5,983 件 ・ 関連度順
概要と使いどころ
Reviews Web Audio API JavaScript/TypeScript code for correctness, thread safety, and deprecated patterns. Use when the user asks to review AudioWorklet code, check an AudioWorkletProcessor, audit Web Audio graph construction, or diagnose crackling/dropouts in a web audio app. Trigger on phrases like "review my Web Audio code", "check my AudioWorklet", "is my AudioWorkletProcessor safe", or "why does my web audio crackle".
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。
Profiling strategy for audio CPU issues — xruns, spikes, and buffer underruns. Use when the user reports CPU overload, audio glitches under load, or performance regressions. Trigger on phrases like "I'm getting xruns", "my plugin causes CPU spikes", "dropouts under high voice count", "audio thread is too slow", "how do I profile my DSP". References audio-numerics-review for denormal slowdown and audio-dsp-review for lock contention.
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Diagnose whether an LLM classifier's validation-gate failure is GOLD-BOUND before spending on prompt revision or model changes. Use when: (1) a scoring pipeline over-predicts a label (precision low, recall high) and a prompt clarification is proposed to tighten it, (2) a pilot/validation gate fails and the fix candidates are prompt edits, (3) inter-rater agreement on the weak label was already low (κ < ~0.6). Core check: if gold POSITIVES share the exact feature the revision would exclude, no prompt can pass a gold-scored gate — recall craters while precision barely moves. Also documents the verified surgical-pilot design (single-section diff, tune/holdout split, pre-registered gate, perturbation check on untouched sections).
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N-round adversarial review pipeline for empirical research output — the chain from data to LaTeX tables to a manuscript that cites them. A Claude drafter proposes minimal diffs, a deterministic mechanical battery gates every diff from a clean state with a regression gate, a Codex reviewer files check-backed critiques, and a blind judge panel decides residual disputes. Manual-invoke ONLY: trigger when the user explicitly runs /adversarial-empirical-review or names 'adversarial-empirical-review' / 'adversarial empirical review'. Do NOT auto-trigger on generic 'review my results', 'check my tables', or manuscript-editing requests. For prose-style refinement use style-emulation instead; this skill AUDITS WHETHER THE TABLES ARE CORRECT — that each number in the tables is what the analysis code computes, reproduces from the data, and is internally consistent. It is an empirical + code review: the manuscript is read only to resolve table numbering, and prose is not examined.
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。
pyfixest demeaner_backend="cupy64" (including its CPU fallback when cupy is absent) is NOT numerically identical to the default numba backend and does NOT drop fully-absorbed/collinear regressors the same way. Use when: (1) adding demeaner_backend="cupy64" to existing pf.feols/fepois calls changes the printed coefficient table, (2) a regression report suddenly gains rows with absurd estimates (e.g. coef 435.8, SE 7106) for controls absorbed by the fixed effects, (3) diffing outputs before/after a backend change, or (4) anything parses a pyfixest text report by line position.
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Complete methodology for computing publication-quality cumulative abnormal returns with proper event-study test statistics, matching the robustness of Kaspereit's eventstudy2 for Stata. Covers dateline construction, event-date mapping, estimation and event windows, thin-trading adjustment, OLS with Theil prediction error correction, abnormal return computation, CAR/CAAR/AAR accumulation, boundary contamination guards, and common tests such as Patell, BMP, Kolari-Pynnonen, generalized sign, Wilcoxon, and GRANK-T. Use when the user mentions abnormal returns, event windows, market-model regressions, CARs, CAAR, AAR, eventstudy2, thin trading, trade-to-trade returns, or event-study test statistics.
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。
Build human adjudication / hand-labeling sheets from LLM-pipeline data without evidence truncation. Use when: (1) preparing a CSV/Excel sheet for a human to rule on cases an LLM classifier or rater panel judged, (2) a labeler reports "there is no information to label from" or cells look empty in Excel, (3) excerpt columns cluster at one exact length (e.g. all 1,500 chars — a hard truncation cap). Covers: full rating-basis recovery, Excel 32,767-char cell cap, multi-line CSV mangling, ruling dropdowns, companion text files.
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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.
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。
Three.js materials - PBR, basic, phong, shader materials, material properties. Use when styling meshes, working with textures, creating custom shaders, or optimizing material performance.
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Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.
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Get detailed data about a shader asset in the Unity project. Returns shader properties, subshaders, passes, compilation errors, and supported status. Use 'assets-find' tool with filter 't:Shader' to find shaders, or 'assets-shader-list-all' tool to list all shader names.
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Get asset data from the asset file in the Unity project. It includes all serializable fields and properties of the asset. Use 'assets-find' tool to find asset before using this tool.
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Systematic 4-phase debugging framework for Unity projects with GameDebug wrapper generation. Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes. Triggers on requests like "this doesn't work", "there's a bug", "it crashes when", "fix this error", "why is this happening", "null reference exception", "test is failing", "unexpected behavior", "something broke", or any debugging task. Four-phase framework (gather evidence, isolate, hypothesize, fix) that ensures understanding before attempting solutions.
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Create new material asset with default parameters. Creates folders recursively if they do not exist. Provide proper 'shaderName' - use 'assets-shader-list-all' tool to find available shaders.
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。
Improve a skill using a test-fix-retest loop. Runs static checks, proposes targeted fixes, rewrites the skill, re-tests, and keeps or reverts based on score change.
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When a GDD is revised, scans all ADRs and the traceability index to identify which architectural decisions are now potentially stale. Produces a change impact report and guides the user through resolution.
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。
UniTask async/await patterns for Unity including cancellation, lifecycle binding, and coroutine interop.
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用于编写或审查靠近 UI 代码的前端单元、组件或轻量集成测试，包括 React Testing Library、Vue Test Utils、hooks/composables、props/emits、回调、可访问查询、user-event 交互、mock、loading/error/empty 状态和回归覆盖。若是测试分层规划、真实浏览器旅程或现有验证失败，优先选择对应测试或验证工作流；中文触发词包括 组件测试、组件单测、单元测试、轻量集成测试。
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 authoring or reviewing frontend unit, component, or light integration tests close to UI code, including React Testing Library, Vue Test Utils, hooks/composables, props/emits, callbacks, accessible queries, user-event interactions, mocks, loading/error/empty states, and regression coverage. For layer planning, real-browser journeys, or existing validation failures, choose the matching testing or validation workflow first; Chinese triggers include component testing, Component unit testing, unit testing, lightweight integration 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 when proving that a frontend refactoring preserved existing behavior, selecting an appropriate safety net, validating each structural step, or assigning a PASS, PARTIAL, or NOT PROVEN preservation verdict.
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。
OpenUI generative UI with LangChain/LangGraph backend. Supports ChatOpenAI and ChatAnthropic.
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。
OpenUI generative UI with Python FastAPI backend. OpenAI and Anthropic SDK variants.
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 a well-structured GitHub PR with proper branching, testing, formatting, and documentation. Use when the user says "create a PR", "make a PR", "open a pull request", or wants to submit changes for review. Handles the full workflow: branch creation, implementation, testing, formatting, committing, and PR creation with comprehensive descriptions.
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。
Provide constructive, evidence-based research mentoring feedback on student manuscripts, proposals, analyses, code, figures, and presentations. Use for advisor-style review that teaches the next improvement without impersonating an advisor or making unsupported scientific judgments.
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。