672 件 ・ 関連度順
概要と使いどころ
Use when ingesting text samples to extract quantitative writing style metrics. Produces a structured JSON style profile with vocabulary patterns, sentence structure, tone markers, and formatting preferences. Invoke before voice-clone-generator to establish style constraints.
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。
Evaluate an interface from a UX perspective, assessing hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring and actionable feedback. Use when the user wants an overall design or UX review—not when the main need is measurable accessibility/performance diagnosis, or final micro-detail polish.
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 business logic flaws — race conditions, workflow state bypass, parameter pollution affecting logic, coupon/discount abuse, refund logic, multi-step process bypass, integer overflow, negative quantities, double-spend. Use when looking for non-CVE bugs that require understanding the app's intended workflow.
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。
Specialized research and analysis prompt methodology for Claude. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output) for data analysis, policy research, systematic evidence review, investigative research, and research-specific deliverables. Use when building prompts for quantitative data interpretation, evidence-based policy recommendations, literature reviews, causal analysis, hypothesis-driven investigation, or briefing documents. Trigger on: "prompt for data analysis," "research prompt," "policy brief prompt," "literature review prompt," "prompt for investigating," "systematic review prompt," "build a prompt for evidence synthesis," "causal analysis prompt," "briefing document prompt." Also use when user describes a research or analytical task and needs a structured Claude prompt for it. Do NOT use for evaluating existing prompts (use rootnode-prompt-validation if available) or auditing Claude Projects (use rootnode-project-audit if available).
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。
Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.
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。
Interpret a chromatogram from GC or HPLC analysis: verify system suitability parameters, identify peaks by retention time and spectral matching, perform accurate peak integration, calculate chromatographic figures of merit, and assess overall peak quality for reliable quantitation.
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。
Systematically interpret ultraviolet-visible absorption spectra to identify chromophores, classify electronic transitions, apply Woodward-Fieser rules for conjugated systems, and perform quantitative analysis using the Beer-Lambert law.
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 market bubble risk through quantitative data-driven analysis using the revised Minsky/Kindleberger framework v2.1. Prioritizes objective metrics (Put/Call, VIX, margin debt, breadth, IPO data) over subjective impressions. Features strict qualitative adjustment criteria with confirmation bias prevention. Supports practical investment decisions with mandatory data collection and mechanical scoring. Use when user asks about bubble risk, valuation concerns, or profit-taking timing.
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 asks to rank deployment platforms and stacks against their product with quantitative trade-offs. Not for source or remote mutation.
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 guide + benchmarked pitfalls for building Monte-Carlo / Simulated Annealing refinement layers on top of greedy hex-grid layouts for code visualization (packages/gui/src/store/hexLayout.ts, sandbox/hex-sandbox, future Rust ports). Use when: (1) planning to add MC/SA on top of an existing hex-grid pack, (2) deciding whether a simpler SA would match a hierarchical MC with force-directed + chain drag, (3) budgeting wall-clock for million-node cold-start, (4) debugging "blob turns Swiss-cheese" or "folders stop nucleating" during MC refinement, (5) choosing between folder cohesion and raw Σ-link-length as cost function. Contains four hard-won, quantified lessons and anti-patterns that would take a full day of experiments to rediscover.
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。
量化专家经验调优库：按「L1 通用专家调优意见 → L2 结构化专家调优意见 → L3 模型专属专家调优意见」三级递进， 输出量化精度调优的完整手段（离群值抑制 / 量化方法·粒度·对称性选型 / 校准集调整 / 敏感层回退 / 模型专属策略）， 每条结论附专家原因分析与专家意见可信度等级。 本 Skill 是专家经验库，负责说明可选离群值抑制算法并询问用户选择哪些算法，以及回答「怎么调、哪些层需要回退及为什么」；不运行离群值抑制 processor，不修改 YAML、不执行量化、不做 EP 检查 / 服务化 / 任务评测。
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。
执行模型测评（统一入口）。LLM/VLM 走主流程 scripts/run_evaluation.py + Evaluation YAML；model_family=dit 走 DiT 扩展节（references/dit/evaluate_workflow.md，vbench.py 批量推理产出视频）。
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。
为 msModelSlim 适配流程注入反量化能力。先识别模型权重是否可反量化，再实现反量化脚本并接入 model_adapter。当前仅覆盖 FP8 的 per-block 与 per-channel 两类；若格式不确定或无公开反量化规则，要求用户提供反量化脚本或浮点权重。
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。
为 msModelSlim 适配器实现逐层量化（按层加载/懒加载）能力。仅在用户明确要求逐层量化或基础适配因 CPU 内存不足无法全量加载权重时使用。该特性为高阶可选项，不是基础适配必需项。
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 this skill whenever the user wants to create, improve, rewrite, review, tailor, or ATS-optimize a PRODUCT MANAGER resume or CV. Triggers include any mention of 'PM resume', 'product manager resume', 'product resume', 'PM CV', 'product management CV', plain-language asks like 'create my resume', 'build my CV', 'optimize my resume', 'review my resume', 'make it ATS-friendly', 'tailor my resume to this job description', 'help me get hired', the Spanish terms 'hoja de vida' or 'curriculum vitae', or a request to turn product/PM experience into a resume, quantify product impact, write PM bullet points, or prepare a resume for an APM, PM, Senior, Lead, Group, Director of Product, Head of Product, or CPO application. Also use for AI Product Manager, Growth PM, Platform/Technical PM, and Product Owner resumes. Do NOT use for engineering-only resumes, LinkedIn rewrites, portfolios, or cover letters unless the resume is the main deliverable.
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 an electronic product idea, behavior description, reverse-engineering note, or incomplete circuit concept into a decision-ready engineering brief and architecture. Use before schematic design or audit when functions, operating states, power, interfaces, mechanics, production quantity, cost, or measurable requirements are missing or ambiguous.
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。
Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted changes.
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。
Maps observed adversary behaviors, security alerts, and detection rules to MITRE ATT&CK techniques and sub-techniques to quantify detection coverage and guide control prioritization. Use when building an ATT&CK-based coverage heatmap, tagging SIEM alerts with technique IDs, aligning security controls to adversary playbooks, or reporting threat exposure to executives. Activates for requests involving ATT&CK Navigator, Sigma rules, MITRE D3FEND, or coverage gap 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。
"Apply event study methodology to measure abnormal returns and cumulative abnormal returns (CAR) around corporate or market events. Use this skill when the user needs to quantify the market impact of announcements, design event and estimation windows, or when they ask 'did this event affect stock price', 'how do I calculate abnormal returns', or 'what is the market reaction to this announcement'.".
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。
"Solve the newsvendor problem for single-period ordering decisions under uncertain demand. Use this skill when the user needs to determine optimal order quantity for perishable goods, seasonal products, or one-time purchase decisions — even if they say 'how much to order for this season', 'perishable inventory', or 'single-period ordering'.".
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。
"Calculate Economic Order Quantity to minimize total inventory cost (ordering + holding). Use this skill when the user needs to determine optimal order size, balance ordering frequency against storage costs, or set reorder points — even if they say 'how much to order', 'optimal batch size', or 'inventory cost minimization'.".
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。
"Run conjoint analysis to measure how product attributes drive consumer preferences and willingness to pay. Use this skill when the user needs to quantify feature value trade-offs, estimate willingness to pay for specific features, or optimize product configuration — even if they say 'which features do customers value most', 'willingness to pay for feature X', or 'product attribute trade-offs'.".
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。
"Calculate price elasticity of demand to quantify how price changes affect sales volume. Use this skill when the user needs to estimate demand sensitivity, set optimal prices, or evaluate the revenue impact of price changes — even if they say 'how sensitive are customers to price', 'will a price increase hurt sales', or 'elasticity calculation'.".
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。
"Calculate Value at Risk to estimate maximum portfolio loss at a given confidence level. Use this skill when the user needs to quantify downside risk, set risk limits, or report regulatory risk measures — even if they say 'worst case loss', 'portfolio risk', or 'how much could we lose'.".
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。