Analytics & Monitoring Agent Skills

Half-Quadratic Quantization for LLMs without calibration data.

Show DAG state, agent progress, and branch status for an AgentHub session.

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script.

Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help.

Use Hugging Face Hub via MCP server tools.

Fast tokenizers optimized for research and production.

Hydrological modelling expert who develops, maintains, and critically reviews forecast models within SAPPHIRE.

Still-to-video conversion guide: model selection, motion prompting,

Implements security monitoring using Datadog Cloud SIEM, Cloud Security

Imports existing Grafana dashboards as Go builder code via gcx.

Run an improve-my-MCP campaign: an autoresearch-style loop that measures the MCP agent experience with the eval harness, picks the…

Production incident response. Use when handling incidents, classifying severity, reconstructing timelines, writing postmortems, generating…

Incident severity classification, runbook templates, root cause analysis, and post-incident review

Explains how Indian equity market data is actually structured, so a price series can be read and trusted.

Rapid pathogen characterization and drug repurposing for outbreaks.

Track and analyze portfolio company performance against plan.

Install the Datadog Agent on Kubernetes using the Datadog Operator — required before enabling Single Step Instrumentation (SSI), which…

Add Pydantic Logfire observability to applications.

Build self-contained interactive HTML dashboards with Chart.js, dropdown filters, and professional styling.

Investigate recent errors and exceptions in your Sentry projects

Evidence collection methods, problem categorization, analysis techniques, and solution design patterns