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Found 11,851 Skills
Build and operate predictive models for logistics networks—demand forecasting at SKU/location/lane granularity; inventory positioning and safety stock optimization interfaces; ETA and lead-time prediction; capacity and congestion signals; route and network flow forecasting at model-integration level; cold chain and perishables; promotion and seasonality; model monitoring, drift, and backtesting against operational KPIs (fill rate, OTIF, WMAPE/MAPE). Use for predictive logistics, demand forecasting logistics, ETA prediction, inventory positioning, safety stock optimization, OTIF forecast, lane demand, WMAPE, logistics ML, capacity forecasting logistics, or cold chain forecast—not pure OR/MIP without logistics domain (operations-research-algorithm-developer), supply chain strategy only (supply-chain-manager), WMS feature dev (wms-developer), fleet telematics ingestion (geospatial-telematics-developer), generic ML without logistics (data-scientist), or EDI document mapping (edi-engineer).
Reframes messages, requirements, metrics, and decisions for organizational audiences—engineering, product, finance, legal, compliance, sales, operations, actuarial, and executive—by detecting jargon, surfacing implicit assumptions, producing dual-audience briefs, RACI-aligned handoffs, owner-tagged meeting actions, technical-to-business and business-to-technical translation, and escalation summaries. Use when translating for engineering, explaining to finance, cross-department bridging, rewriting for executives, business-friendly versions, technical summaries for leadership, inter-team handoffs, department jargon, or dual-audience briefs—not external customer or brand copy (communication-lead), contract redlines (commercial-counsel), full multi-team program execution (technical-program-manager), human-language i18n/l10n product strings, or strategy-only consulting without audience reframing (business-consultant).
Expert knowledge for Azure Data Factory development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing ADF pipelines, mapping data flows, SHIR/SSIS IR, SAP CDC, or CI/CD with ARM/DevOps, and other Azure Data Factory related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks), Azure Stream Analytics (use azure-stream-analytics), Azure Data Explorer (use azure-data-explorer).
Analyze LLM experiment results. Handles single or comparative experiments, exploratory or Q&A modes. Use when user says "analyze experiment", "compare experiments", "analyze against baseline", or provides one or two experiment IDs for analysis.
Provides guidance on OpenTelemetry SDK setup, custom instrumentation, and sending data to Honeycomb. Trigger phrases: "instrument my app", "add tracing", "set up OpenTelemetry", "configure OTel", "add custom spans", "add attributes to spans", "send traces to Honeycomb", "set up OTLP", "configure sampling", "add span events", "add span links", "set up tracing for [any language]", "configure the OTel Collector", or any request about OpenTelemetry SDK setup, custom instrumentation, or sending data to Honeycomb.
Audits SQL migration files for destructive actions, potential table locks, and compatibility issues. Use before applying migrations to production databases to prevent downtime and ensure data integrity.
Automatically add or improve type annotations in legacy Python code. Use to improve code readability, IDE support, and catch type errors early.
Writes and reviews Swift App Intents code that exposes app actions and data to Siri, Shortcuts, Spotlight, widgets, Control Center, and Apple Intelligence. Use when adding AppIntent, AppEntity, OpenIntent, AppShortcutsProvider, EntityQuery, Focus Filters, AssistantEntity/AssistantIntent schemas, or when wiring SwiftData/networked data into intents.
Vector search with SurrealDB using HNSW indexes, KNN queries, and similarity scoring. Use when creating vector indexes, querying vectors with KNN distance operators, building semantic search or RAG pipelines, tuning HNSW parameters (EFC, M, M0, distance function, type), or implementing recommendation systems with SurrealDB. Triggers: HNSW, vector, embedding, KNN, cosine, euclidean, semantic search, RAG, vector::distance.
Internal guidance for presenting Pi helper output back to the user
Fluxo de trabalho com IA em 3 fases — Explorar (brainstorm guiado), Planejar (plano com tarefas atômicas) e Executar (passo a passo). Ative esta skill sempre que o usuário digitar `/explorar`, `/planejar` ou `/executar`, ou quando tiver qualquer objetivo que envolva planejamento, criação ou dúvida aberta. Serve tanto para tarefas de código quanto para tarefas do mundo real (escrever, pesquisar, contatar, montar). Idioma pt-BR.
Write spatial SQL against the connected warehouse — dialect-specific guidance, performance defaults, and CARTO's query/job execution model.