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Found 2,020 Skills
Comprehensive Rust coding guidelines covering ownership, error handling, async patterns, traits, testing, performance, clippy, and documentation. Use when writing new Rust code, reviewing or refactoring existing Rust, implementing async systems with Tokio, designing error hierarchies, choosing between borrowing and cloning, setting up tests or benchmarks, configuring linting, or optimizing performance. Do not use for non-Rust languages or general software architecture unrelated to Rust idioms.
Extract comprehensive, production-ready JSON design specifications from visual inputs using a 7-pass serial architecture with cross-validation. Use when converting screenshots, mockups, or design exports into structured design tokens, component specs, accessibility analysis, and developer handoff artifacts.
Create IoT architecture diagrams using PlantUML syntax with device/sensor stencil icons. Best for smart home, industrial IoT (IIoT), fleet management, edge computing, and sensor network diagrams. NOT for general cloud infra (use cloud skill) or simple flowcharts (use mermaid).
Set up or update the agent-first engineering harness for any repository. Implements the complete scaffolding that makes AI coding agents effective: knowledge maps (AGENTS.md as a concise TOC), structured documentation, architecture boundaries, enforcement rules (.harness/*.yml specs), quality scoring, and process patterns for agent-driven development. Use this skill whenever someone wants to make a repo agent-ready, set up AGENTS.md or docs/ structure, define domain boundaries or golden principles, generate .harness/ configuration, audit agent readiness, or update an existing harness. Also trigger when a user reports problems with agent effectiveness, context management, or architectural drift — these are symptoms of a missing or stale harness. Trigger on: "harness this repo", "set up harness", "agent-first setup", "make this agent-ready", "update the harness", "assess agent readiness", "set up AGENTS.md", "organize for agents", or any discussion about structuring a codebase for AI agent workflows.
The foundational context engineering skill — start here when exploring the discipline. This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Also activates when the user mentions "context engineering" or "context-engineering" for foundational understanding of AI agent context systems.
Store and retrieve agent memories across jobs. Enables long-term context, learning from past interactions, and building agent knowledge bases. Based on OpenClaw's memory-core architecture.
Microservice architecture patterns — service decomposition, inter-service communication, API gateway, saga pattern, event-driven architecture, service mesh, circuit breaker, CQRS, event sourcing. Activate on "microservices", "service decomposition", "saga pattern", "API gateway", "event-driven", "service mesh", "circuit breaker", "CQRS", "event sourcing", "bounded context", "strangler fig", "distributed transactions", "choreography vs orchestration". NOT for monolith design, serverless functions, or Kubernetes infrastructure.
Designs production-grade RAG pipelines with chunking optimization, retrieval evaluation, and pipeline architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, designing embedding pipelines, or evaluating RAG performance with RAGAS metrics.
[Pragmatic DDD Architecture] Guide for Authentication configuration and the `auth` Bounded Context. Use when modifying auth flows, adding social providers, configuring email templates/Resend, working with the Better Auth client/server configurations, or modifying `src/auth/` components and components dependent on session handling.
Decomposes a spec or architecture into buildable tasks with acceptance criteria, dependencies, and implementation order for AI agents or engineers. Produces `.agents/tasks.md`. Not for clarifying unclear requirements (use discover) or designing architecture (use system-architecture). For code quality checks after building, see review-chain. For packaging and PRs, see ship.
Prometheus and Grafana Cloud Metrics overview including PromQL query language, Metrics Drilldown, alerting, recording rules, and integration patterns. Use when working with Prometheus, writing PromQL queries, configuring alerting, or discussing metrics architecture and best practices.
In large applications, information architecture determines whether users can find, understand, and act on data. Naming matters. The UI should mirror the data model and signal how data can be transformed. Dangerous or irreversible changes always require a confirm dialog. Use when designing navigation, naming entities, structuring large feature sets, or modelling data-driven UI.