strawgate/kb-yaml-to-lens

AI Summary: This GitHub issue proposes adding a configuration option to display value labels directly on data points for XY charts. Currently, value labels are hardcoded to 'hide', limiting chart readability. The suggested solution involves introducing a `show_value_labels` boolean field to the chart configuration and updating the compilation logic to utilize this setting.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics and logs from Cloud Foundry applications using the OpenTelemetry Collector's `cloudfoundryreceiver`. It outlines the necessary Cloud Foundry and OpenTelemetry Collector configurations, including UAA authentication and TLS settings, and describes the data model for metrics.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics collected by the OpenTelemetry Collector's StatsD receiver. The dashboard should help monitor applications that send metrics using the StatsD protocol, supporting various metric types like counters, gauges, timers, and distributions.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize logs collected by the OpenTelemetry Collector's `fluentforwardreceiver`. The dashboard should include visualizations for total log volume and log ingestion rate, leveraging common log attributes like tag, timestamp, and source.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics from an ActiveMQ message broker. The visualization will be powered by the OpenTelemetry Collector's `jmxreceiver`, which needs to be configured to connect to the ActiveMQ instance via JMX. Prerequisites and example configurations for both ActiveMQ and the OTel Collector are provided.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize Kubernetes objects as logs, generated by the OpenTelemetry Collector's `k8sobjectsreceiver`. The dashboard should include visualizations for events, object counts by kind and namespace, and potentially other object-specific details based on the receiver's output.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics from the OpenTelemetry Collector's `nsxtreceiver`. The dashboard should display CPU, filesystem, memory, and network utilization data from VMware NSX-T virtual networking environments.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize database metrics collected by the OpenTelemetry Collector's `sqlqueryreceiver`. The receiver supports various databases and allows custom SQL queries to generate metrics and logs, with features like parameterized queries and result tracking.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics from the OpenTelemetry Collector's `httpcheckreceiver`. The dashboard should monitor HTTP endpoint availability and performance, leveraging various metrics like request duration, TLS certificate status, and validation success/failure.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics from SNMP-enabled devices collected by the OpenTelemetry Collector's `snmpreceiver`. It outlines the necessary prerequisites, configuration details for the `snmpreceiver`, and provides an example configuration for basic SNMP v2c collection.

AI Summary: This issue proposes creating dashboards for a comprehensive list of new OpenTelemetry (OTel) receivers. The task involves identifying existing dashboard coverage, tracking issues, and creating new tracking issues for receivers that lack them, including investigation into their metrics and attributes.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics collected by the OpenTelemetry Collector's `kubeletstatsreceiver`. The dashboard should display node, pod, container, and volume metrics from a Kubernetes cluster, leveraging data from the kubelet API.

AI Summary: This issue proposes identifying and implementing new linting rules for dashboard creation. The goal is to improve consistency and quality by leveraging existing style guides, conversion guides, and best practices.

AI Summary: This issue proposes to identify and document unused metrics within the OpenTelemetry Receiver documentation. The goal is to compare the available metrics in each receiver's README with their corresponding dashboards, highlighting any metrics that are not currently utilized. This information will then be added to the respective README files.

AI Summary: This issue proposes to improve dashboard usability by preventing excessively narrow XY charts. The current practice of placing three side-by-side with visible legends leads to charts being too small to be effective. Solutions include moving legends to the bottom or limiting side-by-side XY charts to a maximum of two.

AI Summary: This issue requests the conversion of an existing `docker_otel` dashboard to use ES|QL. The goal is to adhere to established best practices for this migration.

AI Summary: This issue requests an update to existing code snippets that are currently generating objects that do not conform to the defined schema. The goal is to ensure the snippets produce valid output according to the project's specifications.

AI Summary: This issue proposes expanding the `kibana_client` to better support existing and future use cases. Key goals include introducing strongly typed responses, migrating Elasticsearch calls to a proxy API, and refactoring file writing operations to return Pydantic models instead of directly writing to files, thereby keeping the client cleaner and more reusable.

AI Summary: This issue proposes adding a "Dashboard Checker" feature to flag potentially problematic dashboard configurations based on subjective, non-error criteria. It suggests implementing rules to identify issues like specific markdown panel configurations, metrics with mismatched titles, dashboards lacking dataset filters, ESQL queries without WHERE clauses, and dimensions without labels. The goal is to improve dashboard quality and consistency by providing actionable feedback to users.

AI Summary: This issue requests the removal of unnecessary markdown panels that are appearing on ES|QL dashboards. These panels contain explanatory text about the use of TS, RATE(), and TBUCKET() commands, which are deemed redundant and are cluttering the dashboard interface.

AI Summary: This issue requests the validation of three Redis OpenTelemetry dashboards. The task involves ensuring the dashboards compile correctly, import into Kibana, and display key Redis metrics such as uptime, connected clients, memory usage, and command processing.

AI Summary: This issue requests validation of a newly merged Memcached OpenTelemetry dashboard bundle. The task involves ensuring the dashboard compiles correctly, its panels display key Memcached metrics accurately, and its controls function as expected, using provided Docker setup instructions for Memcached and the OpenTelemetry Collector.

AI Summary: This issue calls for a comprehensive review of all existing OpenTelemetry (OTel) dashboards to ensure their accuracy against upstream receivers. The goal is to identify and rectify issues like non-existent attribute names, incorrect ES|QL queries, and missing metrics, drawing lessons from recent pull requests. The ultimate aim is to establish general guidelines for OTel dashboard creation and potentially develop a dedicated guide to prevent future inaccuracies.

AI Summary: This issue requests validation of three Aerospike dashboards (`overview.yaml`, `node-metrics.yaml`, `namespace-metrics.yaml`) that were recently merged. The validation involves ensuring the dashboards compile correctly, their JSON is valid, and they import properly into the system, using specific index patterns and data streams related to Aerospike metrics.

AI Summary: This issue requires validation of an OpenTelemetry dashboard bundle for Apache HTTP Server. The task involves checking the `01-apache-overview.yaml` dashboard against specific metrics and data requirements, using a provided Docker setup for Apache and an example OpenTelemetry Collector configuration.

AI Summary: This issue requests validation of a newly merged OpenTelemetry dashboard bundle for system metrics. The task involves checking five specific dashboard YAML files located in `docs/content/examples/system_otel/` to ensure they correctly display system metrics like CPU, memory, disk, and network usage, using `metrics-*` and `logs-*` index patterns and a specific filter.

AI Summary: This issue requires validating six CrowdStrike dashboards (classic version) located in `docs/content/examples/crowdstrike/`. The validation involves ensuring the dashboards compile correctly, import into Kibana, and that their panels accurately display metrics based on specific index patterns and data streams.

AI Summary: This issue requests validation of three OpenTelemetry dashboards designed for AWS VPC Flow Logs. The validation involves checking if the dashboards correctly display data from the specified index pattern and filter, using a provided list of required fields. Setup instructions for the OpenTelemetry Collector are also included.

AI Summary: This issue requests validation of a newly merged System Integration dashboard bundle containing 28 dashboards for monitoring Linux/Unix and Windows systems. The validation should confirm that the dashboards correctly display data from the Elastic System integration, not OpenTelemetry, and that the expected data patterns are present. Setup instructions for both Elastic Agent and standalone Metricbeat/Filebeat are provided.

AI Summary: This issue requires validation of the System Modern dashboard bundle, specifically the ES|QL versions of 14 dashboards. The validation involves checking that the dashboards correctly display data from the specified index patterns using various ES|QL features. Setup instructions for Elastic Agent are provided.

AI Summary: This issue requests validation of two Docker OpenTelemetry dashboard YAML files located in `docs/content/examples/docker_otel/`. The validation involves checking if key metrics and attributes related to Docker container statistics are correctly displayed and mapped within the dashboards, using a specified index pattern and filter.

AI Summary: This issue requests the validation of four CrowdStrike Modern dashboards (SOC, Threat Investigation, Asset Vulnerability, and Compliance Audit) located in the `docs/content/examples/crowdstrike-modern/` directory. The validation involves ensuring the dashboards compile correctly, import into Kibana, and that their individual panels accurately display data based on specified index patterns and data streams.

AI Summary: This issue requests the validation of two MySQL OpenTelemetry dashboards, `mysql-overview-lens.yaml` and `mysql-overview-esql.yaml`. The validation involves ensuring they compile correctly, import into Kibana, and accurately display key MySQL metrics like uptime, threads, and query counts. Setup instructions for MySQL and an OpenTelemetry Collector are provided to facilitate this validation.

AI Summary: This issue requests validation of seven Elasticsearch OpenTelemetry dashboards located in `docs/content/examples/elasticsearch_otel/`. The validation involves checking if the dashboards accurately display key metrics related to cluster health, node performance, index statistics, JVM health, and circuit breakers, using a specific index pattern and filter.

AI Summary: This issue requires validation of five OpenTelemetry dashboards for Kubernetes cluster monitoring. The validation involves checking if the dashboards correctly display key metrics related to cluster health, workload status, resource allocation, batch jobs, and autoscaling, using a specific index pattern and filter.

AI Summary: This issue requests validation of an AWS CloudTrail OpenTelemetry (OTEL) dashboard bundle. The dashboard is designed to visualize CloudTrail logs for security and compliance, requiring specific data fields and an OpenTelemetry Collector configuration to ingest data from an S3 bucket. The goal is to ensure the dashboard accurately represents the expected CloudTrail data.

AI Summary: This issue proposes adding snapshot tests for more complete dashboard views. The goal is to ensure that changes to the dashboard are accurately reflected and captured by these tests.

AI Summary: This issue requests a compilation process for an 'integrations repo' where each dashboard is compiled into a separate JSON file. The filename should be derived from the dashboard's ID, appended with '.json'. The core question is whether the current system already supports this specific output format.

AI Summary: This issue proposes using the `ruamel.yaml` library to handle the editing of panel size and position within an extension. The goal is to leverage `ruamel.yaml`'s capabilities for more robust YAML manipulation in this context.

AI Summary: This issue proposes setting up YAML fixtures from a specific GitHub repository within a pytest run. The goal is to build YAML versions, compile them, and then use a deepdiff to compare them against existing JSON fixtures, creating new snapshots of any differences.

AI Summary: This issue requests a review and update of existing code snippets to reflect recent schema changes, including size and position adjustments. It also calls for the addition of any new snippets required for newly introduced panels. The goal is to ensure all snippets are accurate and up-to-date.

AI Summary: This issue proposes refactoring the command-line interface (CLI) due to its expanding functionality. The goal is to introduce helper functions for improved message formatting (e.g., colored output) and to simplify testing. Additionally, the CLI might be split into separate files for different environments like Kibana/Elasticsearch versus local.

AI Summary: This issue proposes adding an option to export dashboards as individual JSON files instead of a single NDJSON file, with pretty-printing enabled. It also suggests setting the exit code to reflect the number of updated files, enabling CI integration to detect outdated JSON configurations.

AI Summary: This issue proposes identifying and implementing opportunities to share code related to visualization state compilation. The goal is to improve code reuse, simplify the codebase, and reduce redundancy without introducing excessive indirection or making the code difficult to understand.

AI Summary: This issue proposes migrating the Kibana fixture generator to its own dedicated repository. The goal is to enable consumption of its JSON output without including the generator's code within the main Kibana repository.

AI Summary: The LLM struggles to generate correct ES|QL queries, often defaulting to SQL syntax. This issue proposes distilling the ES|QL and ES|QL TS documentation into a guide to improve the LLM's ES|QL generation capabilities when users specifically request ES|QL dashboards.

AI Summary: This issue proposes a new testing strategy for Kibana. The goal is to directly use Kibana test fixtures to generate YAML configurations for dashboards and then snapshot the differences to ensure consistency.

AI Summary: This issue proposes a review of the project's architecture to address growing maintenance difficulties caused by multiple distribution methods and complex build/test pipelines. The goal is to simplify the project, making it easier to build, test, support, and maintain, potentially by examining successful architectures in other projects.

AI Summary: This issue requests the conversion of 24 Elastic Agent integration dashboards from JSON format to YAML. The goal is to make these dashboards compatible with the `kb-yaml-to-lens` tool, improving the observability of Elastic Agent health and performance for platform, SRE, and DevOps teams.

AI Summary: This issue requests the conversion of existing System integration dashboards from the elastic/integrations repository to YAML format. This conversion is necessary for compatibility with the kb-yaml-to-lens tool, enabling better visualization and analysis of foundational operating system monitoring data across various platforms.

AI Summary: This issue requests the conversion of existing CrowdStrike integration dashboards from the `elastic/integrations` repository into YAML format. This conversion is necessary for compatibility with the `kb-yaml-to-lens` tool, aiming to improve the usability and accessibility of CrowdStrike security data for analysis and incident response.

AI Summary: This issue requests the conversion of PostgreSQL integration dashboards from the `elastic/integrations` repository into YAML format. The goal is to make these dashboards compatible with the `kb-yaml-to-lens` tool, enabling better observability for PostgreSQL databases by monitoring various aspects like performance, connections, and replication.

AI Summary: This issue requests the conversion of Zscaler Internet Access (ZIA) dashboards from the `elastic/integrations` repository to YAML format. The goal is to make these dashboards compatible with `kb-yaml-to-lens` for enhanced security monitoring of web traffic, threats, DLP, and user activity. This conversion is important for security teams to gain better visibility into their organization's internet-bound traffic and zero-trust architecture.

AI Summary: This issue requests the conversion of SentinelOne integration dashboards from the elastic/integrations repository to YAML format. This is for use with a tool called kb-yaml-to-lens and is considered a good first issue, indicating it's a straightforward task.

AI Summary: This issue requests the conversion of existing MySQL integration dashboards from the `elastic/integrations` repository into YAML format. The goal is to make these dashboards compatible with the `kb-yaml-to-lens` tool, thereby improving MySQL observability for database administrators.

AI Summary: This issue requests the conversion of Nginx integration dashboards from the elastic/integrations repository to YAML format. The goal is to make these dashboards compatible with kb-yaml-to-lens, enabling better observability for Nginx, a widely used web server. The conversion will cover various aspects of Nginx performance, traffic, and error analysis.

AI Summary: This issue requests the conversion of existing Google Workspace integration dashboards from the elastic/integrations repository into YAML format. This conversion is intended for use with the kb-yaml-to-lens tool, aiming to improve the usability and accessibility of security and productivity monitoring for Google Workspace environments.

AI Summary: This issue requests the conversion of CyberArk PAS integration dashboards from their current format to YAML for use with kb-yaml-to-lens. This is important for security teams to monitor privileged account usage, detect threats, and meet compliance requirements.

AI Summary: This issue requests the conversion of existing Azure OpenAI integration dashboards from their current format to YAML for use with the kb-yaml-to-lens tool. The goal is to improve observability of Azure OpenAI usage, cost, performance, and security by making these dashboards more accessible and manageable.

AI Summary: This issue requests the conversion of Palo Alto Networks PAN-OS integration dashboards from their current format to YAML for use with kb-yaml-to-lens. The goal is to make these dashboards, which monitor critical security and network data, more accessible and usable for security operations.

AI Summary: This issue requests the conversion of existing Kubernetes integration dashboards from the elastic/integrations repository into YAML format. This conversion is intended for use with the kb-yaml-to-lens tool, aiming to improve the observability of Kubernetes environments for platform teams and SREs.

AI Summary: This issue requests the conversion of Fortinet FortiGate integration dashboards from the elastic/integrations repository to YAML format. This conversion is crucial for enabling the use of these dashboards with kb-yaml-to-lens, facilitating better security monitoring and analysis for FortiGate firewalls.

AI Summary: This issue requests the conversion of existing Azure Metrics integration dashboards from their current format to YAML. The goal is to make these dashboards compatible with the kb-yaml-to-lens tool, enabling better performance monitoring across various Azure resource types.

AI Summary: This issue requests the conversion of existing Windows integration dashboards from the elastic/integrations repository to YAML format. This conversion is intended for use with the kb-yaml-to-lens tool and aims to improve the usability and maintainability of these essential dashboards for monitoring Windows systems.

AI Summary: This issue requests the conversion of existing Google Cloud Platform (GCP) integration dashboards from the elastic/integrations repository into YAML format. The goal is to make these dashboards compatible with the kb-yaml-to-lens tool, enabling version-controlled observability for GCP infrastructure.

AI Summary: This issue requests the conversion of Microsoft Office 365 integration dashboards from the elastic/integrations repository to YAML format for use with kb-yaml-to-lens. The conversion is important for organizations monitoring security and compliance across their Office 365 environment, covering various services like Exchange, SharePoint, OneDrive, and Azure AD.

AI Summary: This issue proposes converting the Okta integration dashboards from their current format to YAML for use with the kb-yaml-to-lens tool. This conversion is important for security teams to effectively monitor Okta events, detect threats, and meet compliance requirements.

AI Summary: This issue requests the conversion of existing Azure integration dashboards within the elastic/integrations repository to YAML format. This conversion is intended for use with a tool called kb-yaml-to-lens, aiming to enable version-controlled observability for Azure infrastructure.

AI Summary: This issue requests the conversion of 53 AWS integration dashboards from JSON to YAML format for use with the kb-yaml-to-lens tool. The goal is to improve version control and maintainability of AWS observability configurations, which is crucial for cloud-native organizations monitoring a wide range of AWS services.

AI Summary: This issue requests the creation of a Kibana dashboard to visualize metrics from the OpenTelemetry Collector's Kubernetes Cluster receiver. The dashboard should display over 50 metrics related to containers, pods, namespaces, deployments, ReplicaSets, StatefulSets, DaemonSets, and Jobs within a Kubernetes cluster.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics from the OpenTelemetry Apache HTTP Server receiver. The dashboard should display key performance and capacity metrics derived from Apache's `mod_status` module, as configured through the OTel Collector.

AI Summary: This issue requests the creation of a Kibana dashboard for the OpenTelemetry Collector's Redis receiver. The dashboard should visualize key Redis metrics, including client connections, memory usage, keyspace statistics, command performance, network I/O, and replication/cluster information.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics from the OpenTelemetry `memcachedreceiver`. The dashboard should display 11 specific Memcached metrics, including bytes used, commands executed, connections, CPU usage, items stored, evictions, network traffic, hit ratio, and operations.

AI Summary: This issue requests the creation of a Kibana dashboard for the OpenTelemetry Collector's Elasticsearch receiver. The dashboard should visualize key metrics related to Elasticsearch cluster health, circuit breakers, node-level operations, caching, file system usage, and HTTP requests. The goal is to provide comprehensive observability into Elasticsearch performance and status.

AI Summary: This issue requests the creation of a Kibana dashboard for the OpenTelemetry Collector's MySQL receiver. The dashboard should visualize over 50 metrics related to MySQL's buffer pool, handler and row operations, InnoDB performance, connections, commands, and I/O waits. This is a follow-up to a parent issue and is labeled as an enhancement with help wanted.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics from CouchDB using the OpenTelemetry Collector's `couchdbreceiver`. The dashboard should display 8 specific metrics related to CouchDB performance and operations, leveraging attributes like HTTP methods and status codes.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to monitor Riak KV using the OpenTelemetry Collector's riakreceiver. The dashboard should visualize key metrics like memory limits, operation counts, and read repairs, leveraging provided Riak and OTel Collector configurations.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics from the OpenTelemetry Collector's `googlecloudspannerreceiver`. The dashboard will display key performance indicators for Google Cloud Spanner databases, such as query execution counts, latency, and resource utilization, derived from Spanner's introspection tables.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics from AWS container workloads (EKS, ECS, Kubernetes on EC2) using the OpenTelemetry Collector's `awscontainerinsightreceiver`. The dashboard should display cluster-level and node-level metrics, with specific examples provided for CPU and GPU utilization.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize JVM metrics collected by the OpenTelemetry Collector's `jmxreceiver`. The dashboard should focus specifically on the `jvm` target system and display at least 15 relevant metrics, such as heap memory usage, garbage collection activity, and thread counts. Prerequisites include a configured JMX connection and an OpenTelemetry Collector with the `jmxreceiver` enabled.

AI Summary: This issue requests the creation of a Kibana Lens dashboard to visualize metrics from Microsoft SQL Server using the OpenTelemetry Collector's `sqlserverreceiver`. It outlines two configuration modes for the receiver (Windows Performance Counters and Direct SQL Connection) and lists over 40 metrics that can be displayed, including core performance and memory/buffer metrics.

AI Summary: This issue requests the creation of a Kibana dashboard for the OpenTelemetry Collector's HAProxy receiver. The dashboard should visualize key metrics related to HAProxy's performance, including throughput, requests, responses, connections, and sessions, covering both default and optional metrics.

AI Summary: This issue requests the creation of a Kibana dashboard to visualize metrics from the OpenTelemetry Collector's RabbitMQ receiver. The dashboard should display key metrics related to queues (messages, consumers) and RabbitMQ nodes (memory, disk, file descriptors, sockets, processes, system performance, and garbage collection).

AI Summary: This issue requests the creation of a Kibana dashboard to visualize metrics from the OpenTelemetry Collector's Kafka Metrics receiver. The dashboard should display key metrics related to Kafka brokers, topics, and consumer groups, including lag, offsets, and replication status. It also needs to accommodate optional metrics for more detailed configuration insights.

AI Summary: This issue requests the creation of a Kibana dashboard for the OpenTelemetry Collector's MongoDB receiver. The dashboard should visualize over 50 metrics related to MongoDB connections, databases, collections, operations, cache, memory, network I/O, cursors, and locks, providing insights into MongoDB performance and health.

AI Summary: This issue identifies that the compiler for `kb-yaml-to-lens` is missing support for `showSingleSeries` and `legendSize` fields, which are used for legend settings in Kibana dashboards, particularly for pie charts. The task involves reviewing Kibana panels to ensure proper support for these legend options.

AI Summary: This issue proposes standardizing all documentation examples and index patterns to use `logs-*` or `metrics-*`. The goal is to make examples runnable for users by aligning them with commonly available fields, ideally those sent by an OpenTelemetry collector, ensuring a seamless copy-paste experience.

AI Summary: This issue aims to improve the documentation for panel examples, specifically for ES|QL metrics. The current examples are often incomplete or commented out, hindering user adoption and automated testing. The proposed solution involves restructuring the documentation into dedicated sections for ES|QL and Lens charts, providing fully runnable examples, and exploring programmatic verification of these examples.

AI Summary: This issue proposes a significant refactoring of the CLI commands, Kibana client, and Elasticsearch client builder. The goal is to improve maintainability, flexibility, and reduce duplication by leveraging Click documentation for CLI configuration and introducing dedicated client classes and helper functions.

AI Summary: This issue proposes adding validation to Kibana panels to ensure minimum requirements are met before rendering. Currently, panels like ES|QL tables, datatables, bar charts, metric charts, and pie charts can render blank if essential configurations (like metrics or axis definitions) are missing. The goal is to implement validators that provide clear error messages instead of empty panels.

AI Summary: The issue proposes a way to update YAML files programmatically without full deserialization and reserialization. This is desired to preserve comments and avoid overwriting user-defined values with defaults. The feature could also enable UI enhancements like drag-and-drop panel reordering in a VS Code extension.

AI Summary: This issue proposes an 'auto-grid' feature for dashboard panels, making x and y coordinates optional. Panels will automatically arrange themselves based on their dimensions and order, filling rows before moving to the next. It also introduces predefined width options and a mechanism to 'lock' panels by specifying their coordinates, around which other panels will flow.

AI Summary: This issue proposes adding functionality to bundle sample data with dashboards. This would allow users to load pre-defined data alongside dashboard configurations, enabling them to visualize "real" data immediately without requiring a separate data ingestion process. Key considerations include exporting data, loading it, timestamp manipulation, and bypassing ingest pipelines.

AI Summary: This issue proposes adding a check to the `gh-check-repo-activity.sh` script to ensure the `gh` CLI is installed before it's used. This will prevent cryptic errors for users who don't have the `gh` CLI installed, improving the script's robustness and user experience.

AI Summary: This GitHub issue, marked as a 'Good First Issue', aims to improve code documentation by adding missing class-level docstrings to several Pydantic view model classes. These models represent compiled output structures for Kibana dashboards. The task involves identifying relevant files, reviewing existing docstring patterns, and adding descriptive docstrings to enhance auto-generated API docs and aid new contributors.

AI Summary: This issue proposes adding support for two new specialized panel types in a dashboard compiler: collapsible sections for better organization and alert panels to display alerts from Observability/Security applications. The implementation will involve creating new configuration, view, and compilation logic files for each panel type and updating existing type definitions.