「race」の検索結果

Diagnose data races, convert callback-based code to async/await, implement actor isolation patterns, resolve Sendable conformance issues, and guide Swift 6 migration. Use when developers mention: (1) Swift Concurrency, async/await, actors, or tasks, (2) "use Swift Concurrency" or "modern concurrency patterns", (3) migrating to Swift 6, (4) data races or thread safety issues, (5) refactoring closures to async/await, (6) @MainActor, Sendable, or actor isolation, (7) concurrent code architecture or performance optimization, (8) concurrency-related linter warnings (SwiftLint or similar; e.g. async_without_await, Sendable/actor isolation/MainActor lint).

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Anti-debug bypass ve obfuscation çözme — ptrace, UPX, PRNG, packer tespiti

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Help users debug PostHog Error Tracking stack-trace symbolication for any supported platform — JavaScript/TypeScript web, React Native (Hermes), Android (Proguard / R8), or iOS / macOS (dSYM). The PostHog symbol-set lookup flow is universal across platforms; build-tool and artifact details live in per-platform references (JavaScript is fleshed out, others come as we encounter them). Use when stack frames stay minified or obfuscated after symbols are uploaded, PostHog symbol sets show last_used but frames are not readable, chunk IDs or dSYM UUIDs do not match, "Token not found" appears, uploaded source maps / dSYMs / Proguard mappings look empty, or bundler / symbol-upload configuration needs troubleshooting.

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Investigate LLM spend in PostHog — total cost over time, cost by model, provider, user, trace, or custom dimension, token and cache-hit economics, and cost regressions. Use when the user asks "how much are we spending on LLMs?", "which model / user / feature is most expensive?", "why did cost spike?", wants to build a cost dashboard or alert, or pastes a trace URL and asks about its cost.

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。

Simulate Joint Commission tracer methodology to assess compliance with accreditation standards across patient care processes and support systems

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。

Pino (高速 JSON 構造化ロガー for Node.js、Fastify 標準ロガー) リファレンス。 logger, child logger, log levels (info / warn / error / debug / trace)、transport、 pretty print (pino-pretty)、redaction (機密情報マスキング)、serializers、 async / sync logging、ファイル出力、structured logging。

Playwright (E2E / コンポーネントテストフレームワーク) リファレンス。 test, expect, page, locator, getByRole / getByText / getByLabel / getByTestId、 toBeVisible, click, fill, route, mock, fixtures、 playwright.config.ts、auth 永続化、trace viewer、codegen、UI モード。

Ergogen (エルゴノミック自作キーボード設計ツール) リファレンス。 YAML / JSON 設定で points, outlines, cases, pcbs を宣言し SVG / DXF / JSCAD / KiCad を出力。 Anchors, Zones, Adjustments, Preprocessing, Footprints, Traces。 キーレイアウト、ケース設計、PCB 生成。

Rust 製バックエンド HTTP サーバーフレームワーク fandhe-backend のリファレンス。 fandhe-backend-core / -http / -routes。Server, BoundServer, Router（merge）, Handler。 4 拡張点 Middleware（on_response_with_status）/ UpgradeHandler / RequestGate / Interceptor。 Diagnostics, StreamingResponse, sans-IO HTTP/1.1, graceful shutdown。 プラグイン cors, websocket（WsSender）, graphql, openapi, webrtc 等。

OpenAI API (developers.openai.com) の Evals / Fine-tuning リファレンス。 Evals API, Datasets, graders（string check, text similarity, model, python, multi）, agent evals, trace grading, external models、 SFT, DPO, RFT（reinforcement fine-tuning）, vision fine-tuning, distillation, prompt optimizer, fine-tuning best practices。 セルフサービス fine-tuning は段階的廃止進行中。

NVIDIA DGX Spark User Guide リファレンス。GB10 Grace Blackwell Superchip ハードウェア仕様、first-boot / UEFI 設定、Spark Stacking クラスタ、 DGX OS / NGC / container runtime ソフトウェアスタック、 PXE / cloud-init プロビジョニング、fleet lifecycle 運用、 apt / fwupdmgr 更新、system recovery、PSIRT サポート、 vLLM / TensorRT-LLM / Ollama / ComfyUI / NIM / NeMo fine-tune / NCCL 等の build.nvidia.com/spark playbook、複数 Spark 接続・Tailscale・VS Code、 NVIDIA Developer Forums 由来の known issues / troubleshooting。

Analyzes base-editing screens for variant function. Covers library design (Sanson 2020 GRACE, Hanna 2021 BRCA1/2 SNV scanning, Cuella-Martin 2021), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end, wider for ABE8e), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.

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Reads, validates, and prepares CDISC SDTM and ADaM clinical trial data for analysis. Covers SDTM domain joins (DM, AE, EX, VS, LB, DS), ADaM architecture (ADSL, BDS, OCCDS, ADTTE) with traceability, treatment-emergent AE conventions, baseline derivation, SUPPQUAL/NSV handling, Define-XML 2.1, and Pinnacle 21 / CORE validation. Use when working with clinical trial datasets in CDISC SDTM/ADaM format, preparing analysis-ready data, or validating for regulatory submission.

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 local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

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Discover, build, verify, and optionally export (HTML, optional PDF) the evidence manifest of one SDD Composy task in evidence_required, routing it toward review_required. Use when acceptance evidence must be assembled or checked — 'juntar as evidências da task', 'gerar o relatório de evidências', 'verify the evidence manifest'. Do NOT use to run tests (sdd-qa), review code (sdd-review), or inspect the trace (sdd-trace).

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 systematic root cause analysis and debugging methodologies to diagnose and fix bugs, test failures, and unexpected behavior. Use when encountering production issues, investigating test failures, diagnosing performance problems, tracing error sources through call stacks, analyzing logs and stack traces, reproducing inconsistent bugs, debugging race conditions, investigating memory leaks, or applying scientific method to problem-solving before proposing fixes.

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 robust error handling strategies including try-catch blocks, custom error classes, error boundaries, graceful degradation, and comprehensive logging. Use when implementing exception handling, creating custom error types, setting up error boundaries in React, designing error response formats for APIs, implementing retry logic, handling async errors and promise rejections, logging errors for monitoring, creating user-friendly error messages, or building fault-tolerant systems that fail gracefully.

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Design a self-improvement loop for a shipped product's AI agents — production traces feed evals, evals feed improvement proposals (prompts, playbooks, retrieval configs), and a human approval gate promotes changes with rollback. Technique-agnostic: chooses per project between ACE-style evolving playbooks, GEPA/MIPROv2 offline optimization, or simple eval-driven iteration via references/techniques.md. Load when the user asks to make my product's agents self-improving, learn from production traces, add a learning loop, evolve prompts or playbooks safely, promote agent improvements, or GEPA-style optimization. NOT harness-evolution (that improves the coding agent), NOT experimentation (product A/B tests), NOT agent-run-retro (dev-phase manual retros — this skill is the production-scale continuation).

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 LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

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Infers person-to-person transmission from pathogen genomes using outbreaker2 (Campbell 2018), TransPhylo (Didelot 2017), phybreak (Klinkenberg 2017), BadTrIP (De Maio 2018), SCOTTI (De Maio 2016), BEASTLIER (Hall 2015), and SNP-distance / cluster-picker approaches (HIV-TRACE for HIV; transcluster). Defines outbreak clusters using pathogen-specific SNP thresholds (NOT a universal cutoff -- TB <=12 SNPs / Walker 2013; MRSA <=15 / Coll 2017; C. difficile <=2 / Eyre 2013; Klebsiella <=21 / Snitkin 2012), models within-host diversity and transmission bottlenecks (Worby-Lipsitch-Hanage 2014; McCrone 2018; Sobel Leonard 2017; Lythgoe 2021 SARS-CoV-2), integrates contact-tracing data, distinguishes generation interval from serial interval (Britton & Scalia Tomba 2019; Ali 2020), attributes source via Bayesian source attribution (Mather 2013 DT104; islandR), and reconciles transmission-network reconstruction with epi metadata. Use when investigating outbreaks for who-infected-whom, defining SNP-cluster outbreak definitions, accounting for unsampled intermediates, choosing between outbreaker2 (rich epi data) and TransPhylo (genomic-only after dated phylogeny), running source attribution between host populations, calling HIV-TRACE thresholds appropriate to the local subtype, or distinguishing recent transmission from reactivation in TB / chronic HIV.

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This skill should be used when the user asks to "このファイルを変えたら何が壊れる？", "what depends on this function", "影響範囲を調べて", "show me what imports this module", or wants to understand the blast radius before modifying a shared utility, exported type, or public API. Traces import/require references up to two levels deep and lists affected test files.

Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

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。

Systematic debugging methodology covering root cause analysis, log-based debugging, production debugging, memory leak diagnosis, deadlock detection, performance anomaly investigation, and reproducible bug reproduction. Distilled from real experience at Google, DeepMind, ByteDance, and Huawei. USE WHEN: investigating production incidents, debugging hard-to-reproduce bugs, analyzing crash dumps, finding race conditions, diagnosing memory leaks, debugging performance regressions, or any situation requiring systematic root cause analysis. Triggers on "debugging", "root cause", "bug", "crash", "segfault", "deadlock", "race condition", "memory leak".

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 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.

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。