Topic: claude
14,433 skills in this topic.
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providing-feedback
Implements feedback and notification systems including toasts, alerts, modals, progress indicators, and error states. Use when communicating system state, displaying messages, confirming actions, or showing errors.
ancoleman/ai-design-components 333
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implementing-mlops
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
ancoleman/ai-design-components 333
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using-timeseries-databases
Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.
ancoleman/ai-design-components 333
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using-relational-databases
Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).
ancoleman/ai-design-components 333
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siem-logging
Configure security information and event management (SIEM) systems for threat detection, log aggregation, and compliance. Use when implementing centralized security logging, writing detection rules, or meeting audit requirements across cloud and on-premise infrastructure.
ancoleman/ai-design-components 333
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configuring-nginx
Configure nginx for static sites, reverse proxying, load balancing, SSL/TLS termination, caching, and performance tuning. When setting up web servers, application proxies, or load balancers, this skill provides production-ready patterns with modern security best practices for TLS 1.3, rate limiting, and security headers.
ancoleman/ai-design-components 333
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transforming-data
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
ancoleman/ai-design-components 333
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deploying-applications
Deployment patterns from Kubernetes to serverless and edge functions. Use when deploying applications, setting up CI/CD, or managing infrastructure. Covers Kubernetes (Helm, ArgoCD), serverless (Vercel, Lambda), edge (Cloudflare Workers, Deno), IaC (Pulumi, OpenTofu, SST), and GitOps patterns.
ancoleman/ai-design-components 333
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debugging-techniques
Debugging workflows for Python (pdb, debugpy), Go (delve), Rust (lldb), and Node.js, including container debugging (kubectl debug, ephemeral containers) and production-safe debugging techniques with distributed tracing and correlation IDs. Use when setting breakpoints, debugging containers/pods, remote debugging, or production debugging.
ancoleman/ai-design-components 333
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writing-infrastructure-code
Managing cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-native infrastructure), designing reusable modules, implementing state management patterns, or establishing infrastructure deployment workflows.
ancoleman/ai-design-components 333
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user-management
How to manage the user registry — creating users for new Slack/GitHub/GitLab identities, managing aliases, resolving users across platforms. Use when a new human interacts with the swarm or when user identity needs updating.
desplega-ai/agent-swarm 335
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create-pr
Create a pull request (GitHub) or merge request (GitLab) from the current branch
desplega-ai/agent-swarm 335
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todos
Handle the agent personal todos.md file
desplega-ai/agent-swarm 335
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review-offered-task
Review a task that has been offered to you and decide whether to accept or reject it
desplega-ai/agent-swarm 335
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respond-github
Respond to a GitHub issue/PR or GitLab issue/MR
desplega-ai/agent-swarm 335
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investigate-sentry-issue
Investigate and triage a Sentry error issue
desplega-ai/agent-swarm 335
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start-worker
Start an Agent Swarm Worker
desplega-ai/agent-swarm 335
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start-leader
Start the Agent Swarm Leader
desplega-ai/agent-swarm 335
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swarm-chat
Effective communication within the agent swarm using internal Slack
desplega-ai/agent-swarm 335
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review-pr
Review a pull request (GitHub) or merge request (GitLab) and provide detailed feedback
desplega-ai/agent-swarm 335
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close-issue
Close a GitHub or GitLab issue with a summary comment
desplega-ai/agent-swarm 335
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swarm-local-e2e
Guide for running local E2E tests with API server, Docker lead/worker containers, task creation, log verification, UI dashboard, and cleanup
desplega-ai/agent-swarm 335
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work-on-task
Work on a specific task assigned to you in the agent swarm
desplega-ai/agent-swarm 335
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implement-issue
Implement a GitHub issue or GitLab issue and create a PR/MR
desplega-ai/agent-swarm 335