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Found 311 Skills
Set up Apollo.io monitoring and observability. Use when implementing logging, metrics, tracing, and alerting for Apollo integrations. Trigger with phrases like "apollo monitoring", "apollo metrics", "apollo observability", "apollo logging", "apollo alerts".
strace and ltrace skill for system call and library call tracing. Use when a binary behaves incorrectly without crashing, diagnosing file-not-found errors, permission failures, network issues, or unexpected library calls by tracing syscalls and library function calls. Activates on queries about strace, ltrace, syscall tracing, library interception, ENOENT, EPERM, strace -e, or diagnosing binary behaviour without a debugger.
INVOKE THIS SKILL when adding Arize AX tracing to an application. Follow the Agent-Assisted Tracing two-phase flow: analyze the codebase (read-only), then implement instrumentation after user confirmation. When the app uses LLM tool/function calling, add manual CHAIN + TOOL spans so traces show each tool's input and output. Leverages https://arize.com/docs/ax/alyx/tracing-assistant and https://arize.com/docs/PROMPT.md.
Integrates Kelet into AI applications end-to-end: instruments agentic flows with OTEL tracing, maps session boundaries, adds user feedback signals (VoteFeedback, edit tracking, coded behavioral hooks), generates synthetic signal evaluator deeplinks, and verifies the integration. Kelet is an AI agent that performs Root Cause Analysis on AI app failures — it ingests traces and signals, clusters failure patterns, and suggests fixes. Use when the developer mentions Kelet or asks to integrate, set up, instrument, or add tracing/signals/feedback to their AI app. Triggers on: "integrate Kelet", "set up Kelet", "add Kelet", "instrument my agent", "connect Kelet", "use Kelet".
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework with built-in backward tracing for deep-stack failures, ensuring root-cause understanding before implementation
Builds composable, pipeable function chains on the iii engine. Use when building functional pipelines, effect systems, or typed composition layers where each step is a pure function with distributed tracing and retry.
Set up orq.ai observability for LLM applications. Use when setting up tracing, adding the AI Router proxy, integrating OpenTelemetry, auditing existing instrumentation, or enriching traces with metadata.
Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.
Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.
Observability guidelines for distributed systems using OpenTelemetry, tracing, metrics, and structured logging
Implement request logging, tracing, and observability. Use for debugging, monitoring, and production observability.
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.