Results for “datagma”

Query and filter Datadog logs from the shell using the Composio CLI, enabling scoped log searches, pivoting across services and environments, and exporting structured JSON for downstream analysis.

Synchronizes Datadog monitor definitions between environments using the Datadog API v2 monitors endpoint, with diff-based updates, tag filtering, and Terraform state reconciliation.

Validates that a Dynamo deployment's NIXL/UCX/NCCL interconnect is ready for disaggregated serving over RDMA/NVLink. Use after deploying a disagg or multi-node recipe to confirm KV transport is correct, or use troubleshoot for already-failed pods.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Diagnose and observe an Oxla distributed analytical database using system catalog tables, Prometheus metrics, runtime log-level changes, and troubleshooting workflows for slow queries, node health, and memory/OOM pressure. Also covers debugging Oxla's external data sources, including the Redpanda/Kafka ingestion path.

Build streaming data pipelines with Redpanda Connect using declarative YAML configs, Bloblang mappings, and component discovery. Covers running, linting, and dry-running pipelines.

Deploy Meta's LlamaGuard moderation model to filter LLM inputs and outputs across 6 safety categories using HuggingFace, vLLM, or FastAPI.

Streams change data capture from PostgreSQL into Redpanda or Kafka using Redpanda Connect's postgres_cdc input, covering setup, snapshotting, and troubleshooting.

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`.

Monitors Datadog Service Level Objectives and burn rate alerts via the Datadog API v2. Generates SLO compliance reports and triggers remediation workflows when error budgets are exhausted.

Provides guidance and best practices for working with Datadog, covering setup, configuration, monitoring, and troubleshooting.