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Found 899 Skills
End-to-end protocol replay toolkit for ChatGPT Plus/Team/Pro subscription with hCaptcha visual solver and anti-fraud empirical research
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.
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.
Replit Slides 八套主题 (helix/holm/vance/bevel/world/atlas/bluehouse)
Replace OOTB (out-of-the-box) B2B Commerce components with open source equivalents in site metadata content.json files, or look up the equivalent open code `site:` component for OOTB definitions. Use when users mention "replace OOTB components", "replace commerce components with open code", "swap OOTB for open source", "replace commerce_builder:", "replace OOTB in site", "replace component in site metadata", "replace component definition", "find open code equivalent", "equivalent open code component", "OOTB to open code mapping", "what is the site component for", components "in this view" or "for a given view", or a specific list of component names — and want to update or only discover mappings in their store metadata.
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.
Replay a HAR file as a mock backend to reproduce frontend performance issues with production data. Use when asked to replay a HAR file, reproduce a dashboard with a HAR, or test frontend performance with captured traffic.
Iteratively improves a PR until Greptile gives it a 5/5 confidence score with zero unresolved comments. Triggers Greptile review, fixes all actionable comments, pushes, re-triggers review, and repeats. Use when the user wants to fully optimize a PR against Greptile's code review standards.
Reconstruct a reference slide image into an editable PowerPoint using DeckKit, route-aware bbox JSON, optional browser Workbench review, lucide/icon semantic reconstruction, source crops, and image-generation prompts for hard bitmap assets.
Analyze an in-progress git branch, compare it with the current master/main using a subagent, derive practical lessons, and generate a concise redo handoff. Use when restarting a messy branch, redoing work cleanly, extracting lessons from current changes, or preparing another agent to verify the handoff, align with the user, and rebuild from the default branch.
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.