「tracing」の検索結果

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

日本語の概要は準備中です。原文の説明を表示しています。

Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.

日本語の概要は準備中です。原文の説明を表示しています。

Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span.* or confident.trace.* attributes; export AI-app traces without the deepeval Python package; wire an OTLPSpanExporter, OpenTelemetry Collector, or vendor-neutral OTel SDK to Confident AI; or pick the US vs EU OTLP endpoint. Language-agnostic: the mechanism is OTLP attribute keys plus an exporter endpoint. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill); for instrumenting with the DeepEval SDK's @observe decorator or framework integrations (use the `deepeval-tracing` skill); or for non-AI software such as web servers, CRUD backends, or infrastructure: the confident.* attributes describe AI components only.

日本語の概要は準備中です。原文の説明を表示しています。

Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.

日本語の概要は準備中です。原文の説明を表示しています。

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Comprehensive OSINT methodology skill for offensive security, red team intelligence gathering, and bug bounty reconnaissance. Covers domain recon, email harvesting, social media profiling, GitHub/code leaks, Shodan/Censys enumeration, breach data lookup, employee profiling, infrastructure mapping, cryptocurrency tracing, geospatial intelligence, and AI-assisted analysis workflows. Use when performing reconnaissance against a target domain or organization, investigating a person or entity, tracing cryptocurrency flows, geolocating images or events, or building an attack-surface map.

日本語の概要は準備中です。原文の説明を表示しています。

Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments. Covers the 5-stage recon pipeline (seed discovery, asset expansion, enrichment, exposure analysis, reporting), asset-graph discipline with 29 asset types, severity rubric (CRITICAL/HIGH/MEDIUM/LOW/INFO), confidence upgrade workflows, time budgeting, asset-level triage rules, scale-based tactics, identity-fabric mapping (Entra/Okta/ADFS/Google/SAML/M365 Teams+SharePoint+OAuth), API and auth-map methodology, JavaScript deep analysis, mobile attack surface, cloud attack surface, breach×identity correlation, detectability tagging, detection-aware probing (back-off, persona rotation), read-only validator discipline, WAF/CDN bypass + origin discovery, vulnerability prioritization (CVE/EPSS/KEV), phishing infrastructure planning + pretext development, bug bounty submission templates, client deliverable templates with risk translation, threat-actor investigation (incl. RU/CN pivots), cryptocurrency tracing, image/video forensics, chronolocation. Use when planning or executing reconnaissance against authorized targets, mapping an organization's external attack surface, investigating a person/entity, tracing crypto flows, geolocating media, or performing attribution work.

日本語の概要は準備中です。原文の説明を表示しています。

Interactive system flow tracing across CODE, API, AUTH, DATA, NETWORK layers with SQLite persistence and Mermaid export. Use for security audits, compliance documentation, flow tracing, feature ideation, brainstorming, debugging, architecture reviews, or incident post-mortems. Triggers on audit, trace flow, document flow, security review, debug flow, brainstorm, architecture review, post-mortem, incident review.

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Debug the running local stack with traces, logs, and a shared headless browser. Use when investigating a bug in a running service, tracing a request across services, reading service logs, reproducing a frontend issue, or writing/reviewing tracing instrumentation.

日本語の概要は準備中です。原文の説明を表示しています。

Braintrust tracing for Claude Code - hook architecture, sub-agent correlation, debugging

日本語の概要は準備中です。原文の説明を表示しています。

Golang everyday observability — the always-on signals in production. Covers structured logging with slog, Prometheus metrics, OpenTelemetry distributed tracing, continuous profiling with pprof/Pyroscope, server-side RUM event tracking, alerting, and Grafana dashboards. Apply when instrumenting Go services for production monitoring, setting up metrics or alerting, adding OpenTelemetry tracing, correlating logs with traces, migrating legacy loggers (zap/logrus/zerolog) to slog, adding observability to new features, or implementing GDPR/CCPA-compliant tracking with Customer Data Platforms (CDP). Not for temporary deep-dive performance investigation (→ See `samber/cc-skills-golang@golang-benchmark` and `samber/cc-skills-golang@golang-performance` skills).

日本語の概要は準備中です。原文の説明を表示しています。

Add file-based observability, tracing, and run analysis to LLM/agent experiments — per-turn transcripts, tool calls and results, reasoning traces, token/cost accounting, per-episode artifacts, plus terminal viewers and a run-comparison report. Platform-agnostic: one small adapter covers OpenAI, Gemini/Vertex, Anthropic, LiteLLM, Tinker, vLLM, or any custom inference stack, and the same tooling then works unchanged. Use this skill whenever the user wants observability, tracing, logging, or run tracking for model experiments; asks why an agent failed a task or wants to replay/debug/diff an episode; wants to compare models, arms, or ablations; wants to instrument a new folder, repo, or platform ("add observability here", "do the same for Tinker/Gemini/OpenAI"); wants to import or tie in existing experiment results so old runs show up alongside new ones; or wants eval runner scripts where many models share one protocol. Reach for this even when they only say "log what the model is doing", "I can't tell what happened in this run", or "set up traces for this experiment".

日本語の概要は準備中です。原文の説明を表示しています。

LangSmith tracing and debugging setup for LLM applications. Configure observability, capture traces, and enable debugging for LangChain/LangGraph agents.

日本語の概要は準備中です。原文の説明を表示しています。

You are a debugging expert specializing in setting up comprehensive debugging environments, distributed tracing, and diagnostic tools. Configure debugging workflows, implement tracing solutions, and establish troubleshooting practices for development and production environments.

日本語の概要は準備中です。原文の説明を表示しています。

Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.

日本語の概要は準備中です。原文の説明を表示しています。

Specify the tracing, metrics, and alerting for an AI agent or LLM feature in production. Use when asked what to log for an LLM app, design agent tracing or spans, define quality and cost monitors, or answer 'how do we know if the agent is misbehaving?'. Produces an observability spec with a trace schema, metric definitions with owners and alert thresholds, sampling and retention policy, and a privacy note for logged content.

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Provides guidance for implementing OpenTelemetry instrumentation in .NET codebases, covering tracing (Activities/Spans), metrics, logs, naming conventions, error handling, performance, SDK setup, resources, context propagation, and API design best practices.

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Integrate antennas into RF systems using MATLAB Antenna Toolbox and RF Toolbox. Covers impedance matching network design (L/Pi/Tee topologies, evaluation parameters, Richards transformation), measured antenna creation (E-field, directivity-only, EmbeddedE, ffsReader import), RF propagation and site planning (txsite/rxsite, coverage, SINR, ray tracing, link budget), and SAR estimation (birdcage+Phantom, conformalArray+Custom3D, direct EHfields). Use when the user wants to match an antenna, create a measuredAntenna, compute coverage or signal strength, perform ray tracing, or estimate SAR.

日本語の概要は準備中です。原文の説明を表示しています。

Instrument Spring Boot with Micrometer metrics, distributed tracing, and structured logging. Use when adding Micrometer metrics, distributed tracing, or structured logging to Spring Boot.

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Enforce structured JSON logging, OpenTelemetry distributed tracing, and RED metrics across backend services. Use when adding request correlation, setting up tracing spans, defining SLO burn-rate alerts, or instrumenting middleware.

日本語の概要は準備中です。原文の説明を表示しています。

Braintrust tracing for Claude Code - hook architecture, sub-agent correlation, debugging

日本語の概要は準備中です。原文の説明を表示しています。