后端与 API

后端服务、API、Webhook 和服务端工具

816 个 Skills 可用

显示 24 个结果

usfiscaldata

Query the U.S. Treasury Fiscal Data REST API for federal financial data. No API key required. Use for national debt (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates, foreign exchange rates, savings bonds, or U.S. government revenue and spending statistics.

k-dense-ai

获取

pymc

42Kbackend-api

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

k-dense-ai

获取

torch-geometric

42Kbackend-api

PyTorch Geometric (PyG) for graph neural networks — node/link/graph classification, message passing (GCN, GAT, GraphSAGE, GIN), heterogeneous graphs, neighbor sampling, and custom datasets. Use when working with torch_geometric, not for general NetworkX analytics or non-graph PyTorch models.

k-dense-ai

获取

networkx

41Kbackend-api

Create, analyze, and visualize complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks (random, scale-free, small-world), reading/writing graph file formats, or drawing network topologies. Common applications include social, biological, transportation, and citation networks.

k-dense-ai

获取

get-available-resources

41Kbackend-api

Detect host inventory and effective CPU, memory, disk, scheduler, container, and accelerator limits when a user asks for resource-aware planning or before a clearly resource-sensitive local workload. Produces a redacted JSON snapshot and conservative planning helpers without stress tests or assuming visible host hardware is usable.

k-dense-ai

获取

optimize-for-gpu

41Kbackend-api

GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.

k-dense-ai

获取

phoenix-cli

39Kbackend-api

Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.

后端与 API

Skills 列表

usfiscaldata

pymc

torch-geometric

networkx

get-available-resources

optimize-for-gpu

phoenix-cli

integrate-context-matic

pptx

nature-image2ppt

codeql

issue-fields-migration

azure-pricing

noob-mode

azure-resource-visualizer

typespec-api-operations

copilot-usage-metrics

typespec-create-api-plugin

containerize-aspnet-framework

project-workflow-analysis-blueprint-generator

dataverse-python-advanced-patterns

technology-stack-blueprint-generator

azure-static-web-apps

dataverse-python-production-code