1,837 件 ・ 関連度順
概要と使いどころ
Use when the user wants to send the same prompt to several LLMs and compare the answers - sequentially (stopwatch, clean per-lane timing) or in parallel (true race), with optional repetitions per model, judged by the starting model across quality, correctness, completeness, instruction fidelity and latency (time is only one dimension). Triggers on /compare-race, 'model race', 'vergleiche modelle', 'gleicher prompt an mehrere modelle'.
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。
Investigates distributed application performance using PostHog APM (OpenTelemetry span) data via MCP. Use when the user asks about service traces, slow HTTP/database spans, error spans, trace IDs, or span attributes — not AI observability traces or product logs. Uses posthog:query-apm-spans, posthog:apm-trace-get, posthog:apm-services-list, posthog:apm-attributes-list, and posthog:apm-attribute-values-list.
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。
ABSOLUTE MUST to debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs. Also use when raw SQL/HogQL against `events.properties.$ai_input` / `$ai_output_choices` returns empty — message content lives only on the dedicated `posthog.ai_events` table.
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。
Append structured execution traces across operational, cognitive, and contextual surfaces with minimal overhead. Load when inspecting agent runs, logging tool calls and observations, enabling post-run debugging, or pairing with structured-planning step IDs. Also triggers on "trace this run", "log execution", "agent observability", "run log", or when fault-localize needs evidence. Default-on during multi-step plans. Traces live at .agent-loom/traces/ — git-ignored by default.
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。
Troubleshoot Coval OpenTelemetry trace ingestion, missing trace UI, sparse traces, bad simulation or conversation correlation, auth/org errors, oversized payloads, duplicate spans, and production debugging with Trace Search.
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。
使用 Arthas 的 watch/trace 获取 EagleEye traceId / 获取请求的 traceId
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。
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.
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 `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.
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。
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.
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 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"
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。
Operate the GRACE 4 CLI for .grace linting, status, module navigation, verification navigation, and file-local semantic markup.
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。
Debug and fix issues in a GRACE 4 project using .grace semantic navigation, assertions, and verification evidence.
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。
Explain GRACE 4 methodology, .grace artifacts, semantic anchors, change lifecycle, verification, and migration boundaries.
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。
Answer questions about a GRACE 4 project by navigating .grace current-state artifacts and file-local semantic markup.
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。
Review GRACE 4 integrity across .grace artifacts, active changes, scopes, assertions, code anchors, and verification evidence.
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。
Design and maintain GRACE 4 verification entries, commands, scenarios, markers, and assertion evidence under .grace/verification.
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。
Detect drift between observed repository state and durable GRACE 4 .grace current state, then help create reconciliation changes.
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。
Execute an approved GRACE 4 GraceChangePlan in sequential or parallel-safe mode with recovery-aware preflight and centralized durable apply.
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。
Refactor GRACE 4 governed code while keeping .grace graph, verification, change scopes, and file-local anchors synchronized.
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。
Show GRACE 4 project health across .grace context, graph, verification, active changes, scopes, and migration boundaries.
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。
Bootstrap a Full GRACE 4 project by creating the canonical .grace context, graph, verification, and changes skeleton.
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。
Agent-applied GRACE 3 to GRACE 4 migration workflow. CLI validates the result but does not convert or delete files.
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 GRACE 4 worker and reviewer subagent presets that understand .grace artifacts, scopes, assertions, and verification evidence.
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。