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Supports medical device quality investigations through CAPA, a process for corrective and preventive action. Helps identify root causes, plan actions, verify results, and report overdue cases, recurrence rates, and other program metrics.
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Package workspace skills and their required service integrations into one named, installable capability. Create a manifest that references existing skills, install the capability, and report which integrations still need to be connected.
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Customize an installed workspace capability, a bundle of agent tools and guidance. It updates the configuration to add MCP servers, which connect agents to tools, or change skills, integrations, and instructions while preserving existing settings.
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Manage capa CLI configuration — `capabilities.yaml` / `capabilities.json`, skills, MCP servers, tools, hooks, sub-agents, rules, plugins, AGENTS.md / CLAUDE.md, security options, tool exposure modes, `capa wrap` shadow workspaces, `--passthrough` native writes, registries (including Claude marketplaces), activity traces, and the local Web UI. Use whenever the user edits the capabilities file, runs any `capa` command (init, install, add, wrap, clean, sh, start/stop/restart/status, auth, upgrade, cache, registry), wires up an MCP server, adds a skill from GitHub / GitLab / a registry / a remote URL / a local path, configures secrets via `${VarName}` placeholders, installs lifecycle hooks, defines sub-agents, or troubleshoots a failed install. Use even when the user only names a fragment ("add a skill", "wire up brave search", "wrap cursor", "block this phrase in skills") without saying "capa" — if a `capabilities.yaml` or `capabilities.json` lives at the project root, this is almost certainly the right skill. If the project does NOT yet have a capabilities file, point at the `bootstrap` skill instead.
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Use this skill to configure and manage Field Service Lightning workforce capacity — covering ServiceResourceCapacity for individual resource caps, WorkCapacityLimit for territory-level demand throttling, and reporting strategies for capacity vs. utilization analysis. NOT for resource skills, certifications, or preference setup — use admin/fsl-resource-management. NOT for work rules and service objectives — use admin/fsl-scheduling-policies. NOT for Omni-Channel agent capacity — use architect/omni-channel-capacity-model.
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Uses capa to identify malware capabilities from a binary: running rule-based detection over disassembly, mapping matched capabilities to MITRE ATT&CK and MBC, and interpreting results to focus deeper analysis. Activates for requests to run capa, generate a capability report, or map a sample's behaviors to ATT&CK automatically.
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Inspect Google Cloud Filestore usage and free space, then propose capacity increases or decreases based on configured thresholds and storage tier limits. Apply resizing after confirmation to address low space or reduce excess capacity.
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Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
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Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
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Size support and operations teams using demand ranges and queueing calculations, which estimate how work waits for service. Assess workload imbalance and service target risks, then build a quarterly hiring plan that accounts for training time and attrition.
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Route internal operations questions to six specialist skills covering workflows, vendors, staffing capacity, communications, procedures, and procurement. Receive a brief summary of findings, recommended actions, and links to the documents or data files produced.
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Configure and troubleshoot automatic node scaling in GKE. Examine logs and workload constraints to explain why nodes fail to grow or shrink, and guide tuning for capacity shortages, zone placement, and sudden traffic increases.
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[omh] Tailoring enabled OMH families: turning one OMH capability family on or off so an install can be tailored instead of taken whole. Use when the user says: capability-toggle, capability policy, disable memory, enable memory, disable coding orchestration, disable a capability family, enable a capability family, 메모리 기능 꺼줘.
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Azure Resource Manager SDK for Fabric in .NET. Use for MANAGEMENT PLANE operations: provisioning, scaling, suspending/resuming Microsoft Fabric capacities, checking name availability, and listing SKUs via Azure Resource Manager. Triggers: "Fabric capacity", "create capacity", "suspend capacity", "resume capacity", "Fabric SKU", "provision Fabric", "ARM Fabric", "FabricCapacityResource".
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Produce a capacity planning document for a service covering traffic forecasts, resource requirements, and scaling strategy. Use when asked to plan infrastructure capacity, forecast resource needs, model traffic growth, define scaling strategy, or produce a capacity review for a service. Produces a structured capacity plan covering current baseline metrics, growth projections, resource requirements per tier, scaling strategy, cost projections, capacity triggers, and an infrastructure action roadmap.
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用于制造现场的不合格品管理，梳理隔离、影响范围调查、原因分析、处置及纠正预防措施的效果验证，并协助解读控制图和改善供应商质量。
Investigate a Homecore capability by tracing relevant source files, tests, and accepted architecture decisions. Check documented status and limitations before proposing code, while keeping implementation presence separate from deployment compatibility or claims of Home Assistant parity.
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Investigate excessive privileges inside Kubernetes pods by reviewing security settings, runtime events, and audit logs. Use admission policies to restrict risky configurations and Falco monitoring to detect suspicious privilege changes and container activity.
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Analyze team workloads, available hours, and upcoming demand to build a resource capacity plan. Identify staffing gaps and bottlenecks, then compare hiring, contracting, reprioritizing, or delaying work within budget and schedule constraints.
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Prepares medical device quality systems for ISO 13485 audits using six focused questions. Reviews design records, corrective actions, process validation, risk files, post-market monitoring, and management reviews, then summarizes readiness gaps and three priority actions.
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Build a sprint plan for a short development cycle using team availability and recent delivery history. Select work that fits capacity, sequence dependent tasks, identify owners and risks, and reserve room for unexpected work.
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Retrieve BigQuery execution records and resource metrics using the right tool for each task. The skill guides monitoring tool selection and SQL queries for performance investigations, capacity analysis, and reviews of billable compute and storage usage.
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Kode capability-management playbook: treat features (LSP, statusline, output styles, plugins, notifications) as configurable capabilities and drive changes through the agent CLI (SlashCommand/Task) instead of pushing users into menu-like flows. Use when the user asks to enable/disable/configure a capability, diagnose why it’s off, or wants the agent to self-manage Kode’s own features (meta capability management).
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Matches operational capacity to expected demand — forecasting load, sizing teams and systems, managing queues, and deciding when to add capacity. Use this to plan staffing for expected volume, diagnose a queue that keeps growing, size support or fulfillment capacity, or decide whether a bottleneck needs more capacity or better flow.
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