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Found 966 Skills
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.
Generate audio replies using TTS. Trigger with "read it to me [URL]" to fetch and read content aloud, or "talk to me [topic]" to generate a spoken response. Also responds to "speak", "say it", "voice reply".
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.
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.
Collect replies from Twitter/X — reply text, author, timestamp, thread context. Use when the user wants to collect reply threads for discussion analysis.
Use when a developer wants to iterate on ONE specific Agent Observability / LLM Obs trace whose output they didn't like — re-running that trace against their LOCAL code, seeing a concise diff of the old vs new output, and looping (change code → replay → diff) until satisfied. Invoked as /agent-observability-replay-trace <trace-id> [changes to test]. Signals: "replay this trace"; "iterate on a trace"; "this trace's output is wrong, fix it and re-run"; "re-run trace <id> with <change>"; pasting a trace id from the Agent Observability UI with a description of what to fix. It fetches the trace via the datadog-llmo MCP or the pup CLI, edits code, re-runs the app to emit a NEW trace, and diffs the two — no local server, no browser. For agents traced with ddtrace / LLM Obs (Python first-class), with JSON-serializable entry input. Do NOT use for: scored Experiments or the browser "Replay" button (that's agent-observability-replay-experiment), building an experiment from a dataset/CSV, writing evaluators, root-causing failed traces, or RUM/HTTP session replay.
Use when the user wants to step through a feature's checkpoints chronologically, pausing at each step to ask questions. Triggers on phrases like "replay <feature>", "walk me through how X was built", "show me the journey of", "step me through how Y was implemented", and "replay the last week"
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.