is the core analysis step. MAI provides:

  • Visual scene descriptions: lighting quality, framing, environment, clothing, production value
  • Audio transcription: speech-to-text with timestamps
  • Content understanding: topic classification, delivery style, structure
python3 scripts/analyze_videos.py --mode mai --videos_per_creator 5 --urls "url1,url2"

Each video returns visual + audio AI analysis. Use this to evaluate:

  • Section 2.1: Look & Feel (lighting, environment, framing) — from visual scenes
  • Section 2.2: Audio Quality (clarity, echo, consistency) — from audio analysis
  • Section 3.x: Delivery & Content (structure, fluency, maturity) — from transcript text
  • Section 4: Positioning (tone, energy, brand safety) — from combined analysis

Fallback: If MAI is unavailable, use --mode transcript for audio-only analysis:

python3 scripts/analyze_videos.py --mode transcript --videos_per_creator 5 --urls "url1,url2"

Step 4: Apply Framework

Score against the selected screening framework:

python3 scripts/score_creator.py --framework cac-crusher --profile profile.json --transcripts transcripts.json

Frameworks live in references/frameworks/:

  • cac-crusher.md — CAC Crusher Creator Screening Framework (Talking Head + Skit categories)
  • default.md — Generic quality screening (5 dimensions, weighted scoring)
  • template.md — Template for creating custom frameworks

Step 5: Generate Report

Output per-creator screening cards with:

  • Profile stats (followers, verified, engagement)
  • Per-section scores (PASS/FAIL/FLAG)
  • Transcript excerpts as evidence
  • Visual quality notes from MAI
  • Final verdict (APPROVED / REJECTED / CONDITIONAL)

Setup

Set the following environment variables before use:

export MEMORIES_API_KEY="your-memories-ai-v2-api-key"   # Required — Memories.ai V2 API key
export APIFY_API_KEY="your-apify-key"                    # Optional — fallback scraper for profiles

Get your API key at https://api-tools.memories.ai

Memories.ai V2 API Reference

All endpoints use: Authorization: <API_KEY> header (no Bearer prefix). Base URL: https://mavi-backend.memories.ai/serve/api/v2

EndpointMethodUseCost
/{platform}/video/metadataPOSTProfile + video stats$0.01/video
/{platform}/video/mai/transcriptPOSTVisual + audio AI analysis~$0.11/video
/{platform}/video/transcriptPOSTAudio transcription only~$0.01/video

Platforms: instagram, tiktok, youtube, twitter

Request body: {"video_url": "...", "channel": "rapid"} MAI response: {"data": {"task_id": "..."}} (async, results via webhook)

URL format: Instagram must use /reel/SHORTCODE/ (not /p/ or /reels/)

Error Handling

  • Add 0.5s delay between API calls
  • Retry failed requests once, then skip
  • If MAI webhook not received, fall back to transcript mode
  • Always normalize Instagram URLs: /p//reel/, /reels//reel/

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

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能力 5

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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