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Found 2,186 Skills
Complete knowledge domain for Cloudflare Workers AI - Run AI models on serverless GPUs across Cloudflare's global network. Use when: implementing AI inference on Workers, running LLM models, generating text/images with AI, configuring Workers AI bindings, implementing AI streaming, using AI Gateway, integrating with embeddings/RAG systems, or encountering "AI_ERROR", rate limit errors, model not found, token limit exceeded, or neurons exceeded errors. Keywords: workers ai, cloudflare ai, ai bindings, llm workers, @cf/meta/llama, workers ai models, ai inference, cloudflare llm, ai streaming, text generation ai, ai embeddings, image generation ai, workers ai rag, ai gateway, llama workers, flux image generation, stable diffusion workers, vision models ai, ai chat completion, AI_ERROR, rate limit ai, model not found, token limit exceeded, neurons exceeded, ai quota exceeded, streaming failed, model unavailable, workers ai hono, ai gateway workers, vercel ai sdk workers, openai compatible workers, workers ai vectorize
Universal ChromaDB integration patterns for semantic search, persistent storage, and pattern matching across all agent types. Use when agents need to store/search large datasets, build knowledge bases, perform semantic analysis, or maintain persistent memory across sessions.
Comprehensive test execution with parallel analysis and coverage reporting. Use when running test suites or troubleshooting failures with the run-tests workflow.
Anthropic's Contextual Retrieval technique for improved RAG. Use when chunks lose context during retrieval, implementing hybrid BM25+vector search, or reducing retrieval failures.
Use when `spec.md`, `plan.md`, and `tasks.md` exist and you need a read-only Spec Kit audit for consistency, requirement-to-task coverage, ambiguity, duplication, or constitution conflicts before implementation.
Use when administering Proxmox VE hosts, creating and managing VMs with qm, managing LXC containers with pct, configuring storage, networking, clusters, and automating provisioning tasks via the Proxmox CLI.
Document Q&A with RAG using Supabase pgvector store.
Generates comprehensive unit tests with AAA pattern (Arrange-Act-Assert), edge cases, error scenarios, and coverage analysis. Creates test files matching source structure with complete test suites. Use for "unit testing", "test generation", "Jest tests", or "test coverage".
Map papers from the core set to each outline subsection and write `outline/mapping.tsv` with coverage tracking. **Trigger**: section mapper, mapping.tsv, coverage, paper-to-section mapping, 论文映射, 覆盖率. **Use when**: structure 阶段（C2），已有 `papers/core_set.csv` + `outline/outline.yml`，需要确保每小节有足够支持论文再进入 evidence/writing。 **Skip if**: 还没有 outline（先跑 `outline-builder`）或 core set 还没收敛。 **Network**: none. **Guardrail**: 覆盖率可审计（避免所有小节重复用同几篇）；为弱覆盖小节留下明确补救方向（扩 query / 合并小节）。
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development.
Modern JavaScript/TypeScript testing with Vitest including mocking and coverage.
Daily compression of time-series data with merge logic for multiple pipeline runs, structured aggregation for dashboards, and storage estimation for capacity planning.