「traces」の検索結果

Add Temps error tracking to applications using the Sentry-compatible SDK. Temps exposes a Sentry-compatible DSN that works with the official Sentry SDK for each language/framework — no code changes beyond initialization are required. Use when the user wants to: (1) Add error tracking to any app (React, Next.js, Vue, Svelte, Angular, Node.js, Python, Go, Rust, Ruby, Java, PHP, .NET, React Native, Flutter), (2) Wire up uncaught exception and unhandled promise rejection capture, (3) Configure session replay for errors, (4) Upload source maps for readable stack traces, (5) Report releases and environments, (6) Capture custom errors/messages. Triggers: "add error tracking", "add sentry", "track exceptions", "report errors", "temps error tracking", "wire up error monitoring".

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。

Manage capa CLI configuration — `capabilities.yaml` / `capabilities.json`, skills, MCP servers, tools, hooks, sub-agents, rules, plugins, AGENTS.md / CLAUDE.md, security options, tool exposure modes, `capa wrap` shadow workspaces, `--passthrough` native writes, registries (including Claude marketplaces), activity traces, and the local Web UI. Use whenever the user edits the capabilities file, runs any `capa` command (init, install, add, wrap, clean, sh, start/stop/restart/status, auth, upgrade, cache, registry), wires up an MCP server, adds a skill from GitHub / GitLab / a registry / a remote URL / a local path, configures secrets via `${VarName}` placeholders, installs lifecycle hooks, defines sub-agents, or troubleshoots a failed install. Use even when the user only names a fragment ("add a skill", "wire up brave search", "wrap cursor", "block this phrase in skills") without saying "capa" — if a `capabilities.yaml` or `capabilities.json` lives at the project root, this is almost certainly the right skill. If the project does NOT yet have a capabilities file, point at the `bootstrap` skill instead.

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 the official Sentry CLI to investigate issues, events, traces, and logs for Vibe Remote without committing Sentry credentials into the repository.

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。

Implements structured diagnostics, correlation and justified metrics or traces; not incident investigation or a mandatory telemetry platform.

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。

Find the root cause of Elixir/Phoenix bugs — crashes, exceptions, stack traces, compile errors, LiveView that won't update, silent failures. Use when something is broken or misbehaves. --parallel for 4 tracks.

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。

Monitoring, logging, and tracing implementation using OpenTelemetry as the unified standard. Use when building production systems requiring visibility into performance, errors, and behavior. Covers OpenTelemetry (metrics, logs, traces), Prometheus, Grafana, Loki, Jaeger, Tempo, structured logging (structlog, tracing, slog, pino), and alerting.

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。

Builds an eval plan for an AI feature - error analysis on real traces, failure-mode ranking, code checks and binary LLM judges validated against human labels, CI tests, production monitoring, and a launch bar. Use when you ask how do we eval this, how do I test an AI feature, our LLM outputs are inconsistent, are the evals good enough, LLM-as-judge, hallucinations, or what accuracy is good enough to launch. Draws on 41 Lenny's Podcast guests incl. Hamel Husain, Shreya Shankar and Sander Schulhoff.

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。

Research: structured investigation, fact-checking, explanation traces.

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 debugging specialist for errors, test failures, log analysis, and system problems. Use when encountering issues, analyzing error logs, investigating system anomalies, debugging production issues, analyzing stack traces, or identifying root causes. Combines general debugging workflows with error pattern detection and log 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。

Your CFO. Builds the unit economics of a business before money is spent: what one unit earns after its direct costs, the real profit margin at the planned volume, the break-even point in units a day, year 1 month by month, the cash needed before it pays for itself, how long the startup spend takes to earn back, and what happens if the price drops, sales come in low or costs rise. Every number traces to an input the user can check. Use when the user says "find the profit margins", "do the numbers", "is this profitable", "break-even", "unit economics", "how much do I need to start", or "run the CFO".

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 operating Inngest API resources from the terminal with `npx inngest-cli@latest api`: Cloud/local run debugging, event-run lookup, function traces, function invocation, app syncs, webhooks, environments, keys, account checks, and Insights queries. Provides prescriptive command routing for agents: which CLI command to run for a run ID, event ID, app ID, function ID, Cloud environment, API key, missing ID, or potentially mutating operation. Use `inngest-cli` for dev server setup/general CLI workflows and `inngest-api` only when raw REST API v2 docs or OpenAPI fallback are needed.

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。

Analyze C++ build performance using vcperf.exe and ETL traces. Use this skill when the user wants to profile a build, understand compile bottlenecks, identify expensive headers or templates, generate JSON analysis reports, or interpret vcperf output. Covers tracing workflows, the /jsonAnalysis flag, reading the resulting JSON schema, and actionable remediation strategies for each bottleneck category.

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。

MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.

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。

Traces execution paths through the code graph with criticality scoring and Mermaid charts. Use when understanding how a function propagates through the system.

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。

Native macOS/iOS app performance profiling via xctrace/Time Profiler and CLI-only analysis of Instruments traces. Use when asked to profile, attach, record, or analyze Instruments .trace files, find hotspots, or optimize native app performance without opening Instruments UI.

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 hand-made first-50 traction into self-reinforcing acquisition loops, so the next 5,000 users come from usage, not founder hours. Use when growth stalls the moment the user stops pushing, every signup traces back to a DM or one launch spike, or they're reaching for "more channels" when the real gap is that using the product creates no new users.

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。

Reconstruct workflow behind a past coding-agent artifact using local ax sessions/commits/skills/tool traces. Use when asked how X was built.

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 an agent, harness, gateway, MCP workflow, or multi-step automation claims completion and the available traces, checkpoints, approvals, tool calls, or deployment records must be judged without trusting self-reported success.

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。

Audit and safely repair cross-runtime Skill governance: canonical-source ownership, divergent or duplicate projections, exposure scopes and budgets, lifecycle drift, quarantine, and retirement. Use when the user explicitly requests cross-runtime or cross-scope Skill governance; ignore project-local Skill inventory, product/runtime loading or enablement checks, usage statistics, and mentions/traces.

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 distinctive, production-grade web frontend interfaces with high design quality. Use when the user explicitly asks to build or substantially redesign a web component, page, or application. Only applies when repository inspection confirms that the visual surface is a browser-rendered web UI. Do not use for native or GPU-rendered graphics, shaders, games, terminal UIs, or other non-web visual systems. Ignore incidental mentions and traces.

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。

Analyze failing tests to detect functional bugs in code. Takes repository and failing test output as input, analyzes execution behavior, assertions, and stack traces to identify suspicious code regions and root causes. Use when debugging test failures, investigating regression bugs, or understanding why tests fail. Explains the bug mechanism, identifies affected code, and suggests fixes based on test expectations vs actual 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。

Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks. Use when you need to debug complex issues, reproduce test cases, prepare traces for formal verification, or analyze program execution. Supports manual instrumentation points, automatic function/method instrumentation, and conditional triggers. Outputs structured JSON snapshots for debugging, replay, and verification workflows.

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。

Locate root causes of failing regression tests by analyzing code changes, error messages, and test dependencies. Use when regression tests start failing after code changes, investigating test failures in CI/CD, debugging flaky tests, or understanding why previously passing tests now fail. Analyzes git diffs, stack traces, test output, and dependency changes to produce structured markdown reports ranking likely causes. Triggers when users ask to find why tests are failing, debug regression failures, investigate test breakage, or analyze failing test suites.

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。

Explains runtime errors and compilation failures with actionable debugging guidance. Use when Python or Java code throws runtime exceptions (NullPointerException, TypeError, AttributeError, etc.), compilation errors (syntax errors, type mismatches, import failures), or dependency issues. Analyzes error messages, stack traces, and code context to identify root causes and provide concrete fixes with examples. Distinct from test-related errors - focuses on errors during normal code execution and build processes.

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。