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Found 767 Skills
Build production-ready Web3 applications, smart contracts, and decentralized systems. Implements DeFi protocols, NFT platforms, DAOs, and enterprise blockchain integrations. Use PROACTIVELY for smart contracts, Web3 apps, DeFi protocols, or blockchain infrastructure.
Coordinate multi-layer security scanning and hardening across application, infrastructure, and compliance controls.
Coordinates linters, pre-commit hooks, and test infrastructure setup
Orchestrates full decomposition (scope → Epics → Stories) by delegating ln-210 → ln-220. Sequential Story decomposition per Epic. Epic 0 for Infrastructure.
Guidance for managing R package lifecycle according to tidyverse principles using the lifecycle package. Use when: (1) Setting up lifecycle infrastructure in a package, (2) Deprecating functions or arguments, (3) Renaming functions or arguments, (4) Superseding functions, (5) Marking functions as experimental, (6) Understanding lifecycle stages (stable, experimental, deprecated, superseded), or (7) Writing deprecation helpers for complex scenarios.
Microservice Infrastructure Guide, covering core infrastructure of microservice architecture such as conditional configuration, event-driven architecture, inter-service communication, internationalization and logging. It is used when users implement inter-service calls, configure multi-environments, implement asynchronous communication, handle internationalization or standardize log output.
Write integration tests using TestContainers for .NET with xUnit. Covers infrastructure testing with real databases, message queues, and caches in Docker containers instead of mocks.
Guide for implementing formatting rules using Biome's IR-based formatter infrastructure. Use when working on formatters for JavaScript, CSS, JSON, HTML, or other languages. Examples:<example>User needs to implement formatting for a new syntax node</example><example>User wants to handle comments in formatted output</example><example>User is comparing Biome's formatting against Prettier</example>
Knowledge base for designing, reviewing, and linting agentic AI infrastructure. Use when: (1) designing a new agentic system and need to choose patterns, (2) reviewing an existing agentic architecture ADR or design doc for gaps/risks, (3) applying the lint script to an ADR markdown file to get structured findings, (4) looking up a specific agentic pattern (prompt chaining, routing, parallelization, reflection, tool use, planning, multi-agent collaboration, memory management, learning/adaptation, MCP, goal setting, exception handling, HITL, RAG, A2A, resource optimization, reasoning techniques, guardrails, evaluation, prioritization, exploration/discovery). All rules and guidance are grounded in the PDF "Agentic Design Patterns" (482 pages).
Check and configure UX testing infrastructure (Playwright, accessibility, visual regression)
Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-analysis, write-judge-prompt, or validate-evaluator instead).
Reduce LLM API and infrastructure costs through model selection, prompt caching, batching, caching, quantization, and self-hosting strategies. Track spend by team and model, set budgets, and implement cost-aware routing.