「trace」の検索結果

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。

Apply when reviewing or shaping code that's hard to trace. Count layers between question and answer, and hidden state in the reader's head; collapse one-caller wrappers and shrink mutable scope.

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。

Apply when debugging. Trace each symptom to its root cause and fix it there; reproduce first, ask why until you reach it, resist nil-check guards that silence crashes.

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。

Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI

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。

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.

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 an error tracking and observability expert specializing in implementing comprehensive error monitoring solutions. Set up error tracking systems, configure alerts, implement structured logging, and ensure teams can quickly identify and resolve production issues.

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 an error tracking and observability expert specializing in implementing comprehensive error monitoring solutions. Set up error tracking systems, configure alerts, implement structured logging,

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。

Use when a test fails and you need to diagnose the root cause. Run the test, read errors, trace through generated and source code, fix, and verify.

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。

When a change has effects past the immediate fix—incentives, scale, feedback—trace consequence chains with timing and probability before committing.

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。

When a real test is too rare, large, or irreversible, run a controlled counterfactual: isolate one variable, fix conditions, trace the mechanistic chain, and bound what the result implies.

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。

Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

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。

Performs systematic root cause analysis to identify the true source of bugs, errors, and unexpected behavior through structured investigation phases — not just treating symptoms. Use when a user reports a bug, crash, error, or broken behavior and needs to debug, troubleshoot, or investigate why something is not working; especially for complex or intermittent issues across multiple components. Applies the Five Whys method, hypothesis-driven testing, stack trace analysis, git blame/log evidence gathering, and causal chain documentation to isolate and confirm root causes before applying any fix.

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。

Declarative OpenTelemetry-aligned telemetry vocabulary and instrumentation conventions for traces, metrics, logs, and PII handling

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。

Author and validate durable data and ML feasibility studies using the Feasibility Study Interchange Profile, constrained YAML authority, UUID URN identity, lifecycle lineage, and evidence traceability. Use when assessing whether available data and technical evidence support a proposed outcome.

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。

Build traceable internal-review proposal, RFI, RFP, tender, bid, and questionnaire responses from supplied questions and approved sources. Use to analyze questions, contribute business or product evidence, or draft qualified responses.

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。

Write code evaluators for known failure modes with objective rules. Use when code can check the rule from a trace, with or without a reference answer. Use `write-judge-prompt` when the rule requires interpretation.

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。

Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.

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。

Attribute GPU time in one sglang-omni stage to specific lines of `sglang_omni/` python source. Drives the `llm-torch-profiler-analysis` backend with a mapping/formal trace pair plus steady-state gates, and carries the rules for reading its three tables on an omni stage. Use when a stage is slower than expected and the question is which python code is the bottleneck, not which kernel is hot.

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。

Creates reusable, source-traced research reports and coordinates provider/mode lanes with original exports. Use for 深度研究, 调研报告, literature reviews, market or company research, and ChatGPT/Kimi/UniFuncs research routes. Technology choice uses tech-selection; competitor code uses competitors-analysis.

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。

Present a design to a client so they can make a decision rather than react to a picture — the brief restated, the moves explained against it, the options with their trade-offs, and a specific decision requested. Use when asked to present a design to a client, prepare a design review meeting, present concept options, or when a client keeps changing their mind after sign-off. Produces the presentation structure, the brief-to-design traceability, the options with honest trade-offs, the cost and programme position, and the decision record that makes sign-off stick.

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。

Run a blameless postmortem for an incident caused by an AI agent or LLM feature — hallucinated facts shipped to users, runaway tool use, prompt injection, cost blowouts, or wrong actions taken autonomously. Use when asked to write up an AI incident, analyse why an agent did something wrong, or produce corrective actions after an LLM failure. Produces a structured postmortem with trace reconstruction, a root-cause layer analysis, and corrective actions including a permanent regression case. For non-AI production incidents use incident-postmortem.

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.

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。

1:1 rebuild of award-winning creative websites (WebGL / scroll-animation / portfolio sites). Evidence-driven pipeline - mirror-first forensics, line-number-traceable reverse engineering of minified bundles, verbatim porting, quantitative verification gates. Use when user asks to "复刻网站", "重建网站", "1:1 rebuild", "clone this site", or provides a URL of a creative/award site to reproduce.

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。