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Found 1,287 Skills
How an AI agent plans, builds, and deploys a complete Ethereum dApp. The three-phase build system for Scaffold-ETH 2 projects. Use when building a full application on Ethereum — from contracts to frontend to production deployment on IPFS.
Conducts comprehensive backend design reviews covering API design quality, database architecture validation, microservices patterns assessment, integration strategies evaluation, security design review, and scalability analysis. Evaluates API specifications (REST, GraphQL, gRPC), database schemas, service boundaries, authentication/authorization flows, caching strategies, message queues, and deployment architectures. Identifies design flaws, security vulnerabilities, performance bottlenecks, and scalability issues. Produces detailed design review reports with severity-rated findings, architecture diagrams, and implementation recommendations. Use when reviewing backend system designs, validating API specifications, assessing database schemas, evaluating microservices architectures, reviewing integration patterns, or when users mention backend design review, API design validation, database design review, microservices assessment, or backend architecture evaluation.
Lists TrueFoundry workspaces and clusters. Provides workspace FQNs for deployment, cluster connectivity status, available GPU types, and base domains.
Use when creating a new beo skill, editing an existing beo skill, or pressure-testing a beo skill before deployment. This skill should win whenever the task is to make a beo skill robust against rationalization, misuse, or failure under pressure. Do not use it for project-specific AGENTS.md conventions, one-off solutions, or ordinary feature planning.
Production-safe Drizzle migration workflow for schema changes that require data backfills or constraint tightening. Use when changing enums/check constraints/defaults, removing status values, or sequencing custom and generated migrations in Drizzle. Trigger on requests about Drizzle migration safety, deployment-safe backfills, migration ordering, and rollback planning.
Deploy Alibaba Cloud official tech solutions. Trigger when the user mentions an Alibaba Cloud solution, pastes a solution URL (aliyun.com/solution/tech-solution/...), or wants to deploy an official solution template. Covers both Terraform module deployment and CLI step-by-step execution paths.
Use when managing NixOS systems — rebuilding, configuring, deploying, installing, or building images. Covers flakes, modules, secret management, VM management, disk imaging, remote deployment, and common anti-patterns to avoid.
Orchestrates Android development tasks including project creation, deployment, SDK management, and environment diagnostics using the `android` command-line tool.
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.
Scans any project repository and generates a "Source of Truth" documentation set in the core-knowledge folder, covering architecture, business logic, feature flags, deployment, and any cloud/serverless integrations.
Navigate the Hermes Agent ecosystem — skills, tools, integrations, deployment, and multi-agent orchestration resources