「trace」の検索結果

Use when measuring or improving agent quality and performance — set up evaluators, online monitoring, CI/CD quality gates, observability, or cost optimization. Triggers on: "evaluate my agent", "add evaluator", "measure quality", "quality gate", "run evals", "agent too slow", "why is it slow", "reduce latency", "set up observability", "CloudWatch dashboard", "how much does my agent cost", "cost optimization", "logs not showing up", "logs missing", "spans not found", "eval failing", "eval error", "dev traces", "local traces", "agentcore dev traces", "traces to CloudWatch". Not for debugging errors or crashes — use agents-debug. Slow but correct routes here; broken routes to debug.

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Use `pvanalyze` to inspect existing .NET `.nettrace` files from the command line, including GC, JIT, CPU stacks, allocation, DATAS, events, exceptions, timeline, and call-tree analysis with JSON or SpeedScope. USE FOR: the user mentions pvanalyze, PerfView-style CLI trace analysis, or cross-platform .nettrace inspection; the task starts from an existing .nettrace file and needs. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Turn any source that describes how a kind of task gets done (a SKILL.md, a chat log, a runbook, plain prose) into a runnable Morph trace. Lift it into a DAG, write a contract per step, place inputs, and drive the full run lifecycle to verify it. Use whenever someone wants to make a trace from a source.

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Use when you need to run a binary, trace execution, or observe runtime behavior. Runtime analysis via QEMU emulation, GDB debugging, and Frida hooking - syscall tracing (strace), breakpoints, memory inspection, function interception. Keywords - "run binary", "execute", "debug", "trace syscalls", "set breakpoint", "qemu", "gdb", "frida", "strace", "watch memory"

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Instruments programs to capture execution traces specifically for reproducing reported bugs, enabling consistent replay and diagnosis of failures. Use this skill when you need to reproduce a bug, capture execution traces for debugging, instrument code to record program behavior, generate replay scripts for bug reproduction, diagnose hard-to-reproduce failures, or perform deterministic replay of program execution. Triggers when users ask to instrument code for tracing, capture execution traces, reproduce bugs, generate replay scripts, or enable deterministic debugging.

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Think and work like an expert Chemical Oceanographer. Use when a task calls for Chemical Oceanographer judgment. Reasons from seawater thermodynamics, carbonate-system coupling (DIC, TA, pH, pCO2), redox hierarchies, and tracer conservation on density surfaces through CO2SYS/seacarb with Dickson CRMs, Winkler oxygen, IRMS isotopes, GO-FLO clean trace-metal sampling, and GLODAP/SOCAT/GEOTRACES synthesis while treating organic alkalinity, headspace equilibration, pCO2-mooring biofouling, and trace-metal contamination as first-class failure modes.

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Run trace-mcp security, quality-gate, and antipattern checks before committing or opening a PR. Activate when the agent is about to create a commit or pull request in a project indexed by trace-mcp.

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Use trace-mcp tools for code navigation, impact analysis, and framework-aware queries instead of Read/Grep/Glob/Bash. Activate whenever the agent needs to explore, understand, or modify a codebase that has trace-mcp indexed.

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Mine a per-kernel optimization trace — a git repository capturing successive versions of one kernel being optimized — into structured, gate-validated optimization-experience records for the GPU kernel wiki. Use when asked to distill an optimization run, a kernel_opt trace directory or a version ladder into wiki records; to report what such a run actually achieved; to build, extend, re-run or validate the staging store behind those records; or to explain how a trace-derived record's number, snippet or provenance was established.

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Analyze production Agentforce agent behavior using session traces and Data Cloud, and manage Agent Health Monitoring (AHM) alerts. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries; creates, lists, updates, or deletes an AHM data alert on an agent metric (escalation rate, deflection, etc.); asks why an alert is not firing or wants to check whether alerts have fired (notification counts, or the per-alert Incidents view). DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use agentforce-generate); writes or runs test specs (use agentforce-test); uses sf agent preview for local development iteration; deploys or publishes agents.

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当用户想使用 HiAgent 观测(observe)能力（API Token、Trace Spans 查询）时，务必使用这个技能给出可直接执行的 cli-anything-hiagent observe 命令与排障步骤。尤其适用于用户提到 agent-harness、cli_anything、--json、workspace-id、custom-app-id、VOLC_ACCESSKEY/VOLC_SECRETKEY、observe token、observe trace，以及希望用命令行(cli-anything-hiagent / python -m cli_anything.hiagent_sdk)。trace-ai-process / trace-ai-history / alert-ai-process / RuleID / IsStream

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Jaeger provider for obz. Covers trace search, trace retrieval by ID, and extension commands for service and operation discovery. This skill should be used when the user mentions "Jaeger", "obz trace -p jg", or needs to search distributed traces from a Jaeger backend.

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Analyzes API call traces from a sandbox or API monitor (JSON) to group calls by category, reconstruct high-level behaviors (process injection, file drops, network, crypto), and flag suspicious call sequences. Activates for requests to analyze an API trace, interpret sandbox API logs, or identify behavior from Win32/Native API calls.

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The investigation harness for "why is this slow?" over a Pulp Perfetto trace (.pftrace). Runs the hypothesis→query→drill-down chain-of-evidence loop autonomously and returns a plain-English root cause + evidence + a concrete fix. TRIGGER on "why is my plugin slow to open", "find the slowest frames", "why is the UI stuttering", "why is my plugin using so much CPU", "which DSP node is expensive", "the load meter looks calm but CPU is pinned", or a named `pulp trace gpu-startup|gpu-health|gpu-probe` analysis. Ships Pulp-specific hints for dsp, frame, js, gpu, and cross-platform symptoms.

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对 profiling 数据（kernel_details.csv / trace_view.json / step_trace_time.csv）做统一分析： 5 层递进管道（analyze_trace.py）+ baseline/优化后 kernel diff（compare_traces.py），输出 瓶颈诊断、算子执行序、可融合候选与 P0-P2 方向建议。输入统一来自 profiling-collect。 只要用户有 profiling 产出并问"为什么慢/瓶颈在哪/优化前后差多少/往哪个方向"，都用本技能；采集走 profiling-collect，方案选档走 dit-perf-opt，实现/并行/基准走 pattern-dev/dit-parallel-opt/benchmark-dev——本技能只做诊断与方向。 由 model-auto-optimization 的 S1（融合分析）/S3（并行）阶段调用，亦由 dev-workflow 的分析阶段指引加载。 融合机会候选只做**识别**；候选的**边界与收益判定**（能不能融成一个单元、融多大、值不值）交 `fusion-scope-analyze`（本技能的辅助技能）——本技能不判融合边界。

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Traces complete execution paths through DAG workflows. Records timing, inputs, outputs, and state transitions for all nodes. Activate on 'execution trace', 'trace execution', 'execution path', 'debug execution', 'execution log'. NOT for performance analysis (use dag-performance-profiler) or failure investigation (use dag-failure-analyzer).

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Use when working with Gcp Cloud Trace — google Cloud Trace latency analysis, trace exploration, sampling configuration, and span diagnostics via gcloud CLI.

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Designs and analyzes stable-isotope-resolved metabolomics (SIRM / isotope tracing / fluxomics) experiments that measure metabolic ACTIVITY via 13C/15N/2H tracers, distinct from steady-state pool profiling. Covers tracer choice, isotopologue vs isotopomer, mass-isotopomer distributions (MID), fractional enrichment, the mandatory natural-abundance + tracer-purity correction (IsoCor, AccuCor), and the metabolic/isotopic steady-state vs non-stationary (INST-MFA) distinction. Use when feeding a labeled tracer and interpreting labeling patterns, correcting raw isotopologue intensities, computing or plotting an MID, or deciding tracing vs abundance profiling. For absolute pool concentration and MRM mechanics see metabolomics/targeted-analysis; for constraint-based genome-scale flux (FBA, not empirical tracing) see systems-biology/flux-balance-analysis; for feature detection see metabolomics/xcms-preprocessing; for pathway enrichment that ignores the pool-vs-flux caveat see metabolomics/pathway-mapping.

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Compile your local stellar-build usage traces into sharper skills. Use when the user says "optimize my skills", "optimize-skills", "improve my skills from usage", "learn from my traces", "stellar-loop optimize", or wants the learning loop to refine installed skills based on how they've actually been used. DSPy-style prompt optimization over the local trace store at ~/.stellar-build/traces — fully local, reversible.

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Improve Coval trace quality after basic ingestion works. Use when traces are sparse, missing useful STT/LLM/TTS/tool spans, missing attributes needed for Coval built-in metrics, or when a customer wants maximum debugging and observability value from agent traces.

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This skill should be used when the user asks to "このエラーを直して", "help me debug this", "fix this stack trace", "なぜこのエラーが出るの", "CI が落ちた", or pastes an error message or stack trace and wants help diagnosing it. Classifies the error type, identifies related files, and proposes up to three root-cause hypotheses with concrete fix plans.

Trace agent execution by collecting spans and building a trace tree for a task

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Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when applying rejection sampling to model outputs, or when building DPO pairs from passing and failing runs.

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Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.

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。