「leak」の検索結果

Foundations of digital signal processing — sampling, aliasing, the Nyquist criterion, quantization, convolution, the discrete Fourier transform, FFT, FIR and IIR filter design, windowing, spectral leakage, and practical issues in fixed-point versus floating-point implementation. Use when designing filters, analyzing spectra, choosing sample rates, reasoning about aliasing, or implementing DSP algorithms on a microcontroller, DSP chip, or host CPU.

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。

Vehicle/automotive image editing for cars, trucks, SUVs, motorcycles — car scenes, reflections, tires refinement with snow/mud/grass, segment windshield/wheels/body/windows/hubcaps, atmospheric effects (dust, fog, snow, light leaks, lens flare), and lighting harmonization (hot-day, cold-day, hot-night, cold-night presets). Powered by Bria.ai's dedicated automotive pipeline. Use this skill whenever the user mentions a car, vehicle, truck, SUV, motorcycle, auto photo, car reflections, tire enhancement, muddy tires, vehicle background, automotive marketing, car dealership visual, vehicle configurator, or car scene generation. ALWAYS prefer this skill over general image tools when the subject is a vehicle — faster and more accurate for automotive 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。

Use AddressSanitizer to detect memory safety bugs in C/C++ programs. Identifies use-after-free, buffer overflow, memory leaks, and other memory 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。

Reviews Node.js runtime behavior in JavaScript or TypeScript, including server entrypoints, route handlers, middleware, streams, processes, dependencies, and environment configuration. Loads alongside language and CLI checks when applicable; investigates blocking I/O, backpressure, injection, lifecycle leaks, and misleading failure signals.

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 discovering niche signals, auditing ICP or won/lost evidence, rescoring accounts, or building account and lead scoring Plays. Triggers on fit scoring, engagement scoring, external proxies, and scoring leakage. Skip pure outreach copy or contributor skill installation tasks.

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。

Reviews a whole deliverable (talk, course module, paper, chapter, or referee report) against its manifest without rewriting it. Runs a deterministic gate for build, citations, numbers, facts, and leaks and stops on failure, then runs a finder over each section and a strong-model pass over the whole work, and adjudicates into checks/<date>/findings.json and report.md with anchored findings, severities, and honest NOT CHECKED coverage within a dollar budget. Use when asked to lint or review the whole talk, module, or paper, for a pre-ship review, or for the night shift. Revision stays serial and author-led.

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。

Stop AI agents from secretly bypassing your rules. Mechanical enforcement with git hooks, secret detection, deployment verification, and import registries. Born from real production incidents: server crashes, token leaks, code rewrites. Works with Claude Code, Clawdbot, Cursor. Install once, enforce forever.

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 user-facing docs, CLI reference text, examples, and scaffolds for truthfulness, prose quality, and RFC leakage. Use when a broad review needs a dedicated docs/claims pass or when the user asks for docs-truth review specifically.

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 reviewing, writing, or debugging Go code under exercises/part2/ (and matching embedded code in docs/part2/**/*.mdx). Thinks hard about correctness, idiomatic Go, error handling, resource cleanup, concurrency, and — crucially — what the tests would catch: edge cases, races, leaks, timeouts, and port conflicts. Knows the repo's module-mode quirk (GO111MODULE=off) and which dirs carry go.mod.

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。

Verify and lock project identity before ANY git push, Cloudflare deploy, or Supabase operation. Essential when working with multiple GitHub accounts (personal + work), multiple Cloudflare accounts, or multiple Supabase/Neon projects. Prevents wrong-account deploys, cross-project secret leaks, and git history contamination.

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。

Scan staged changes, commits, or repositories for secrets before they leave the workstation or CI job, instead of relying on a later platform-side catch.

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 the user says 'audit my conversion rate', 'cro check', 'find conversion leaks', or asks what is hurting conversion before a paid campaign or after a redesign. Scores six friction dimensions: above-the-fold clarity, CTA hierarchy, form length, social proof, speed, trust signals.

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 writing or modifying code for s&box, sbox, the Facepunch sandbox engine, or any Source 2 game project in C#. Triggers on mentions of s&box, sbox, sandbox game, Facepunch engine, Source 2 game, `.sbproj`, `.razor` with `PanelComponent`, `@inherits PanelComponent`, `Sandbox.Component`, `GameObject` + `Components.Get<T>`, `Scene.Trace`, `CharacterController` + `.Move()`, `SkinnedModelRenderer`, `NavMeshAgent`, `[Sync]`, `[Rpc.Broadcast]`, `[Property]`, `INetworkListener`, `ISceneEvent`, `PlayerController` in C#. Also triggers on any file with `using Sandbox;` or `using Sandbox.UI;` that isn't Unity/Godot/Unreal. Writes idiomatic s&box C# components, Razor UI, and networking code; prevents Unity-pattern leakage.

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。

Evaluates alternative trading systems with fill rate analysis, information leakage assessment, and venue toxicity measurement. Use when analyzing dark pools, evaluating ATS venues, or assessing execution venue quality.

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。

Hunt GraphQL-specific vulns — introspection abuse, field-level authorization (IDOR), batched queries for brute force, alias-based rate limit bypass, deep query DoS, mutation injection, schema disclosure, suggestion leakage, CSRF via GET, and CSRF via POST without preflight. Use when a GraphQL endpoint is present.

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。

Redact rendered visual artifacts — Mermaid diagrams, SVGs, and HTML dashboards — before public disclosure, using an ordered longest-first mapping table (specific label, then namespace label, then catch-all placeholder) that swaps sensitive identifiers for descriptive stand-ins while preserving structure, node ids, and teaching value. Covers whole-word identifier replacement, node-label-vs-node-id discrimination, re-rendering the derived image from the redacted source, and verifying the result through the redaction gate. Use when a diagram or dashboard built from internal findings must ship to a public mirror without leaking the names behind it.

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。

Redact a network/MITM capture directory — JSONL, JSON, logs, HAR-style dumps — in place before it can be referenced publicly, scrubbing credential tokens, API keys, service ids, UUIDs, emails, home paths, usernames, and device or session hashes while preserving token-class prefixes for analysis and leaving public identifiers untouched. Covers class-preserving substitution, an idempotent re-runnable pass, distinguishing a secret from a public id, and verifying the scrubbed capture through the redaction gate. Use when a wire capture from a sanctioned probe must enter notes, a guide, or a public repo without leaking the session that produced it.

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。

Redact reverse-engineering findings for public disclosure while preserving methodology, generalizable patterns, and teaching value. Covers the private-vs-public repo split, deny-list pattern maintenance, orphan-commit publish pattern that prevents `git log` leaks, category-based redaction calibration (methodology/pattern/version-finding/internal), and the `check-redaction.sh`-style CI gate that blocks merges when a deny-listed pattern appears. Use when publishing findings about a CLI harness you don't own, when preparing upstream proposals to an unrelated project, or when archiving a private research repo for public reference.

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。

State management for Angular 18-21: signals (signal/computed/effect), services-as-state, NgRx Store + Effects + Selectors, NgRx Component Store, NgRx Signals (newer signal-based store). RxJS essentials — operators, async pipe, takeUntilDestroyed, signal/observable interop. Use this skill to: - Pick the right state tool (signals / services / NgRx variant / vue-query equivalent). - Use signals correctly (signal/computed/effect — when each). - Build a Pinia-style service-as-state singleton. - Set up NgRx Store + Effects + Selectors. - Use RxJS without leaking subscriptions (async pipe, takeUntilDestroyed, Subject patterns). - Bridge signals ↔ observables via toSignal / toObservable. Do NOT use this skill for: - General Angular conventions (see angular-conventions). - Routing state (see angular-routing). - Form state (see angular-forms). - Testing state (see angular-testing).

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 Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits (scaffold, cold-start, temporal, combination), evaluator metrics, benchmark groups, and bounded molecular-oracle workflows. Use this skill to find which TDC datasets exist for a therapeutic task, load them with a split that does not leak, score predictions with the task's own official metric rather than a generic one, and run benchmark groups reproducibly. Also trigger on PyTDC, Therapeutics Data Commons, tdc.single_pred, tdc.multi_pred, ADMET Benchmark Group, scaffold split, get_split, or molecular oracles such as GSK3B, JNK3 and DRD2.

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 tracking heap allocations on Linux with heaptrack: allocation hotspots, peak heap, temporary allocations, leaks, or a diff of two runs via heaptrack_print. Not for heap errors: use valgrind.

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 tuning or profiling native code on Apple M-series Macs: unified memory, 16 KiB pages, Accelerate and Metal for matrix work, xctrace and leaks, Rosetta 2, or sysctl hardware queries.

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。

分析 PyTorch memory snapshot pickle 文件（_dump_snapshot 导出的内存快照），提供内存峰值、碎片、泄漏、OOM 检测与交互式 HTML 报告。仅当用户明确提及 memory snapshot、内存快照、_dump_snapshot、pickle 内存文件 或需要对 snapshot pickle 做内存分析时才触发，不处理一般的 NPU 性能分析、SQLite 查询或通用内存问题。

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。

对 msMemScope 显存数据做全链路处理：根据用户的显存问题诉求（OOM、显存泄漏、显存占比拆解、Step 间对比等），指导并帮助用户以最合适的 msMemScope 采集配置完成显存数据采集（支持命令行、Python 接口、mstx 打点三种采集方式，覆盖训练、推理 vLLM-Ascend、FSDP、强化学习 verl 等场景）；对已采集的 memscope_dump_*.csv/db 数据做系统性解读与问题诊断（整体显存曲线趋势分析、显存使用大头按模块/组件拆解分析、显存峰值点各模块与流程占比分析，以及对显存泄漏、OOM 等典型问题的根因定位与原因分析）。

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。