搜索「Observability」的结果

io.github.tsviz/actions-pulse

Real-time GitHub Actions observability: DORA Metrics, Cost Analysis, CI/CD Health dashboards.

io.github.geored/lumino

AI-powered SRE observability for Kubernetes/OpenShift with 40+ Tekton debugging tools

io.github.Dewars30/fulcrum

AI governance MCP server for policy enforcement, cost control, and observability.

io.github.TANTIOPE/datadog-mcp

Full Datadog API access: monitors, logs, metrics, traces, dashboards, and observability tools

com.newrelic/mcp-server

Access New Relic observability data through MCP - query metrics, logs, traces, entities, and more

io.github.browser-use/browser-use/open-source

Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browser_use, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle hooks, MCP server setup, or monitoring/observability with Laminar or OpenLIT. Also trigger for questions about browser-use installation, prompting strategies, or sensitive data handling. Do NOT use this for Cloud API/SDK usage or pricing — use the cloud skill instead. Do NOT use this for directly automating a browser via CLI commands — use the browser-use skill instead.

io.github.PostHog/posthog/exploring-llm-traces

Debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs. Also use when raw SQL/HogQL against `events.properties.$ai_input` / `$ai_output_choices` returns empty — message content lives only on the dedicated `posthog.ai_events` table.

io.github.mukul975/Anthropic-Cybersecurity-Skills/implementing-runtime-security-with-tetragon

Implements eBPF-based runtime observability and in-kernel enforcement in Kubernetes with Cilium Tetragon, monitoring process execution, file access, network connections, and syscalls, and blocking dangerous calls at the kernel level. Use when deploying Tetragon to detect or block syscalls such as ptrace, mount, and unshare, enforcing kernel-level policy, or adding low-overhead runtime detection to a cluster. Keywords: Tetragon, Cilium, eBPF, TracingPolicy, kprobe, enforcement, process lineage. Do not use for Falco-based detection - use detecting-container-runtime-threats-with-falco.

io.github.affaan-m/ECC/enterprise-agent-ops

Operate long-lived agent workloads with observability, security boundaries, and lifecycle management. Use when running long-lived agent workloads that need observability, security boundaries, or lifecycle control.

io.github.microsoft/ai-agents-for-beginners/deploying-scalable-agents

Dalhin ang isang gumaganang prototype ng agent sa isang scalable, observable na production deployment sa Microsoft Foundry. Saklaw nito ang mga deployment pattern (client-hosted, hosted agents, agent workflows), ang lifecycle ng agent, model routing, response caching, evaluation gates, human-in-the-loop approval, observability gamit ang OpenTelemetry, cost optimisation, at smoke-testing ng mga deployed na agent gamit ang AI Smoke Test action. Batay sa Lesson 16 ng AI Agents for Beginners. GAMITIN PARA SA: pag-deploy ng agent sa production, pag-scale ng agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test ng hosted agent, production customer support agent. HUWAG GAMITIN PARA SA: pagbuo ng iyong unang agent (simulan sa Lesson 01), pagpapatakbo ng mga agent nang lokal sa device (gamitin ang local-ai-agents / Lesson 17), Azure infrastructure prov

io.github.pollinations/pollinations/tinybird-deploy

Deploy Tinybird pipes and datasources for enter.pollinations.ai observability with the Tinybird Forward CLI.

io.github.microsoft/ai-agents-for-beginners/deploying-scalable-agents

Take one working agent prototype go scalable, observable production deployment for Microsoft Foundry. E cover deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluation gates, human-in-the-loop approval, observability with OpenTelemetry, cost optimisation, and smoke-testing deployed agents with the AI Smoke Test action. Based on Lesson 16 of AI Agents for Beginners. USE FOR: deploy one agent go production, scale one agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test one hosted agent, production customer support agent. DO NOT USE FOR: building your first agent (start with Lesson 01), running agents locally on-device (use local-ai-agents / Lesson 17), Azure infrastructure provisioning we no relate to agents, non-Foundry deployment targets.

io.github.microsoft/ai-agents-for-beginners/deploying-scalable-agents

Take a working agent prototype to a scalable, observable production deployment on Microsoft Foundry. Covers deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluation gates, human-in-the-loop approval, observability with OpenTelemetry, cost optimisation, and smoke-testing deployed agents with the AI Smoke Test action. Based on Lesson 16 of AI Agents for Beginners. USE FOR: deploy an agent to production, scale an agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test a hosted agent, production customer support agent. DO NOT USE FOR: building your first agent (start with Lesson 01), running agents locally on-device (use local-ai-agents / Lesson 17), Azure infrastructure provisioning unrelated to agents, non-Foundry deployment targets.

io.github.microsoft/ai-agents-for-beginners/deploying-scalable-agents

Take a working agent prototype to a scalable, observable production deployment on Microsoft Foundry. Covers deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluation gates, human-in-the-loop approval, observability with OpenTelemetry, cost optimisation, and smoke-testing deployed agents with the AI Smoke Test action. Based on Lesson 16 of AI Agents for Beginners. USE FOR: deploy an agent to production, scale an agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test a hosted agent, production customer support agent. DO NOT USE FOR: building your first agent (start with Lesson 01), running agents locally on-device (use local-ai-agents / Lesson 17), Azure infrastructure provisioning unrelated to agents, non-Foundry deployment targets.

io.github.compozy/compozy/testing-boss

Testing doctrine for tests that reveal bugs instead of passing for the wrong reason — spanning software and LLM/AI systems. Use when authoring or reviewing tests, adding a mock, deciding where a test belongs, letting a coding agent generate tests, triaging flaky CI, designing an eval suite for an LLM/agent feature, or rebuilding a brittle suite. Not for general code review, library debugging unrelated to tests, CI pipeline design beyond tests, or production observability.

io.github.lobehub/lobehub/agent-signal

Build or extend LobeHub Agent Signal pipelines. Use for signal sources, signal/action types, policies, middleware, workflow handoff, dedupe, scope behavior, or observability.

io.github.Arize-ai/phoenix/phoenix-frontend

Frontend development guidelines for the Phoenix AI observability platform. Use when writing, reviewing, or modifying React components, TypeScript code, styles, or UI features in the app/ directory. Triggers on any frontend task — new components, UI changes, styling, accessibility fixes, form handling, or component refactoring. Also use when the user asks about frontend conventions or component patterns for this project. For design system rules (error display, layout, dialogs, tokens), use the phoenix-design skill.

io.github.telagod/code-abyss/backend

Backend engineering judgment, distilled from a stronger model - invoke when CHOOSING a tech stack, language, database, queue, or architecture; designing a service, API, business logic, or schema; making a system production-ready (observability, failure handling, security); or reviewing server-side code and judging codebase health. Scenario-driven stack tradeoffs, logic-design rules, data discipline, production floors, and a rot catalog (the early signs of unmaintainable code).

io.github.PostHog/posthog/exploring-apm-traces

Investigates distributed application performance using PostHog APM (OpenTelemetry span) data via MCP. Use when the user asks about service traces, slow HTTP/database spans, error spans, error-rate trends or spikes, latency distributions, trace IDs, or span attributes — not AI observability traces or product logs. Uses posthog:query-apm-spans, posthog:apm-trace-get, posthog:apm-spans-sparkline, posthog:apm-services-list, posthog:apm-attributes-list, and posthog:apm-attribute-values-list.

io.github.seaworld008/Commonly-used-high-value-skills/kubernetes-specialist

Use when managing Kubernetes clusters, debugging Pods and workloads, designing Helm charts, reviewing manifests, or improving deployment, scaling, and observability practices.

skillsmp.lukasniessen-kubernetes-skill-skill-md

Prevent Kubernetes hallucinations by diagnosing and fixing failure modes: insecure workload defaults, resource starvation, network exposure, privilege sprawl, fragile rollouts, and API drift. Use when generating, reviewing, refactoring, or migrating manifests, Helm charts, Kustomize overlays, cluster policies, and platform-specific Kubernetes work for EKS, GKE, AKS, OpenShift, GitOps controllers, or observability stacks.

io.github.alirezarezvani/claude-skills/slo-architect

Use when defining, reviewing, or operating SLOs/SLIs/error budgets. Triggers on "define an SLO", "what should our SLO be", "error budget", "burn rate", "SLI", "service level objective", "Google SRE workbook", "multi-window burn-rate alert", or any reliability-target question. Ships SLO designer, error-budget calculator with multi-window burn-rate thresholds, and SLO reviewer that catches the common bugs (target too aggressive, window too short, conflicting SLOs, no SLI definition). 4 references on SLO principles + SLI design + error budget math + composition with feature-flags-architect/chaos-engineering/kubernetes-operator. NOT a generic observability skill — specifically the SLO discipline.

io.github.alirezarezvani/claude-skills/engineering-advanced-skills

Index of 37 advanced engineering agent skills for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Use when browsing or choosing among the POWERFUL-tier engineering skills: agent design, RAG, MCP servers, CI/CD, database design, observability, security auditing, changelog/release automation, reliability (SLO/chaos/flags/operators), platform ops.

io.github.mukul975/Anthropic-Cybersecurity-Skills/implementing-ebpf-security-monitoring

Implements eBPF-based security monitoring using Cilium Tetragon for real-time process execution tracking, network connection observability, file access auditing, and runtime enforcement. Covers TracingPolicy CRD authoring with kprobe/tracepoint hooks, in-kernel filtering via matchArgs/matchBinaries selectors, JSON event export, and integration with SIEM pipelines. Use when building kernel-level runtime security observability for Linux hosts or Kubernetes clusters.