Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.

Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.

Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.

Add-on to the sniff-test skill that looks across many initiatives on a portfolio Kanban for board-level patterns — duplicate/shadowing initiatives, WIP clustering, stale-status sweeps, experiment scope creep, and portfolio risk balance (Explore/Discovery vs. Build & Deliver split). Use when reviewing a whole board or portfolio, prepping a sponsor/ELT portfolio review, or deciding where the portfolio is over- or under-invested. For sniffing a single initiative, use sniff-test. For the operating model behind the board rather than the board itself, use portfolio-to-product-shift-coach. If the question is broad — AI pilots with no clear return, or "where do we start" — run ai-activity-to-impact first; it finds the constraint and routes here.

Serverless compute for Databricks jobs, Lakeflow pipelines and Declarative Automation Bundles (DABs), with STANDARD or PERFORMANCE_OPTIMIZED and classic only where serverless cannot run the workload. Use when creating, deploying, scheduling or editing a job or pipeline, or deciding its compute. Invoke BEFORE writing a job spec. For migrating existing classic workloads, use databricks-serverless-migration.

Serverless compute for Databricks jobs, Lakeflow pipelines and Declarative Automation Bundles (DABs), with STANDARD or PERFORMANCE_OPTIMIZED and classic only where serverless cannot run the workload. Use when creating, deploying, scheduling or editing a job or pipeline, or deciding its compute. Invoke BEFORE writing a job spec. For migrating existing classic workloads, use databricks-serverless-migration.

Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gateway rate limits (not Unity Gateway); discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: Unity Gateway CRUD and management (databricks-unity-gateway), training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).

Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).

Build apps on Databricks Apps platform. Use when asked to create data apps, analytics tools, or custom interactive visualizations. A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.

Automate Abyssale tasks via Rube MCP (Composio). Always search tools first for current schemas.

Build, run, and improve a Security Operations Center — alert prioritization, runbook authoring, escalation criteria, on-call structure, alert tuning workflow, MTTD / MTTR / fidelity KPIs, analyst tiering, and shift handoffs. Use when the user mentions 'SOC,' 'security operations,' 'SOC analyst,' 'alert triage workflow,' 'runbook,' 'escalation,' 'on-call,' 'SOC tiering,' 'tier 1 / tier 2,' 'MTTD,' 'MTTR,' 'alert fatigue,' 'shift handoff,' 'SOAR,' or wants to design or improve a security operations team.

Use when working in a repository that uses bd or Beads for durable project task tracking, issue dependencies, blocker management, multi-session handoff, or shared work memory. Trigger when the user asks to find ready work, claim or close tasks, create follow-up work, inspect blockers, recover project context, sync beads data, recover from an empty local database, or choose between local planning and persistent project tracking.

Use for anything AI/agent/conversational built on Algolia: algobot CLI, Agent Studio, RAG systems, conversational product discovery, genAI content generation from search results (carousels, descriptions, headers), chatbots or recommendation agents using Algolia as retrieval, config-as-code workflows, multi-environment deploy (dev/staging/prod), memory and personalization, MCP tool integrations, conversation history / GDPR retention, or adding a chat widget alongside InstantSearch. Trigger on: "algobot", "Agent Studio", "RAG with Algolia", "conversational experience", "AI agent" + Algolia, "genAI carousel", "chat widget", or building AI features on top of Algolia search. Do NOT use for Agent Studio evaluations or eval-driven agent improvement - use agent-studio-evaluations. Do NOT use for raw index ops (records, synonyms, settings) - use algolia-cli. Do NOT use for pure frontend search UI (InstantSearch, autocomplete) with no AI/agent layer.

Track real-time cryptocurrency prices across exchanges with historical data and alerts. Provides price data infrastructure for dependent skills (portfolio, tax, DeFi, arbitrage). Use when checking crypto prices, monitoring markets, or fetching historical price data. Trigger with phrases like "check price", "BTC price", "crypto prices", "price history", "get quote for", "what's ETH trading at", "show me top coins", or "track my watchlist".

Run conversion rate optimization through hypothesis-driven testing including audit, hypothesis generation, test design, statistical analysis, and rollout decisions. Use this skill whenever the user wants to optimize conversion, run A/B tests, audit a funnel, generate test hypotheses, design experiments, or analyze test results. Triggers on conversion optimization, CRO, A/B test, split test, multivariate test, hypothesis, conversion funnel, funnel audit, experiment design, statistical significance, lift, optimization. Also triggers when the user has a conversion problem and isn't sure where to start, or when test results are ambiguous and need interpretation.

Procedure to review a pull request in mixpanel-headless and check each finding before it is posted. Use for every pull request review in this repository.

Test XR session lifecycle and mode transitions. Use when verifying XR enter/exit behavior, testing mode-dependent features, or debugging session state issues.

Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of writing it up. Use for 'blast radius of X', 'what could this break', or reviewing a small diff you don't trust.

Use this skill when converting any Prefect flow to Orchestra YAML to handle flow-level configuration. Triggers: any Prefect flow definition containing @flow, schedule= (CronSchedule, IntervalSchedule, RRuleSchedule), retries=, timeout_seconds=, flow.deploy() or flow.serve() deployment config, flow parameters (typed function args), or flow-level tags. Must be applied before converting individual tasks — it establishes the pipeline root fields that wrap all task conversions.

This skill should be used when the user says "save plan", "save the plan", "arness code save plan", "structure this plan", "create project from plan", "export plan", "export project plan", "organize this plan", "set up project structure", "turn this into a project", "generate project structure", "finalize the plan", or wants to convert a planning conversation into an actionable phased project structure with implementation and testing plans.