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Found 197 Skills
Vapor framework guardrails, patterns, and best practices for AI-assisted development. Use when working with Vapor projects, or when the user mentions Vapor. Provides Fluent ORM, async Swift, routing, middleware, and server-side Swift guidelines.
Micronaut framework guardrails, patterns, and best practices for AI-assisted development. Use when working with Micronaut projects, or when the user mentions Micronaut. Provides compile-time DI, HTTP server/client, data access, and cloud-native guidelines.
TypeScript/JavaScript guardrails, patterns, and best practices for AI-assisted development. Use when working with TypeScript (.ts, .tsx) or JavaScript (.js, .jsx) files, package.json, or tsconfig.json. Provides strict mode conventions, async patterns, testing standards, and module system guidelines.
Operate CoinGecko and GeckoTerminal market data APIs through UXC with a curated OpenAPI schema, API-key auth, and read-first guardrails.
Operate Matrix Client-Server API through UXC with a curated OpenAPI schema, bearer-token auth, and homeserver-aware messaging guardrails.
Create validated LLM-as-a-Judge evaluators following best practices — binary Pass/Fail judges with TPR/TNR validation for measuring specific failure modes. Use when you need to automate quality checks, build guardrails, or measure a specific failure mode identified during trace analysis. Do NOT use when failures are fixable with prompt changes (use optimize-prompt) or when failure modes are unknown (use analyze-trace-failures first).
Discover, find, compare, audit, repair, adapt, and design repeatable AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs. Use when a user asks to analyze a codebase for potential loops, mine coding-thread history for work done more than once, turn repeated engineering work into a loop, find or recommend a published loop, create a recurring agent workflow or automation cadence, turn an outcome into a bounded copy-ready loop, or review an existing loop for weak checks, unsafe authority, unbounded repetition, stale state, or unclear stopping behavior.
Intercept the AG2 agent loop with `BaseMiddleware` — wrap full turns (`on_turn`), each LLM call (`on_llm_call`), each tool execution (`on_tool_execution`), or each human-input request (`on_human_input`). Use for retry, logging, history trimming, request mutation, tool auditing, guardrails, or rate limiting. Built-ins: `LoggingMiddleware`, `RetryMiddleware`, `HistoryLimiter`, `TokenLimiter`, `TelemetryMiddleware` (see `ag2-telemetry`). For per-tool hooks see also `ag2-add-custom-tool` tool-middleware section.
Enforce modern, natural copywriting guardrails and rewrite copy to avoid staccato contrast, slogan fragments, and repeated adjective stacks. Use when drafting or rewriting marketing, product, website, or UX copy, including headlines, taglines, CTAs, and short brand statements, especially when the input includes contrasty sentence patterns that need smoothing.
Implementing safety filters, content moderation, and guardrails for AI system inputs and outputs
Use when planning A/B tests in LaunchDarkly, Optimizely, or similar platforms. Sizes the experiment (sample size, MDE, runtime), drafts hypothesis + success metrics + guardrails, and produces a launch checklist + rollback plan.
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.