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Found 1,668 Skills
Complete fal.ai video-to-video system. PROACTIVELY activate for: (1) Kling O1 video editing, (2) Sora Remix transformation, (3) Video upscaling, (4) Frame interpolation, (5) Style transfer (anime, painting), (6) Object replacement/removal, (7) Color correction, (8) Video enhancement pipelines. Provides: Edit types (general/style/object), upscaling options, style keywords, enhancement workflows. Ensures consistent video transformation without flickering.
When the user wants help with outbound sales prospecting, lead sourcing, or pipeline building. Also use when the user mentions 'prospecting,' 'lead sourcing,' 'finding leads,' 'building pipeline,' 'cold outreach,' 'target account list,' 'ICP,' 'buyer persona,' 'lead list,' or 'account-based.' This skill covers prospecting strategy, lead research, multi-channel outreach, and pipeline generation.
Generate, edit, upscale, variate, and style-transfer images using the AgentOS multi-provider image pipeline with automatic fallback and character consistency.
Complete FFmpeg + OpenCV + Python integration guide for video processing pipelines. PROACTIVELY activate for: (1) FFmpeg to OpenCV frame handoff, (2) cv2.VideoCapture vs ffmpeg subprocess, (3) BGR/RGB color format conversion gotchas, (4) Frame dimension order img[y,x] vs img[x,y], (5) ffmpegcv GPU-accelerated video I/O, (6) VidGear multi-threaded streaming, (7) Decord batch video loading for ML, (8) PyAV frame-level processing, (9) Audio stream preservation with video filters, (10) Memory-efficient frame generators, (11) OpenCV + FFmpeg + Modal parallel processing, (12) Pipe frames between FFmpeg and OpenCV. Provides: Color format conversion patterns, coordinate system gotchas, library selection guide, memory management, subprocess pipe patterns, GPU-accelerated alternatives to cv2.VideoCapture. Ensures: Correct integration between FFmpeg and OpenCV without color/coordinate bugs. See also: ffmpeg-python-integration-reference for type-safe parameter mappings.
Follow Up Boss integration. Manage Persons, Organizations, Leads, Deals, Pipelines, Activities and more. Use when the user wants to interact with Follow Up Boss data.
Use this skill when working with Xquik's X Twitter Scraper API for tweet search, user lookup, follower extraction, media workflows, monitors, webhooks, MCP tools, SDKs, and confirmation-gated X account actions. Triggers on Twitter API alternatives, X API automation, scrape tweets, profile tweets, follower export, send tweets, post replies, DMs, and X/Twitter data pipelines.
Workload-aware architecture design for Apache Doris. MUST USE when designing data architectures, choosing between data models, planning ingestion strategies, sizing clusters, or translating business requirements into Apache Doris system designs. Complements doris-best-practices with decision frameworks and sizing-first workflow. Use when user describes a workload involving: IoT, sensor data, telemetry, real-time analytics, dashboard, log analysis, log search, CDC sync, time-series, device monitoring, point query service, ad-hoc analytics, lakehouse federation, ETL/ELT pipeline, report analytics, clickstream, user behavior, observability, metrics, fleet tracking, or any OLAP workload requiring table design from scratch. Also triggers on prompts like: "design a table for...", "how should I store...", "build an architecture for...", "we have X devices sending data every Y seconds", "recommend a cluster size for...", "what data model should I use for...", "we need to ingest X GB/day", "migrate from MySQL/PostgreSQL to Apache Doris". Also use for legacy analytics/search/serving stack consolidation prompts even when Apache Doris is not named explicitly, including replacing or migrating from Impala, Kudu, Elasticsearch/ES, Greenplum, Presto, HBase, Hive, Hadoop, Redis, or Lambda-style multi-engine data platforms.
Audit your biggest closed-won deals to find your PROVEN ideal customer profile, then find more accounts like them. Use whenever someone wants to analyze won deals, audit their best customers, see which companies generated the most revenue, find their real ICP, build a look-alike target list, segment customers by what actually pays, or learn which acquisition channel produced their best revenue. Triggers on: 'audit my biggest deals', 'which customers made us the most money', 'analyze my closed-won', 'what's my proven ICP', 'find more customers like my best ones', 'look-alike accounts', 'HubSpot deal analysis', 'revenue by account', 'which channel generated my best deals', 'acquisition source analysis'. For RevOps, Heads of Sales/Marketing, founders and growth leads doing ICP refinement, account-based targeting or pipeline/QBR review. Reads HubSpot via its MCP or a CSV export, then hands the profile to sales-nav-search-builder to generate the prospecting search. Maintained by La Growth Machine.
Implements infrastructure as code using Terraform, Kubernetes, and cloud platforms. Designs scalable architectures, CI/CD pipelines, and observability solutions. Provides security-first DevOps practices and site reliability engineering guidance.
Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.
Workflow and best practices for writing Apache Airflow DAGs. Use when the user wants to create a new DAG, write pipeline code, or asks about DAG patterns and conventions. For testing and debugging DAGs, see the testing-dags skill.
MongoDB document modeling, aggregation pipeline optimization, sharding strategies, replica set configuration, connection pool management, and indexing patterns. Use this skill for MongoDB-specific issues, NoSQL performance optimization, and schema design.