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LangChain Observability Setup

langchain-observability

jeremylongshore/claude-code-plugins-plus-skills

Guide for instrumenting LangChain applications with LangSmith tracing, Prometheus metrics, OpenTelemetry spans, structured logging, Grafana dashboards, and alerting to monitor health and performance in staging or production.

Langfuse LLM Tracing Workflow

langfuse-core-workflow-a

jeremylongshore/claude-code-plugins-plus-skills

Guides setup of Langfuse tracing for LLM pipelines, covering automatic OpenAI wrapping, manual spans, streaming responses, and parent-child trace linking for end-to-end observability.

Langfuse Trace Starter

langfuse-hello-world

jeremylongshore/claude-code-plugins-plus-skills

Provides a minimal Langfuse trace example that initializes the client, records spans and generations, flushes data, and logs a trace URL—ideal when starting integrations, validating setup, or learning trace patterns.

Langfuse SDK Patterns

langfuse-sdk-patterns

jeremylongshore/claude-code-plugins-plus-skills

Guides Langfuse SDK best practices—singleton clients, trace lifecycle updates, nested spans, Python decorators, session tracking, and scoring—for reliable LLM observability.

Sentry Performance Tracing

sentry-performance-tracing

jeremylongshore/claude-code-plugins-plus-skills

Configure Sentry to monitor application performance, enable distributed tracing, and visualize spans so teams can identify bottlenecks and validate that requests propagate through services as expected.

Gitleaks Secret Scanning

implementing-secret-scanning-with-gitleaks

mukul975/Anthropic-Cybersecurity-Skills

Define a Gitleaks-driven secret scanning workflow that spans repo baseline scans, pre-commit hooks, GitHub Actions CI/CD runs, custom detection rules, and remediation steps to stop hard-coded credentials from entering source control.

Golang Testing Guidelines

samber/cc-skills-golang

Comprehensive Go testing guidance that spans table-driven subtests, testify suites, mocking, unit/integration tests, benchmarks, coverage, fuzzing, goleak leak detection, snapshot checks, and GitHub Actions CI to keep production suites reliable.

Managing Annotation Configs and Feedback

arize-annotation

github/awesome-copilot

This skill manages the schema for human feedback (annotation configs) within the Arize platform. It allows users to define label types (categorical, continuous, freeform) and programmatically apply these annotations to project spans using the Python SDK. Essential for establishing standardized human review processes and improving model evaluation.

Generate Deep Links to Arize UI

github/awesome-copilot

This skill generates highly precise deep links to various resources within the Arize platform, including traces, spans, sessions, datasets, and evaluators. It requires collecting specific resource IDs (e.g., project_id, dataset_id) and managing time ranges (startA/endA) to ensure the user is taken to the exact view, optimizing the user experience for ML practitioners and data scientists.

Arize Evaluator Workflow

arize-evaluator

github/awesome-copilot

Defines and runs LLM-as-judge evaluators on Arize via ax commands, handling evaluator creation, continuous monitoring, column mapping, trigger runs, and evaluation spans or experiments with configurable templates, models, and query filters.

Arize Instrumentation Assistant

arize-instrumentation

github/awesome-copilot

Guides adding Arize AX tracing to apps via an agent-assisted two-phase flow, emphasizing read-only analysis before manual instrumentation, favoring auto-integration, and adding manual chain/tool spans for LLM tool calls.

Arize Trace Export Tool

github/awesome-copilot

Downloads spans, traces, and sessions from Arize via the ax CLI to debug LLM applications, using IDs, filters, sampling rules, and bulk/export safeguards while writing output to .arize-tmp-traces.

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