23 plugins de marketplaces públicos de Claude Code. Cada uno agrupa skills, comandos de barra, agentes, hooks o MCP servers que se instalan juntos.
Mostrando 23 plugins que coinciden con estos filtros.
magnus-agent-skillsmagnus919/agent-skills3
testland-qatestland/qa2
xpoz-pluginsXPOZpublic/xpoz-agent-skills1
claude-code-plugins-plusjeremylongshore/tons-of-skills-marketplace1Envía el repo con .claude-plugin/marketplace.json y listaremos cada plugin.
Enviar marketplaceMetrics collection, logging infrastructure, distributed tracing, SLO implementation, and monitoring dashboards
Error tracing, root cause analysis, and smart debugging for production systems
Distributed system tracing and debugging across microservices
Social media intelligence powered by Xpoz. Search, analyze, and export data from Twitter/X, Instagram, Reddit, and TikTok. Includes 13 skills: best practices reference for query syntax, pagination, field selection and all platform tools; getting started onboarding and setup; social tracking for continuous keyword/user/hashtag monitoring; sentiment analysis across platforms; Twitter data export to CSV (up to 500K rows); influencer discovery and ranking; Reddit discussion research; competitive intelligence and brand comparison; security OSINT and threat monitoring; AI answer tracing (the searches, retrieved pages, and cited sources behind Claude, ChatGPT, and Gemini answers); GEO visibility checking (where a brand appears in AI answers and who wins instead); GEO Reddit influence mapping (the subreddits and threads AI engines cite); buying-intent lead scanning across the four platforms.
Set up distributed tracing for microservices
LLM and prompt evaluation with Promptfoo, DeepEval and Ragas, Giskard vulnerability scans, Langfuse tracing, and golden-dataset regression suites. Audits eval suites for methodology errors. Agents (prompt-eval-reviewer, llm-red-team-planner) review an eval suite and plan adversarial probe campaigns across the OWASP LLM Top 10.
ai-mlDistributed tracing tests with OpenTelemetry: assert span names, attributes, parent links and status, and query Jaeger, Zipkin or Tempo in CI. Validates OpenTelemetry Collector pipelines and writes per-feature trace specs. A review agent (trace-coverage-reviewer) finds untraced critical paths and spans missing semantic-convention attributes.
backend-distributedLLM observability with Langfuse — OpenTelemetry-based tracing, evaluations, prompt management, datasets, and production best practices
LangChain.js patterns for building LLM applications — chat models, LCEL chains, prompt templates, structured output, agents, tools, RAG, streaming, and LangSmith tracing
AI integration review patterns. Use when reviewing model API calls, prompt construction, LLM output handling, RAG pipelines, and tool-calling code. Covers prompt-injection call-chain tracing, output validation, stop reasons, token budgets, retry and timeout handling, streaming, and key/PII exposure.
Plan static inspection of unknown binary files with a deterministic CLI whose Ghidra operations are currently unimplemented and fail closed. Use when you need to inspect a PE, ELF, or Mach-O file — triage suspicious binaries, map imported APIs, decompile functions, trace call paths, or produce structured evidence reports. Do not use for runtime analysis (debugging, dynamic tracing, sandbox execution), for modifying or patching binaries, or for binaries you already know everything about. The skill owns planning, hypothesis formation, and evidence synthesis; the CLI owns all deterministic operations.
Use when someone asks what is true, false, misleading, unsupported, unknown, or genuinely complicated in a persuasive message, article, pitch, advertisement, policy claim, or viral post. Analyze propaganda, spin, selective framing, urgency, social proof, deceptive marketing, and AI-generated persuasion by tracing claims to evidence and separating literal truth from implied conclusions. Do not use to read deception from demeanor, adjudicate intent, or replace broad domain research.
Operate the observability stack that deploys as one unit: Prometheus scrape configuration, recording and alerting rules, relabeling, retention, and high availability; OpenTelemetry Collector pipelines (receivers, processors, exporters, sampling, trace/span correlation); and Loki ingest, LogQL, retention, and label design — with a bundled read-only telemetry-check script for Prometheus rule sanity and scrape-target reachability. Use when running, tuning, or troubleshooting a Prometheus, OpenTelemetry Collector, or Loki deployment, or reviewing the collection/ingest/retention layer, including bounded PromQL and LogQL query construction, semantic review, and no-data diagnosis. Do not use for observability strategy, SLI/SLO design, or paging policy (that is platform-engineering), Grafana dashboards, panels, and Grafana-side alerting (that is grafana), or Tempo/tracing operations, which remain deferred to a future named-tool skill.
Phoenix AI observability skills for LLM application debugging, evaluation, and tracing. Includes CLI debugging tools, LLM evaluation workflows, and OpenInference tracing instrumentation.
Skills for AI production operations: cost optimization, model switching, monitoring, request tracing, experiment tracking, safety testing, and error debugging
Datadog CLI for searching logs, querying metrics, tracing requests, and managing dashboards. Use when debugging production issues or working with Datadog observability.
engram + skillet MCP servers and toolkit skills (local memory, skill management, agent tracing) — everything runs on your machine.
productivityProbe-falsify-escalate skill + inquisitor-mcp server (web search, project analysis, code tracing, Newton 7-phase state machine).
Use when you need to implement or improve distributed tracing with OpenTelemetry in Java — including trace/span modeling, context propagation, semantic conventions, span attributes/events/status, sampling strategy, baggage usage, privacy safeguards, and backend integration with OTLP collectors.
Claude Code hooks for workflow tracing and observability.
