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Found 1,778 Skills
Operational prompt engineering for production LLM apps: structured outputs (JSON/schema), deterministic extractors, RAG grounding/citations, tool/agent workflows, prompt safety (injection/exfiltration), and prompt evaluation/regression testing. Use when designing, debugging, or standardizing prompts for Codex CLI, Claude Code, and OpenAI/Anthropic/Gemini APIs.
Helps users discover and apply shared coding solutions when they ask "has anyone solved this", "search for a fix", "find a workaround", or want proven patterns before debugging from scratch. Uses `npx shareful-ai search` to find relevant shares, compare options, and recommend the best match.
Pinia Colada expert for Vue 3 — queries, mutations, keys, invalidation, optimistic updates, pagination, and debugging.
Expert guidance for writing fast, maintainable Minitest tests in Rails applications. Use when writing tests, converting from RSpec, debugging test failures, improving test performance, or following testing best practices. Covers model tests, policy tests, request tests, system tests, fixtures, and TDD workflows.
Instrument Python LLM apps, build golden datasets, write eval-based tests, run them, and root-cause failures — covering the full eval-driven development cycle. Make sure to use this skill whenever a user is developing, testing, QA-ing, evaluating, or benchmarking a Python project that calls an LLM, even if they don't say "evals" explicitly. Use for making sure an AI app works correctly, catching regressions after prompt changes, debugging why an agent started behaving differently, or validating output quality before shipping.
Expert guidance for developing cross-platform desktop applications with Avalonia UI framework. Use when building, debugging, or optimizing Avalonia apps including MVVM architecture, XAML design, data binding, styling, theming, custom controls, and cross-platform deployment for Windows, macOS, Linux, iOS, Android, and WebAssembly.
Check GitHub Actions workflow status after git push using gh CLI. Reports CI status, identifies failing jobs, and suggests local reproduction commands. Use after "git push", when user asks about CI status, workflow failures, or build results. Use for "check CI", "workflow status", "actions failing", or "build broken". Do NOT use for local linting (use code-linting), debugging test failures locally (use systematic-debugging), or setting up new workflows.
Reference skill for Zoom REST API. Use after choosing an API-based workflow when you need endpoint selection, resource-management patterns, OAuth requirements, rate-limit awareness, or API error debugging.
Access PUDL table data plus table/column/source metadata in Jupyter or Marimo notebooks for debugging and visualization. Use when users ask what a table contains, how to read it, or how columns are defined.
Code quality orchestrator enforcing TRUST 5 validation, proactive code analysis, linting standards, and automated best practices. Use when performing code review, quality gate checks, lint configuration, TRUST 5 compliance validation, or establishing coding standards. Do NOT use for writing tests (use moai-workflow-testing instead) or debugging runtime errors (use expert-debug agent instead).
Cloudflare Workers observability with logging, Analytics Engine, Tail Workers, metrics, and alerting. Use for monitoring, debugging, tracing, or encountering log parsing, metric aggregation, alert configuration errors.
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.