Results for “da”

Build financial projections, P&L statements, DCF models, and valuation analyses from assumptions and historical data. Use when the user requests financial modeling or provides relevant inputs for this workflow.

User research specialist. Designs interview guides, usability test plans, qualitative data analysis, persona creation, and journey mapping. Complements Echo's UI validation. Use when user research design or analysis is needed.

Publish HTML or Markdown to a public URL instantly with ShipPage. Use when a user wants to share, preview, host, or deploy generated pages, reports, dashboards, prototypes, docs, or product listings.

Implement PCI DSS compliance requirements for secure handling of payment card data and payment systems. Use when securing payment processing, achieving PCI compliance, or implementing payment card security measures.

Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.

Derive selection thresholds from market data instead of hardcoding. Trigger when: (1) reviewing hardcoded parameters, (2) volume/price thresholds seem arbitrary, (3) selection returns too many/few candidates.

Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.

Recover context from local AI conversation history with RecallBase. Use for same-day work summaries, resuming prior AI or coding sessions, or tracing a past decision to its source.

Expert knowledge for Azure Oracle development including troubleshooting, security, configuration, and integrations & coding patterns. Use when configuring TDE with Key Vault, Oracle@Azure connectivity, AI DB VNets, Exadata log export, or Sentinel, and other Azure Oracle related development tasks. Not for Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure VMware Solution (use azure-vmware-solution).

Expert knowledge for Azure Quantum development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using QDK with Azure Quantum workspaces, IonQ/Quantinuum/Rigetti targets, QIR/OpenQASM jobs, or Resource Estimator, and other Azure Quantum related development tasks. Not for Azure HPC Cache (use azure-hpc-cache), Azure Batch (use azure-batch), Azure Databricks (use azure-databricks), Azure Machine Learning (use azure-machine-learning).

Two-stage review engine with four-layer quality model for the WinDAGs meta-DAG. Receives completed node outputs and produces ReviewResult containing QualityVector. Stage 1 (Haiku) checks Floor + Wall on every node. Stage 2 (Sonnet) runs Ceiling evaluation conditionally using economic escalation formula. Enforces BC-EVAL-001 through BC-EVAL-006. Activate when operating as the Evaluator role in the meta-DAG, when reviewing node outputs, when computing quality vectors, or when deciding Stage 2 escalation.

Quarto agente do Time de Migração. Opera em duas fases. Fase 1: detecta a topologia do legado, sempre propõe uma topologia moderna alternativa e produz topology_decision.md (com pausa humana para aprovação). Fase 2: desenha as specs do sistema novo sob a topologia escolhida, produzindo target_architecture.md, target_domain_model.md, target_data_model.md e data_migration_plan.md, com rastreabilidade total para o legado. Ativação: /reversa-designer (geralmente invocado por /reversa-migrate).

Operate, configure, provision, secure, and troubleshoot Grafana OSS, Enterprise, and Cloud, including dashboards, folders, data sources, annotations, alert rules, contact points, notification policies, silences, mute timings, service accounts, RBAC, plugins, APIs, and as-code workflows. Use for Grafana product work and Grafana-side integrations. Do not use for defining SLOs or paging policy, operating Prometheus/Loki/Tempo/InfluxDB backends, generic Docker/Kubernetes/Terraform/reverse-proxy work, plugin development, or authorized security assessments; use the corresponding specialist skill.

Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

Expert knowledge for Azure Batch development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Batch pools/tasks, autoscale, containers/MPI, storage mounts, or CI/CD for HPC/render workloads, and other Azure Batch related development tasks. Not for Azure HDInsight (use azure-hdinsight), Azure Databricks (use azure-databricks), Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Virtual Machines (use azure-virtual-machines).

Expert knowledge for Azure Stack Edge development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when deploying IoT Edge modules, Kubernetes/GPU apps, DeepStream pipelines, Arc GitOps, or local ARM workloads, and other Azure Stack Edge related development tasks. Not for Azure Data Box (use azure-data-box-family), Azure IoT Edge (use azure-iot-edge), Azure Local (use azure-local), Azure Virtual Machines (use azure-virtual-machines).

Redige a peticao inicial pelo rito comum (CPC 319-321), com requisitos do art. 319 (qualificacao, fatos, fundamentos, pedido, valor da causa, provas, opcao por audiencia 334), pedido certo e determinado (324), cumulacao de pedidos (327), tutela provisoria se cabivel (cross-link tutela-urgencia/tutela-evidencia), valor da causa correto e gratuidade. Use quando o operador disser inicial, peticao inicial, vou ajuizar, propor acao, ingressar com a acao, distribuir, ou descrever um caso novo para autor.

Use to distil a CX dashboard into the three numbers a board or exec actually needs — volume/demand health, quality/outcome, and cost/efficiency — with commentary that drives decisions instead of forty tiles nobody acts on. Trigger for "board pack", "exec summary for support", "what should leadership see", "too many metrics on the dashboard", "three KPIs for the board", quarterly business review CX section, or when a leadership deck is mostly charts with no story.

Design, run, and learn from safe chaos engineering experiments with structured plans, blast radius calculation, and postmortem generation.

Define, track, and interpret product metrics across discovery, growth, and mature product stages using frameworks like AARRR, North Star, and HEART.

Analyze sprint data with Python scripts for velocity forecasting, health scoring, and retrospective insights to coach agile teams.

Set up, audit, and debug analytics tracking implementations including GA4, Google Tag Manager, event taxonomy, and conversion tracking.

Programmatically configure Google Analytics accounts, properties, data streams, custom dimensions, conversion events, and integrations using the Admin API.

Defines vehicle routing problem types (TSP, VRP, PDP) and the data requirements needed to formulate them, without covering any API or interface details.

Deploys the vss-video-analytics-api REST service standalone with config, data-log bind, and optional Elasticsearch/Kafka connectivity.

Routes NuRec/Neural Reconstruction requests to the correct upstream NVIDIA skill (datasets, conversion, training, rendering, object harvesting, frame cleanup).

Orchestrate multi-agent coding tasks by dispatching parallel agents in isolated worktrees, monitoring progress, and reading structured reports.

Quickly query genomic reference databases for Ensembl IDs, gene metadata, sequences, BLAST searches, and enrichment analysis using the gget CLI or Python package.

Optimize content to get cited by AI search engines and rank in LLM-generated answers.

Generate SEO-optimized pages at scale using templates and data to capture long-tail search traffic.

Generates a compact HTML KPI digest from Adobe Analytics, showing how key metrics changed over a period and what drove the movement.

Audit Django code for validated performance issues including N+1 queries, unbounded querysets, missing indexes, and write loops.

Opens a browser-based whiteboard for sketching ideas with sticky notes, then reads the visual snapshot and clipboard data to provide conversational analysis and suggestions.

Build and run evaluators for AI/LLM applications using Phoenix, covering error analysis, custom evaluators, experiments, and production monitoring.

Queries Microsoft 365 Copilot data (emails, meetings, documents, Teams, people) using the WorkIQ CLI/MCP server for live context, summaries, and recommendations.

Bulk-migrate repo labels and Project V2 fields into GitHub org-level issue fields (single select, text, number, date).