microsoft-foundry
microsoft/azure-skillsGuides building, deploying, evaluating and managing Microsoft Foundry agents, models and resources end to end.
Most installed first. Copy the install command into a terminal; Claude Code loads the skill when a task needs it.
Guides building, deploying, evaluating and managing Microsoft Foundry agents, models and resources end to end.
Routes requests for Azure AI Search, Speech, OpenAI and Document Intelligence to the matching tools and steps.
Gives the agent ADK code patterns for agent types, tools, callbacks, state and graph workflows.
Guides building ADK agents end to end: scaffold, build, evaluate, deploy, publish and observe.
Creates and upgrades agent projects with agents-cli, adding deployment and CI/CD setup.
Adds Firebase AI Logic to mobile and web apps so client code can call Gemini for text, chat and streaming.
Guides building MCP servers in Python or TypeScript that wrap external APIs as well-designed tools.
Finds and labels LLM workflows in a repo so gateway spend can be grouped by job, after user approval.
Guides building with TypeSafe's Jev, turning LLM prompts and heuristics into typed, calibrated questions.
Reference for building with the Claude API and SDK, covering models, tool use, caching and streaming.
Guides building AI features with the Vercel AI SDK, checking APIs against the docs of the installed version.
Runs a cross-model benchmark comparing Claude, GPT and Gemini on a skill or task.
Guides the agent through designing agents: capabilities, knowledge, context and a simple tool-use loop.
Builds real-time voice AI agents on ElevenLabs, covering setup, prompts, tools, and web embedding.
Guides building an MCP server that serves interactive UI widgets rendered inline in chat hosts.
Guides MCP server design for Claude: picks deployment model and tool pattern before any scaffolding.
Defines custom DataPoint node and edge models so cognee's LLM extraction builds a typed knowledge graph.
Makes the agent write custom cognee tasks, chain them into pipelines, and store custom DataPoints.
Makes the agent find and safely delete specific documents, datasets, or derived memory in cognee.
Makes the agent use cognee session memory and improve() to store turns and bridge them into the graph.
Makes the agent ingest text, files, URLs and repos into cognee with remember(), choosing extractors.
Makes the agent install the cognee Python SDK and run a first remember and recall example.
Tunes cognee ingestion and reads for speed, rate limits and cost: batching, chunk size, dry-run estimates.
Sets up cognee users, roles and dataset sharing, and explains how permissions and isolation are enforced.
Queries cognee memory with recall(), choosing search types, dataset scope and reading results or citations.
Loads a company's SQL database, tickets and docs into cognee and answers questions across all three.
Lets the container agent call local models served by the Atomic Chat desktop app through MCP tools.
Guides evaluation of agents and models with datasets, LLM-as-judge metrics and failure analysis.
Teaches LLM-as-judge methods: direct scoring, pairwise comparison, rubric calibration and bias control.
Guides project-level LLM decisions: task fit, pipeline shape, cost estimates, and single versus multi-agent choice.
Writes one optimized, paste-ready prompt for a named AI tool, such as an LLM, Cursor or Midjourney.
Guides changes to Xberg's text chunking config, chunker types and embedding generation for RAG pipelines.
Splits extracted text into chunks for LLM context or RAG, sized by characters or tokens, with overlap.
Turns a parsed document into sourced fact, explanation, risk and action cards for review.
Rewrites expert prompts into a portable five-part format for reuse across different AI assistants.
Gets typed classifications, scores, and calibrated probabilities from a decision model via the tai CLI.
Diagnoses whether an AI workflow suffers from context stuffing and guides structured context design fixes.
Guides 0G Compute SDK calls for decentralized AI inference, fine-tuning and response handling.
Guides design of n8n AI agent, chain and classifier nodes covering tools, memory, output parsing and RAG.
Exposes SenseNova image generation, editing, VLM recognition and LLM text tools as raw backend calls.
Guides wiring the Agentica SDK through a local proxy that calls the Claude CLI with tool permissions.
Reference for Agentica's multi-agent pattern classes, core components and coordination APIs.
Explains how to write prompts for Agentica REPL agents so they follow instructions reliably.
Shows how to build Python agents with the Agentica SDK using agentic functions, spawn and tools.
Sets up the Agentica server with a Claude proxy and explains the startup order and debugging steps.
Picks and launches Agentica multi-agent patterns such as swarm, hierarchical, generator-critic and jury.
Makes the agent write PinMe Worker code that calls OpenRouter models and chat completions via a proxy.
Adds an LLM provider to RedAmon while keeping API keys out of scan containers and model ids prefix-routed.
Builds and queries a citation-traceable knowledge wiki from documents to retrieve evidence for tasks.
Guides Fireworks AI inference: endpoints, keys, model ids, reasoning output quirks and tool calling.
Builds MCP servers with interactive React UIs using the Leanmcp UI SDK decorators and components.
Gives the agent an indexed list of Langroid multi-agent design patterns with reference docs to consult.
Guides the agent in building MCP servers that expose tools, resources and prompts to Claude.
Creates structured prompts for multi-stage Claude pipelines with research, planning and execution stages.
Guides building MCP servers in Python FastMCP or TypeScript with well-designed, agent-friendly tools.
Answers questions across a video library by semantic search, citing each claim with its video and timestamp.
Guides building evaluation systems for AI products using human, code-based and LLM-as-judge methods.
Adds an LLM provider with call and stream methods, wired into the provider factory and usage tracking.
Helps choose and deploy on-device AI on Apple devices with Foundation Models, Core ML, MLX Swift or llama.cpp.
Guides building on-device Apple Intelligence features with Foundation Models, structured output, tools and evals.
Guides the agent through calling AWS Bedrock foundation models, creating embeddings and building RAG.
Maintains this repo's local MCP server, covering tool domains, safety guards, host config and runtime troubleshooting.
Repeats prompts for Haiku and other non-reasoning models to improve accuracy on structured tasks like tests and parsing.
Guides a founder to scope, launch, grade and schedule a Claude Managed Agent in their own account.
Recaps a finished Claude Managed Agent build, shows its resources and status, and suggests next steps.
Extracts each independently changeable claim from a conversation window as a record with subject, time and status.
Judges which offered memory rows a nominated statement retires, by meaning rather than shared words.
Judges which older memory facts a newer record supersedes and rewrites the facts that remain current.
Judges whether a stored row states a retired profile position as current, by meaning.
Judges whether a memory completes, removes or opens an active task, by meaning rather than wording.
Makes the agent pick one offered state attribute slug for a stored statement, or null when none fits.
Makes the agent match a new display name to an existing entity id, or answer new when unsure.
Audits and redesigns AI-generated feedback for pedagogical quality, timing and learning impact.
Runs a question across several AI providers, shows each answer verbatim, and writes a consensus synthesis.
Runs parallel Claude analyst agents across providers and returns schema-checked analyses with confidence ratings.
Convenes independent Claude subagents, each with one lens, and synthesizes their views as a same-model panel.
Guides production LLM features: validated outputs, RAG, guardrails, streaming and cost control.
Guides building tool-using AI agents with Pydantic AI in Python and the Claude Agent SDK in Node.js.
Reference of current AI model names, specs and picking tips for Claude, OpenAI, Gemini and others.
Builds chat and tool-calling agents on the Convex agent component with threads, streaming, and RAG.
Builds a small eval set with checkable pass criteria to test whether an agent, prompt or skill works and catches regressions.
Covers advanced AgentDB use: QUIC sync, multiple databases, custom distance metrics and hybrid vector search.
Implements persistent agent memory with AgentDB for session history, long-term storage and context management.
Recommends chunk sizes, overlap and boundary methods for RAG and checks retrieval precision and recall.
Guides writing and optimizing LLM prompts with few-shot examples, chain-of-thought and system prompt design.
Guides building RAG pipelines with document chunking, embeddings, vector storage and retrieval.
Guides designing agent-facing tools and CLIs so parameters and descriptions teach correct use.
Scores and rewrites a prompt using 31 named frameworks, asking clarifying questions when intent is unclear.
Manages JeecgBoot AIFlow LLM flows from natural language: create, edit, query, debug, publish, delete.
Ranks free OpenRouter models and sets fallbacks in openclaw.json to handle rate limits.
Enforces schema-defined structured outputs, validation, and fallbacks at AI pipeline boundaries.
Guides the agent to record session events and search long-term memories using the Redis Agent Memory SDKs.
Has the agent check its own answers by generating and answering verification questions before revising.
Guides using the Jev model for narrow typed judgments on natural language, such as classify, route, or score.
Classifies a task by type and complexity and routes it to the most suitable LLM.
Gives the agent reference guidance for the neo4j-agent-memory package and its hosted memory service.
Creates, configures, and invokes Neo4j Aura agents through the v2beta1 REST API.
Builds, audits, repairs, and deploys Salesforce Agentforce agents written in Agent Script.
Captures smoke test or inference run evidence into standardized repro_outputs files.
Sets up a conservative conda env and records checkpoint, dataset and cache paths for a repro target.
Runs candidate-only deep learning research exploration with fair comparisons and auditable outputs.
Read-only analysis of deep learning repos: model structure, entrypoints and configs, without modifying code.
Reproduces a deep learning repo README-first with minimal, evidence-logged runs and documented deviations.
Implements exploratory model changes on an isolated branch with rollback-aware records.
Runs a selected training command for startup or short-run checks, or resume, logging evidence.
Guides time series ML with aeon for classification, regression, forecasting, anomaly detection and search.
Queues and runs batches of ML experiments on SSH GPU servers with OOM retry and wave scheduling.
Builds author-voice SFT datasets from books: segmentation, prompt templates, LoRA training and evaluation.
Guides LLM fine-tuning with Axolotl YAML configs, covering LoRA, QLoRA, DPO, KTO, ORPO, GRPO and FSDP.
Guides large-scale LLM pretraining with TorchTitan across many GPUs using 4D parallelism and checkpointing.
Guides GRPO reinforcement learning fine-tuning with TRL, including custom reward functions.
Guides fast HuggingFace tokenization, custom vocabulary training and text-to-token alignment tracking.
Guides loading, training and LoRA or QLoRA fine-tuning of LLMs with LitGPT's single-file architectures.
Explains and applies Mamba selective state-space models for linear-time sequence modeling and fast inference.
Guides large-scale RL training of MoE models with miles, covering FP8/INT4 and train-inference alignment.
Guides training a minimal GPT-2 style model with nanoGPT on Shakespeare or OpenWebText data.
Guides parameter-efficient fine-tuning with PEFT, LoRA and QLoRA for large models on limited GPU memory.
Guides using RWKV, an RNN-style transformer hybrid with linear inference cost, for training and generation.
Guides SentencePiece training and use for language-independent BPE or Unigram tokenization on raw text.
Guides training and analyzing sparse autoencoders with SAELens to find interpretable model features.
Guides mechanistic interpretability with TransformerLens: activation caching, hooks and activation patching.
Guides choosing and implementing ML.NET, Microsoft.Extensions.AI, ONNX or local LLM features in .NET 8+.
Guides scikit-learn workflows for classification, regression, clustering, tuning and pipelines.
Guides ML modeling with baselines, evaluation, overfitting diagnosis, and critical review of your approach.
Creates and manages dstack inference-optimization presets, pointing to external dstack docs for syntax.
Guides choosing and diagnosing a fine-tuning method such as SFT, DPO, or RL based on reward shape.
Runs LLMs through 60+ academic benchmarks such as MMLU, GSM8K and HumanEval using a standard harness.
Adds multi-GPU and mixed precision training to PyTorch scripts via Hugging Face Accelerate's unified API.
Generates a runnable Python experiment skeleton with data, models, training and evaluation from a blueprint.
Guides MATLAB tabular classification: prepare data, train classifiers, and compare accuracy with statistical tests.
Deploys neural networks to embedded targets with MATLAB, Simulink and Embedded Coder code generation.
Selects and engineers features for single-response MATLAB classification or regression models.
Imports PyTorch, ONNX and Keras models into MATLAB as dlnetwork objects and checks numerical match.
Explains tabular MATLAB models using feature importance and per-prediction explanations.
Trains, evaluates and exports MATLAB neural networks with current dlnetwork APIs and migrates legacy code.
Tunes configurable parameters against a measured score by reading the project, proposing trials and recording results.
Guides training and prompt optimization of AI agents with Microsoft's Agent Lightning using tracing and RL.
Compares training runs at the same epoch across trackers, separating proxy metrics from downstream targets.
Analyzes dataset class balance, bias and fairness, and plans stratified samples or data collection.
Trains a LoRA on fal's MiniMax H3 from captured motions, then A/B tests it against plain H3.
Renders labelled poses for one creature, fine-tunes a DINOv2 joint reader and checks its held-out error.
Creates and trains agent learning plugins with AgentDB's reinforcement learning algorithms such as Q-Learning.
Applies Andrej Karpathy's mental models to neural net training, ML debugging and LLM app design.
Applies Andrew Ng's views on task-level automation, data-centric ML and cheap AI prototyping.
Applies Christopher Manning's view of NLP as a domain science, with modular design and adaptable models.
Applies David Silver's reinforcement learning view: self-play, search, and learning from experience over human data.
Guides adding the open-source Laya model to classify, route, score or guard text without an LLM call.
Runs Meta's SAM 3 locally on an NVIDIA GPU to mask, outline, cut out or count objects in images.
Explains where each agent stores logs, how records map to tokens and costs, and which reports each agent supports.
Installs Fluid in KubeSphere and manages Datasets, cache runtimes, DataLoad warming and troubleshooting.
Guides GPU-accelerated LLM training data curation: deduplication, quality filtering and PII redaction.
Guides distributed ML data processing with Ray Data: streaming preprocessing and batch inference.
Sets cleaning rules, deduplicates and standardises messy tables, and logs every change in an Excel workbook.
Describes how Xberg's Office, PDF and archive extractors parse files within security budgets.
Covers Xberg OCR backends, preprocessing, caching, page acceptance and cross-backend quality rules.
Explains how Xberg picks the native or Pdfium PDF backend and its rendering and OCR fallback.
Parses PDF, Word and Excel files into Markdown with metadata and a confidence score, using fallback parsers.
Migrates an ArcGIS Hub open data site to PortalJS as PMTiles and GeoParquet with a parity report.
Adds DCAT catalog feeds in JSON-LD, Turtle and RDF/XML to a PortalJS portal so open-data portals can harvest it.
Converts a geo file into PMTiles and GeoParquet, uploads them to R2, and registers the dataset in a PortalJS portal.
Adds a second file, such as a data dictionary or extra period data, to an existing PortalJS dataset.
Turns human-approved, redacted agent runs into local traces, eval cases, and training-ready JSONL files.
Maps dbt model and source YAML, including column tests, into ktx semantic-layer overlays and column notes.
Turns one historic SQL table usage file into a concise usage summary recorded as table usage evidence.
Classifies and resolves conflicts found during bundle ingest, such as duplicates, contradictions and evictions.
Turns a live database schema snapshot into semantic-layer sources, joins and wiki notes from table comments.
Guides the agent to inspect unknown data files by magic bytes and encoding, then parse them safely.
Runs PySpark or Python on Fabric Spark through temporary Livy sessions without creating a notebook.
Builds data pipelines for AI systems: RAG ingestion, embeddings, feature stores and orchestration.
Guides data platform design: lake vs warehouse vs lakehouse, data modeling, medallion layers and governance.
Converts a product-library CSV into a SIF schedule for dealer systems, checking field mappings against the vendor spec.
Plans and checks analytics tracking, including GA4, tag managers, UTM parameters and event tracking plans.
Reads, writes and manipulates AnnData .h5ad and Zarr objects for single-cell and scverse workflows.
Analyzes A/B test results for significance, sample size, and confidence intervals, then recommends ship, extend, or stop.
Analyzes user retention and feature adoption by cohort, producing curves, trends, and segment insights from engagement data.
Builds a read-only, time-windowed product report of usage, performance, and errors from configured sources.
Guides enabling the Analytics Admin API via gcloud and managing accounts, properties, and data streams.
Guides enabling the Analytics Data API via gcloud and running custom reports on property data.
Computes statistics and comparison tables from ML experiment results, then writes findings and next experiments.
Cleans raw data, builds formula-driven Excel summaries and charts, and states conclusions in plain language.
Builds a KPI dashboard workbook with metric definitions, target versus actual, attainment rates and trend charts.
Teaches the agent to render charts with chart_render in a BI-grade style, choosing ECharts or Mermaid.
Extracts keywords with YAKE or RAKE, detects document language and creates embeddings for search.
Extracts text, tables, and structured fields from images and PDFs using VLM or traditional OCR providers.
Runs the open-source Google Maps scraper in Docker to collect business leads, contacts and reviews locally.
Generates HTML charts, slide decks and full-page reports from data, using built-in templates and color presets.
Runs an end-to-end Excel analysis workflow: sheet counts, cleaning, filtering, aggregation and exports.
Extracts data from chart and table images by captioning them with a vision model, then structures it.
Handles Excel datasets over 10k rows with streaming reads, Parquet conversion, chunking and memory limits.
Analyzes scientific data files in 200+ formats and writes a markdown report on structure, quality and statistics.
Guides fine-grained Python matplotlib plotting and exports publication-ready PNG, PDF and SVG figures.
Guides building, analyzing, and plotting graphs and networks in Python with NetworkX.
Guides statistical plots with seaborn for distributions, relationships and categories from pandas data.
Diagnoses missing notebook packages and installs Python or R packages through the managed package tool.
Builds a local data dashboard from datasets, with a sources panel showing where each number comes from.
Fetches stock, forex, crypto, and economic data plus technical indicators from the Alpha Vantage API.
Flags price scale, ticker, date, allocation and unit errors in market documents before publishing.
Profiles scientific data, recommends a chart type, then draws journal-ready figures in matplotlib and plotly.
Runs Apify scrapers across social, search, maps, and review platforms for research and monitoring.
Runs a read-only quality audit on a CSV or TSV file, reporting nulls, types, duplicates and date issues.
Builds a local terminal dashboard of agent activity, cron runs, token use, and cost across coding tools.
Guides setup, interpretation and improvement of app analytics, funnels, attribution and KPIs.
Guides the agent to annotate price charts with levels, patterns and events using annotation tools.
Handles data analysis, Excel formulas, ROI reviews, charts, reports and slides, with an expert perspective.
Researches Chinese platforms' last 30 days of posts into cited reports, and shows today's hot-search board.
Makes the agent collect AI models, products and repos first seen in the last 24 hours across several sources.
Finds recurring cross-table query intents in historic SQL shards and records them as typed pattern evidence.
Extracts metric formulas and field meanings from staged Looker dashboards and explores into ktx memory.
Maps LookML views, measures and explores into ktx semantic layer sources, with three worked examples.
Applies visualization judgment on conclusion, aggregation, chart type and color before drawing any chart.
Makes the agent compute arithmetic, unit conversions and formulas with labeled results, steps and checks.
Creates charts and tables with text summaries and JSON-LD so AI engines can parse and cite the underlying data.
Chooses a chart or diagram form for a dataset and applies encoding rules so the finding reads at a glance.
Guides MATLAB reliability analysis: life distribution fits, MTTF, B10, survival curves and bootstrap intervals.
Guides interactive curve and surface fitting in the MATLAB Curve Fitter app and exports results.
Answers Scenario usage questions and builds CSV, Markdown, PDF or dashboard reports from MCP data.
Finds, downloads and checksum-verifies App Store Connect analytics report segments before analysis.
Guides the agent to design readable dashboards and reply to comments without resolving them.
Guides writing Vega and Vega-Lite specs as Deneb visuals in Power BI PBIR reports.
Guides writing matplotlib or seaborn scripts for Python visuals in Power BI PBIR reports.
Queries Fabric and Power BI semantic models with DAX to feed dashboards, reusing existing model measures.
Creates R ggplot2 script visuals for Power BI PBIR reports, with the required print step and data rules.
Builds inline SVG charts and indicators in Power BI via DAX measures with ImageUrl data category.
Lets the agent run read-only DuckDB queries on Fabric lakehouse Delta tables and files.
Maps contest data files to subquestions, cleans them, and writes one reusable data quality profile.
Generates render-checked matplotlib figures from saved results, following an approved plan and style.
Guides evidence-based analysis of high-stakes decisions, keeping causes, forecasts and advice separate.
Analyzes multi-dimensional personal health data for anomalies and risks, and produces AI health reports.
Answers probability questions by reading market prices and cross-checking several trading signals into a report.
Guides server-side use of the unofficial yahoo-finance2 library for quotes, charts, and history.
Writes ggsql queries that combine SQL with grammar-of-graphics plot clauses, using only documented syntax.
Frames data analysis around a decision, then analyzes CSVs, metrics, trends and A/B tests.
Researches census, income, age, housing and employment data around a site, citing public sources in a concise report.
Builds interactive Plotly charts in Python, from quick Plotly Express plots to fine Graph Objects control.
Creates statistical charts with seaborn on pandas data, covering distributions, relationships and regressions.
Makes the agent run reproducible Python or R statistics on research data and produce publication-ready tables.
Guides the agent through pandas and matplotlib data cleaning, EDA, charts and reproducible notebooks.
Analyzes betting odds: converts formats, removes vig, finds edge, sizes bets with Kelly, and checks arbitrage and parlays.
Extracts titles, facts, colors and palette from an HTML page into JSON without redesigning it.
Lints records against ALCOA+ data-integrity rules and leaves compliance verdicts to a human reviewer.
Analyzes FIT and KML cycling, running and hiking tracks into reports and shareable route stories.
Crawls a site with Playwright to find its pages, platform, dataLayer events and GTM containers.
Profiles a database or DuckDB file before SQL is written: columns, grain, PII, joins, and semantic layer.
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