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Content & Marketing
SEO, copywriting, social, campaigns, content strategy, audio, video, and media
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Showing 5,281–5,304 of 14,052 skills
- VideodbSee, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio...Votes: 0GitHub stars: 3
- Video EditingAI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.Votes: 0GitHub stars: 3
- Nuxt4 PatternsNuxt 4 app patterns for hydration safety, performance, route rules, lazy loading, and SSR-safe data fetching with useFetch and useAsyncData.Votes: 0GitHub stars: 3
- Marketing CampaignEnd-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches.Votes: 0GitHub stars: 3
- Marketing AgentMarketing strategist and copywriter for campaign planning, audience research, positioning, copy creation, and content review. Covers landing pages, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use when the user wants to plan or execute a product launch or marketing campaign.Votes: 0GitHub stars: 3
- Lead IntelligenceAI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, LinkedIn, and X. Use when the user wants to find, qualify, and reach high-value contacts.Votes: 0GitHub stars: 3
- X ApiX/Twitter API集成,用于发布推文、线程、读取时间线、搜索和分析。涵盖OAuth认证模式、速率限制和平台原生内容发布。当用户希望以编程方式与X交互时使用。Votes: 0GitHub stars: 3
- Videodb视频与音频的查看、理解与行动。查看:从本地文件、URL、RTSP/直播源或实时录制桌面获取内容;返回实时上下文和可播放流链接。理解:提取帧,构建视觉/语义/时间索引,并通过时间戳和自动剪辑搜索片段。行动:转码和标准化(编解码器、帧率、分辨率、宽高比),执行时间线编辑(字幕、文本/图像叠加、品牌化、音频叠加、配音、翻译),生成媒体资源(图像、音频、视频),并为直播流或桌面捕获的事件创建实时警报。Votes: 0GitHub stars: 3
- Video EditingAI辅助的视频编辑工作流程,用于剪辑、构建和增强实拍素材。涵盖从原始拍摄到FFmpeg、Remotion、ElevenLabs、fal.ai,再到Descript或CapCut最终润色的完整流程。适用于用户想要编辑视频、剪辑素材、制作vlog或构建视频内容的情况。Votes: 0GitHub stars: 3
- Fal Ai Media通过 fal.ai MCP 实现统一的媒体生成——图像、视频和音频。涵盖文本到图像(Nano Banana)、文本/图像到视频(Seedance、Kling、Veo 3)、文本到语音(CSM-1B),以及视频到音频(ThinkSound)。当用户想要使用 AI 生成图像、视频或音频时使用。Votes: 0GitHub stars: 3
- Crosspost跨X、LinkedIn、Threads和Bluesky的多平台内容分发。使用内容引擎模式根据平台适配内容。从不跨平台发布相同内容。当用户希望跨社交平台分发内容时使用。Votes: 0GitHub stars: 3
- X Apiツイートの投稿、スレッド、タイムラインの読み取り、検索、分析のためのX/Twitter API統合。OAuth認証パターン、レートリミット、プラットフォームネイティブなコンテンツ投稿をカバーする。ユーザーがプログラムでXと対話したい場合に使用する。Votes: 0GitHub stars: 3
- Videodbビデオとオーディオの表示、理解、アクション。表示:ローカルファイル、URL、RTSP/ライブストリーム、またはリアルタイムのデスクトップ録画からコンテンツを取得し、リアルタイムコンテキストと再生可能なストリームリンクを返す。理解:フレームを抽出し、ビジュアル/セマンティック/時間的インデックスを構築し、タイムスタンプと自動クリップでモーメントを検索する。アクション:トランスコードと正規化(コーデック、フレームレート、解像度、アスペクト比)、タイムライン編集(字幕、テキスト/画像オーバーレイ、ブランディング、オーディオオーバーレイ、吹き替え、翻訳)、メディアアセットの生成(画像、オーディオ、ビデオ)、ライブストリームまたはデスクトップキャプチャされたイベントのリアルタイムアラートを実行する。Votes: 0GitHub stars: 3
- Video Editing実写素材のカット、構築、強化のためのAI支援ビデオ編集ワークフロー。生の撮影素材からFFmpeg、Remotion、ElevenLabs、fal.aiを経て、DescriptまたはCapCutで最終仕上げを行う完全なパイプラインをカバーする。ユーザーがビデオの編集、素材のカット、vlogの作成、またはビデオコンテンツの構築を望む場合に使用する。Votes: 0GitHub stars: 3
- Fal Ai Mediafal.ai MCPによる統合メディア生成(画像、動画、音声)。テキストから画像(Nano Banana)、テキスト/画像から動画(Seedance、Kling、Veo 3)、テキストから音声(CSM-1B)、動画から音声(ThinkSound)をカバーします。ユーザーがAIで画像、動画、音声を生成したい場合に使用します。Votes: 0GitHub stars: 3
- Vigil Cognitive Bias EvalEvaluates a real-time browser extension's ability to detect and mitigate cognitive bias triggers in online text. It probes span-level identification of persuasive rhetoric and bias patterns, alongside system latency and mitigation quality. Use when the user wants to benchmark on SemEval-2020 Task 11, Moralization Corpus, or asks about evaluating this task. Reports micro-F1.Votes: 0GitHub stars: 3
- Urdu Mner EvalEvaluates the ability of models to recognize named entities (Person, Location, Organization, Miscellaneous) in Urdu social media posts by jointly processing textual and visual inputs. It probes cross-modal alignment, handling of low-resource language morphological complexity, and ambiguity resolution using visual context. Use when the user wants to benchmark on Twitter2015-Urdu, or asks about evaluating this task. Reports F1 score.Votes: 0GitHub stars: 3
- Tad Bench EvalEvaluates the effectiveness of various text embedding models combined with different anomaly detection algorithms for identifying text anomalies. It probes how well embedding-based anomaly detection generalizes across diverse domains (spam, fake news, hate speech) and distinguishes between patterned versus context-dependent anomalies. Use when the user wants to benchmark on Email-Spam, SMS-Spam, COVID-Fake, LIAR2, Hate-Speech, OLID, or asks about evaluating this task. Reports AUROC.Votes: 0GitHub stars: 3
- Momenta Misinformation EvalEvaluates a multimodal misinformation detection model's ability to classify fake vs. real news across heterogeneous datasets, measuring classification accuracy, ranking quality, and class-balanced performance under calibrated decision thresholds. Use when the user wants to benchmark on Fakeddit, MMCoVaR, Weibo, XFacta, or asks about evaluating this task. Reports F1.Votes: 0GitHub stars: 3
- Mbib EvalThis benchmark evaluates a model's ability to identify various forms of media bias, including linguistic, cognitive, political, racial, gender, and hate speech bias, across diverse text sources like news articles, tweets, and social media comments. It probes whether models can generalize across different bias types and dataset sizes without being skewed by larger datasets. Use when the user wants to benchmark on MBIB, or asks about evaluating this task. Reports macro F1-score.Votes: 0GitHub stars: 3
- Gmner EvalEvaluates a model's ability to recognize named entities in multimodal social media content and ground them to visual regions, while dynamically deciding when to use internal knowledge versus external search tools. Use when the user wants to benchmark on Twitter-GMNER*, Twitter-FMNERG*, or asks about evaluating this task. Reports F1 score.Votes: 0GitHub stars: 3
- Crisismmd Classification EvalEvaluates multimodal deep learning models for disaster response by classifying social media posts into informativeness categories (informative vs. not-informative) and humanitarian content categories (e.g., affected individuals, rescue efforts, infrastructure damage). It tests the model's ability to jointly learn from text and image modalities to improve classification performance over unimodal baselines. Use when the user wants to benchmark on CrisisMMD, or asks about evaluating this task. R...Votes: 0GitHub stars: 3
- Social ContentWhen the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, Facebook, or other platforms. Also use when the user mentions 'LinkedIn...Votes: 0GitHub stars: 36
- Referral ProgramWhen the user wants to create, optimize, or analyze a referral program, affiliate program, or word-of-mouth strategy. Also use when the user mentions 'referral,' 'affiliate,' 'ambassador,' 'word of...Votes: 0GitHub stars: 36