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Found 1,668 Skills
Use Alibaba Cloud DashScope API and LingMou to generate AI video and speech. Seven capabilities — (1) LivePortrait talking-head (image + audio → video, two-step), (2) EMO talking-head, (3) AA/AnimateAnyone full-body animation (three-step), (4) T2I text-to-image (Wan 2.x, default wan2.2-t2i-flash), (5) I2V image-to-video (Wan 2.x, default wan2.7-i2v-flash, supports T2I→I2V pipeline), (6) Qwen TTS (auto model/voice by scene, default qwen3-tts-vd-realtime-2026-01-15), (7) LingMou digital-human template video with random template, public-template copy, and script confirmation. Trigger when the user needs talking-head, portrait, full-body animation, text-to-image, text-to-video, or speech synthesis.
Cloud Convert integration. Manage Deals, Persons, Organizations, Leads, Projects, Pipelines and more. Use when the user wants to interact with Cloud Convert data.
Use this skill when applying visual filters or post-processing effects in Phaser 4. Covers bloom, blur, glow, color matrix, barrel distortion, displacement, custom shaders, and the filter pipeline. Triggers on: filter, post-processing, shader, bloom, blur, glow, color effects.
Automates declarative resource creation and provisioning for data pipelines, supporting BigQuery, Dataform, Dataproc, BigQuery Data Transfer Service (DTS), and other resources. It manages environment-specific configurations (dev, staging, prod) through a deployment.yaml file. Use when: - Modifying or creating deployment.yaml for deployment settings. - Resolving environment-specific variables (e.g., Project IDs, Regions) for deployment. - Provisioning supported infrastructure like BigQuery datasets/tables, Dataform resources, or DTS resources via deployment.yaml. Do not use when: - Resources already exist. - Managing resources not supported by `gcloud beta orchestration-pipelines resource-types list`. - Managing general cloud infrastructure (VMs, networks, Kubernetes, IAM policies), which are better suited for Terraform. - Infrastructure spans multiple cloud providers (AWS, Azure, etc.). - Already uses Terraform for the target resources.
Expertise in generating clean, correct, and efficient Dataform pipeline code for BigQuery ELT. Use this when creating or modifying Dataform pipelines, actions, or source declarations, when Dataform, SQLX, or BigQuery are mentioned in a transformation, when data needs to be ingested from GCS into BigQuery via Dataform, or when setting up a new Dataform project or configuring workflow_settings.yaml.
Discovers and inspects BigQuery Data Transfer Service (DTS) configurations. Use this to identify existing ingestion pipelines and extract datasource or transfer config metadata for data pipelines. Use when a user asks for ingestion scenarios while building or managing data pipelines or when a user asks to "ingest" or "add" data that may already be managed by a DTS transfer.
Navigate the stardust design pipeline — assess project state under `stardust/` and recommend the next design stage. Use when the user wants to check design-pipeline progress, doesn't know which stage to run next, asks a general question about `stardust/` artifacts without naming a specific stage, says `/stardust`, or asks about files under `stardust/` (brand, briefings, wireframes, prototypes) without a clear edit target.
Generate "image + text" style visual PPT decks (evangelism / internal sharing / client-facing) using an HTML→PPTX pipeline with safe-zone typography, Nano Banana backgrounds, and overflow-to-notes discipline. Use this when the user needs a visually dense, cinematic slide deck where the layout is image-driven rather than text-driven. NOT suitable for pure data reports or text-heavy documents.
Revenue operations analyst specializing in pipeline health diagnostics, deal velocity analysis, forecast accuracy, and data-driven sales coaching. Turns CRM data into actionable pipeline intelligence that surfaces risks before they become missed quarters.
Builds production AI/ML systems — model training, fine-tuning, MLOps pipelines, model serving, evaluation frameworks, RAG optimization, and agent orchestration at scale. Use when the user asks to build, train, or deploy ML models, set up MLOps pipelines, optimize RAG systems, create inference endpoints, or design production AI agents.
Streak integration. Manage Persons, Organizations, Deals, Pipelines, Users, Roles. Use when the user wants to interact with Streak data.
Fireberry integration. Manage Organizations, Pipelines, Users, Goals, Filters. Use when the user wants to interact with Fireberry data.