Results for “da”

Automate Stormglass IO operations through Composio's Stormglass IO toolkit via Rube MCP, including tool discovery, connection management, and execution.

Extracts structured news data from Google News via the BrowserAct API, including headlines, sources, publication times, and article links.

Create, read, manipulate, and store annotated data matrices using the AnnData Python package, designed for single-cell genomics and general-purpose annotated data workflows.

Store and process large N-dimensional arrays with chunking, compression, and parallel I/O, integrating with NumPy, Dask, and Xarray for cloud-native scientific computing.

Store and query embeddings with metadata filtering, vector search, and full-text search using an open-source database that scales from notebooks to production.

Numerical computing with MATLAB and GNU Octave for matrix operations, data analysis, visualization, and scientific computing, including script execution and syntax guidance.

Automates scientific research workflows from data analysis to publication, orchestrating multiple agents for hypothesis generation, methodology development, computational experiments, and LaTeX paper writing.

Implements production LLM operations: RAG pipelines, embeddings, vector databases, fine-tuning, advanced prompt engineering, cost estimation, quality evals, semantic caching, streaming, and agents.

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.

Econometrics skill for panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"

Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.

Guides the design of effective, user-friendly, and accessible Power BI reports with optimal chart selection and layout design.

Extracts structured product data from Amazon search results, including prices, ratings, sales estimates, and shipping info, using the BrowserAct API.

Process large-scale ML datasets with distributed streaming execution across CPU/GPU, supporting Parquet, CSV, JSON, images, and integration with PyTorch, TensorFlow, and Ray Train.

Manages Ray clusters, jobs, Serve deployments, and distributed workloads via the Dashboard API and CLI, with discovery-first checks and safety guardrails.

Audits medical LLM benchmarks across five lifecycle phases using 46 medically tailored criteria to assess clinical relevance, data integrity, safety-critical capabilities, validity, and governance.

Analyze an API, schema, migration, or compatibility decision with `analysis-agent`, or implement its bounded contract change with `task-agent`. Do not select it when no data or public-contract surface is affected.

Linkable-Asset Skill — data studies that pull links (Lesson 7)

Civic tech — transparence, open data, participation citoyenne, budgets participatifs

Econometrics skill for panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"

Discovers podcasts via the iTunes Search API, parses RSS feeds, downloads audio, transcribes with OpenAI Whisper, chunks transcripts, and upserts results into a database table.

Syncs key-value preferences and state across multiple agents using a shared TiDB Zero database, with optional auto-provisioning of a free ephemeral database.

Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Scrapes on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions.

Interpreted crypto wallet data for AI agents. Use when an agent needs portfolio values, token positions, DeFi positions, NFT holdings, transaction history, PnL data, token prices, charts, gas prices, swap quotes, or DApp information across 41+ chains. Zerion transforms raw blockchain data into agent-ready JSON with USD values, protocol labels, and enriched metadata. Supports x402 pay-per-request ($0.01 USDC on Base) and API key access. Triggers on mentions of portfolio, wallet analysis, positions, transactions, PnL, profit/loss, DeFi, token balances, NFTs, swap quotes, gas prices, or Zerion.

Provides idiomatic PyTorch patterns and best practices for building robust, efficient, and reproducible deep learning applications, covering model architecture, training loops, data pipelines, and checkpointing.

Designs and reviews feedback loops for AI agents to ensure goals are machine-decidable, loops are damped, and human judgment is preserved.

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining, covering SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.

Provides a standardized interface for ingesting raw data across domains such as genomics, network analysis, document review, and spatial mapping, converting it into semantic vectors for agent use.

Identify a linear dynamic model from input-output or time-series data using MATLAB System Identification Toolbox. Use when estimating transfer function, state-space, ARX, ARMAX, BJ, OE polynomial or process models from measurement data.

Review protected asset policy and risky local data surfaces.

Designs the data model, API contracts, and structural foundation of the system.

Show DAG state, agent progress, and branch status for an AgentHub session.

Scan codebases for known vulnerabilities using OSV, CVE, and GHSA databases.

Design or revise a company's commercial policy governing discounts, approver thresholds, exception flows, and deal frameworks using data-backed matrices and linting tools.

Provides an adaptive methodology for adding machine learning models to existing codebases, covering problem framing, data readiness, architectural decoupling, and baseline model integration.