「traces」の検索結果

Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, review experiments, inspect datasets, and query the GraphQL API. Use when debugging AI/LLM applications, analyzing trace data, working with Phoenix observability, or investigating LLM performance issues.

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Guidelines for fixing unhandled errors from the VS Code error telemetry dashboard. Use when investigating error-telemetry issues with stack traces, error messages, and hit/user counts. Covers tracing data flow through call stacks, identifying producers of invalid data vs. consumers that crash, enriching error messages for telemetry diagnosis, and avoiding common anti-patterns like silently swallowing errors.

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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.

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Build PowerPoint decks headless with python-pptx. Pairs with excel-author for model-backed decks where every number traces to a workbook cell. Use for pitch decks, IC memos, earnings notes.

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Search logs and codebases for error patterns, stack traces, and anomalies. Correlates errors across systems and identifies root causes. Use PROACTIVELY when debugging issues, analyzing logs, or investigating production errors.

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Adds observability. OpenTelemetry traces/metrics/logs, health checks, custom metrics.

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Traces ransomware cryptocurrency payment flows using blockchain analysis tools such as Chainalysis Reactor, WalletExplorer, and blockchain.com APIs. Identifies wallet clusters, tracks fund movement through mixers and exchanges, and supports law enforcement attribution. Activates for requests involving ransomware payment tracing, bitcoin wallet analysis, cryptocurrency forensics, or blockchain intelligence gathering.

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E2E artifact handling routing. Use when traces, videos, screenshots, reports, or failure bundles need controlled repository-local placement; do not use for simple unit tests or production-mutating E2E flows.

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Hunt Spring Boot specific vulnerabilities — Actuator endpoints (heapdump, env, loggers, mappings, shutdown), Spring Expression Language (SpEL) injection → RCE, H2 console RCE, Jolokia JMX exposure, Spring4Shell (CVE-2022-22965), Spring Cloud Function SPEL (CVE-2022-22963), heap dump credential extraction. Use when target runs Spring Boot — detected via X-Application-Context header, /actuator, Whitelabel Error Page, or Java stack traces.

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Complete observability & reliability engineering system. Use when designing monitoring, implementing structured logging, setting up distributed tracing, building alerting systems, creating SLO/SLI frameworks, running incident response, conducting post-mortems, or auditing system reliability. Covers all three pillars (logs/metrics/traces), alert design, dashboard architecture, on-call operations, chaos engineering, and cost optimization.

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Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.

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Implementing distributed tracing with W3C Trace Context — the byte-precise `traceparent` and `tracestate` header formats, OpenTelemetry's W3CTraceContextPropagator as the modern default (replacing X-B3-* and X-Datadog-*), head vs tail sampling, and HTTP→SQL trace propagation via sqlcommenter. Grounded in the W3C REC and OpenTelemetry specs. NOT for OTel metrics/logs pipelines, vendor backend setup (Datadog/Honeycomb/Jaeger/Tempo), or browser RUM tracing.

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Designs or reviews provenance-bound DAG attempt traces, coverage gaps, cancellation, and unknown-effect reconciliation. NOT for claiming execution completeness, deployment monitoring, or root-cause proof.

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