6,370 件 ・ 関連度順
概要と使いどころ
Analyzes malicious PDF documents: parsing the object structure for JavaScript, OpenAction/launch triggers, embedded files, and URIs, then extracting and decoding suspicious streams without opening the document in a reader. Activates for requests to analyze a malicious PDF, inspect PDF JavaScript, or extract embedded payloads from a PDF.
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Analyzes Python-based malware packaged as PyInstaller/py2exe executables by detecting the packer, locating the embedded archive, and identifying bundled .pyc modules for extraction and decompilation. Activates for requests to analyze compiled Python malware, unpack a PyInstaller binary, or extract pyc modules from a frozen Python executable.
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。
Automates radare2/rizin analysis through r2pipe to script function enumeration, string and import extraction, and cross-reference queries across samples programmatically. Activates for requests to automate radare2 with r2pipe, script rizin analysis, or batch-extract functions and xrefs from binaries.
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。
Decrypts statically embedded malware configuration blobs by trying common schemes (single-byte and multi-byte XOR, RC4, base64 layers) and scoring decoded output for config-like content. Activates for requests to decrypt an embedded config, recover hardcoded settings from a blob, or brute-force a configuration encryption scheme.
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。
Process meeting notes into structured notes + action items
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。
Record, compose, publish, serve, and review short desktop validation video proofs for Pulp UX/test-harness work. Use when the user asks to "show me it working", record a validation video, prove a click/interaction, share a local proof link, attach a video to a review issue, or compare an implemented UI against source material. Covers macOS desktop capture, Remotion proof composition, attachment budgets, local/Tailscale serving, and review closure.
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。
Stand up a fast, cached, isolated, disposable macOS CI lane on Tart — layered golden VM images, ephemeral per-job GitHub Actions runners, host-mounted caches, and a reusable per-repo vm-image manifest. Use when setting up VM-based CI for Pulp or generalizing it to another repo, building/refreshing golden images, wiring ephemeral runners, or debugging the VM lane.
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 a moment of friction — a conflicting PR, a wedged runner, a mysterious red check, a repeated manual chore — into a durable, actionable report. Sizes up the scenario, captures the evidence, separates one-off from SYSTEMIC, proposes a fix, and routes it (fix now, or hand off). Reports are LIVING documents that the fixing agent updates with the outcome. Invoke whenever something feels like it shouldn't have been this hard, or when the same annoyance shows up twice.
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。
Android platform development for Pulp — NDK cross-compilation, Oboe audio, Dawn/Skia GPU rendering, JNI bridge, touch interaction, emulator workflows, and end-to-end smoke validation. Covers build, deploy, debug, and the gotchas discovered during bringup.
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。
Reproduce and debug "only happens in a DAW" audio plugin bugs (cutouts, glitches, parameter-change failures) entirely offline — headless Processor scenes for DSP bugs and a standalone AudioUnit host probe for adapter/host-interaction bugs. Use when a plugin misbehaves in Logic/Live/etc. but unit tests are green.
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。
DevOps/运维角色。关键词：部署/上线/CI-CD/服务器/Docker/nginx/SSL/域名/端口/监控/GitHub Actions。接收测试通过报告后执行部署，维护生产环境，监控AI调用成本。
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Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.
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。
Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that same input. Use when: (1) designing a classifier/LLM extractor whose target is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. Symptom of the underlying failure: elaborate theory accumulates to explain why gold is "partially unpredictable" when the model was simply never shown the document the annotators read.
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One-shot module map — go up a layer of abstraction and get a domain-vocabulary module map of the codebase. Use when lost in unfamiliar code, after a long deep-dive session, or when you need to re-orient before planning.
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Inject juice and game feel into gameplay code using the project's chosen middleware. Use when adding screen shake, tweens, particles, audio feedback, haptics, hitstop, or any form of "juice" to gameplay events. Triggers on requests like "add juice", "make this feel better", "add screen shake", "polish this feature", "add feedback to this action", "make the hit feel impactful", "add particles when", "screen shake on damage", "tween this", "add camera shake", "make this snappy", or any game feel, polish, or feedback work. Always reads ProjectConfig.yaml -> feel_tools and docs/GFD.md Feedback Matrix before generating code.
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Validates a UX spec, HUD design, or interaction pattern library for completeness, accessibility compliance, GDD alignment, and implementation readiness. Produces APPROVED / NEEDS REVISION / MAJOR REVISION NEEDED verdict with specific gaps.
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Scaffold the test framework and CI/CD pipeline for the project's engine. Creates the tests/ directory structure, engine-specific test runner configuration, and GitHub Actions workflow. Run once during Technical Setup phase before the first sprint begins.
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。
Generates a sprint or milestone retrospective by analyzing completed work, velocity, blockers, and patterns. Produces actionable insights for the next iteration.
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。
Unity physics query correctness patterns. Catches common mistakes with Raycast, SphereCast, OverlapSphere, NonAlloc allocation, LayerMask construction, trigger interaction, hit ordering, and query type selection. PATTERN format: WHEN/WRONG/RIGHT/GOTCHA. Based on Unity 6.3 LTS.
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。
Sentry Unity SDK integration patterns for error tracking, performance monitoring, transactions, spans, and custom instrumentation.
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。
Unity 6 XR development guide. Use when building VR, AR, or MR experiences. Covers XR Interaction Toolkit, XR Plug-in Management, OpenXR, hand tracking, controllers, haptics, AR Foundation (plane detection, anchors, image tracking), and platform-specific setup (Meta Quest, Apple Vision Pro). Based on Unity 6.3 LTS documentation.
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。
用于设计、实现或审查前端交互动效、页面转场、滚动动画、Framer Motion、GSAP、Lottie、CSS 动画、动效强度、动画性能、减少动效行为或兼具表现力与可访问性的 UI 动效。不要用于仅 SVG 路径绘制、Canvas/WebGL 渲染或普通 hover 状态；中文触发词包括 动效设计、交互动效、页面转场、滚动动画、Framer Motion、GSAP。
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。
用于创建或审查 Next.js 14+ App Router 项目、文件路由、layout、服务端/客户端组件边界、SSR/SSG/ISR、streaming、metadata、middleware、server actions 或 Next 特定数据获取。通用客户端 React 组件架构另按项目 React 约定处理；中文触发词包括 Next.js、App Router。
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 building, reviewing, or improving frontend UI that needs product-specific design direction, design-system generation, Master/Page overrides, distinctive visual identity, anti-generic interface choices, first-screen hierarchy, UI polish, chart UX, interaction states, responsive behavior, or visual QA. Interface polishing, interactive state.
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。