Results for “python”

Scans a DICOM series folder to extract header metadata and produce a preflight verdict without decoding pixel data.

Design, simulate, and run quantum circuits on quantum computers and simulators using Google's Cirq framework.

Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API.

Build voice AI agents with natural conversations, multiple LLM providers, custom tools, and easy web embedding.

Instrument LLM applications with OpenInference tracing for Phoenix AI observability, covering setup, custom spans, and production deployment.

Train and use SentencePiece tokenizers for multilingual NLP, supporting BPE and Unigram algorithms with raw Unicode text.

Extract structured data from LLM responses with Pydantic validation, automatic retries, and streaming support across multiple providers.

Scales Python ML workloads across clusters using Ray's distributed tasks, actors, data, and serving capabilities.

Converts documentation websites, GitHub repositories, and PDF files into AI-ready skills for multiple LLM platforms.

Design, simulate, and run quantum circuits on simulators and quantum hardware using Google's Cirq framework.

Computes the R2Score metric using torchmetrics, handling single and multi-output predictions with options for adjusted and variance-weighted scores.

Computes the Anderson-Darling test statistic and p-value using scipy.stats.anderson for evaluating predictions against ground truth.

TIGER v2 — Multi-scanner trading system for Hyperliquid perps via Senpi MCP. 5 signal patterns (BB compression breakout, BTC correlation lag, momentum breakout, mean reversion, funding rate arb), DSL v4 trailing stops, goal-based aggression engine, and risk guardrails. Configurable profit target over deadline. 12-cron architecture (10 TIGER + prescreener + ROAR meta-optimizer). Pure Python analysis. Requires Senpi MCP, python3, mcporter CLI, and OpenClaw cron system.

Scale pandas and NumPy workflows to larger-than-memory datasets using parallel and distributed computing.

Integrates Dify with external systems via REST APIs, webhooks, and the MCP protocol, including client and server implementations.

Provides guidance on using the Aeon library for time series forecasting, covering model selection, implementation, and evaluation.

Write, debug, and test NumPyro probabilistic programs on JAX with correct shapes, PRNG keys, and inference choice.

Access AWS Bedrock foundation models for generative AI, including text generation, embeddings, and image generation, with CLI and Python examples.

Finds the open GitHub PR for the current branch and helps address review comments using the gh CLI.

Guides building original LangGraph agents for Warden Protocol, from setup to deployment and publishing in Warden Studio.

Design, simulate, and run quantum circuits on simulators and real quantum hardware using Google's Cirq framework.

Reference for building Tavily-powered search, extraction, crawling, and research into agentic workflows and RAG systems.

Build LLM-powered applications with modular components for chains, agents, memory, and retrieval, supporting Python and JavaScript frameworks.

Create publication-quality statistical graphics directly from tabular datasets, covering relational, distribution, categorical, regression, and matrix plots with minimal code.

Instrument LLM applications with Langfuse for tracing, prompt versioning, evaluation, and dataset management across Python and JavaScript SDKs.

Financial analyst agent skill and plugin for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Ratio analysis, DCF valuation, budget variance, rolling forecasts. 4 Python tools (stdlib-only).

Generates, remixes, polls, lists, downloads, and deletes Sora videos via OpenAI's video API using a bundled CLI.

This skill should be used when the user wants to "package an MCP server", "bundle an MCP", "make an MCPB", "ship a local MCP server", "distribute a local MCP", discusses ".mcpb files", mentions bundling a Node or Python runtime with their MCP server, or needs an MCP server that interacts with the local filesystem, desktop apps, or OS and must be installable without the user having Node/Python set up.

Use for writing, reviewing, debugging, testing, or diagnosing Python Bayesian models built directly with PyMC, including Model, coords/dims, Data, random variables, potentials, posterior sampling, prior/posterior predictive checks, and InferenceData output. Trigger on model geometry, shape errors, divergences, sampler choice, mutable prediction data, and probabilistic validation. Do not use for Bambi formula models, NumPyro/JAX programs, ArviZ-only analysis of existing draws, deterministic optimization, or general statistics without PyMC code.

Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.

Create publication-quality statistical graphics from tabular datasets with minimal code, supporting multivariate analysis, statistical estimation, and complex multi-panel figures.

Create publication-quality statistical graphics with dataset-oriented plotting, multivariate analysis, and automatic statistical estimation using minimal code.

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow.

Build LLM-powered applications with agents, chains, and RAG using a framework that supports multiple providers and 500+ integrations.

Guides the creation of MCP servers that enable LLMs to interact with external services through well-designed tools.

Designs collaborative AI agent teams with CrewAI, covering agent roles, task decomposition, crew orchestration, process types, memory, and flows.