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Found 790 Skills
Use Replay MCP to inspect the contents of https://replay.io recordings.
Evaluate Clojure code via nREPL using clj-nrepl-eval. Use this when you need to test code, check if edited files compile, verify function behavior, or interact with a running REPL session.
AEM as a Cloud Service content distribution and replication. Covers programmatic publishing using the Replication API and distribution event monitoring using Sling Distribution events.
Single-file horizontal-swipe HTML deck in the style of Replit Slides's landing-page template gallery. Eight distinct themes (helix, holm, vance, bevel, world-dark, world-mint, atlas, bluehouse) — each a complete visual system (palette + type + accent) captured from replit.com/slides. Pick one theme, do not mix. For pitch decks, board reports, brand memos, campaign reveals — when the user explicitly wants "Replit Slides style".
Package and build custom AI models with Cog for deployment on Replicate. Use when creating a cog.yaml or predict.py, defining model inputs and outputs, loading model weights at setup time, building Docker images for ML models, serving locally with cog serve or cog predict, or porting a HuggingFace, GitHub, or ComfyUI model to run on Replicate. Trigger on phrases like "build a model", "package a model", "create a Cog model", "wrap a model", "containerize an AI model", "predict.py", "cog.yaml", "BasePredictor", or "Cog container", and when referencing cog.run, github.com/replicate/cog, or github.com/replicate/cog-examples. Covers GPU and CUDA setup, pget for fast weight downloads, async predictors with continuous batching, streaming outputs, and cold-boot optimization for image, video, audio, and LLM models. For pushing built models to Replicate, see publish-models. For running existing models, see run-models.
This skill provides comprehensive guidance for using the Replicate CLI to run AI models, create predictions, manage deployments, and fine-tune models. Use this skill when the user wants to interact with Replicate's AI model platform via command line, including running image generation models, language models, or any ML model hosted on Replicate. This skill should be used when users ask about running models on Replicate, creating predictions, managing deployments, fine-tuning models, or working with the Replicate API through the CLI.
Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.
Prompting techniques for AI image generation and editing models on Replicate. Use when writing prompts for image models or building image generation features.
Push and publish custom AI models to Replicate, and set up CI/CD for releasing new model versions safely. Use when running cog push, deploying a model to Replicate, releasing a new version, validating a model with cog-safe-push before publishing, configuring a Replicate deployment, setting up GitHub Actions for model releases, or porting a community model to an official one. Trigger on phrases like "push a model to Replicate", "publish a model", "deploy a model", "release a new version", "cog push", "cog-safe-push", "model CI", "r8.im", or "schema compatibility", and when referencing github.com/replicate/cog-safe-push or github.com/replicate/model-ci-template. Covers cog push, the full cog-safe-push config (test cases, fuzz, deployment, official_model), GitHub Actions patterns, multi-model matrix pushes, and post-publish monitoring. Assumes you already have a working Cog project; see build-models if you need to package one first.
Create and manage Tavus replicas (AI digital twins). Use when training custom replicas from video, listing stock replicas, or managing replica assets. Covers training video requirements, consent statements, and the Phoenix-3 model.
Receive and verify Replicate webhooks. Use when setting up Replicate webhook handlers, debugging signature verification, or handling prediction events like start, output, logs, or completed.
Operate and evolve agent-memory-workbench with replay-first memory, minimal JSON edits, and a strict two-branch policy (normal + human-verification).