798 件 ・ 関連度順
概要と使いどころ
Builds regime detection models with hidden Markov, threshold, and Bayesian change-point methodologies for strategy adaptation. Use when modeling regime changes, detecting market shifts, or adapting strategies to market conditions.
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Evaluates OFS sector investments with rig count sensitivity, day rate analysis, and technology adoption curves. Use when analyzing oilfield services, evaluating service company economics, or assessing technology uptake.
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。
QA validation and fix loop workflow — validates implementation completeness then iterates fix cycles until all acceptance criteria pass and quality gates clear
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 execution against the frozen Execution Contract, produce Evidence/Gaps, and decide pass or unfreeze. Use after ac-execute or at milestone acceptance gates.
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。
Plan, implement, or improve an internationalization and localization strategy for UI content, formatting, and regional adaptation. Use when the user asks to add i18n, localize, translate, support multiple languages, handle regional formats, manage locale switching, or build a multilingual product.
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。
Adapt designs for narrow, medium, wide, embedded, or print web contexts without losing usability, hierarchy, or target sizing. Use when the user mentions responsive design, breakpoints, narrow layouts, viewport changes, touch-capable browsers, or cross-context adaptation.
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 JSON Web Token (JWT) vulnerabilities — alg=none bypass, RS256→HS256 key confusion, weak HMAC secret cracking, kid path traversal, JWKS injection, jku/x5u header attacks, embedded JWK confusion, expired-token acceptance, claim mutability, and token replay. Use when an app uses JWT for authentication or stateless sessions.
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。
用户需要在新的环境/市场/文化中部署已有方案时;当同一个问题在不同地区/场景下表现不同时; 当需要将通用方案本地化时;当用户说"为什么在这里不管用"或"换个地方方案就失效了"时。 不适用于: 环境因素不重要、问题与场景无关的纯技术/逻辑场景。
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 prompt-building methodology for content and communications tasks. Provides 11 tested approaches (3 identity, 4 reasoning, 4 output) for writing, editing, content strategy, copywriting, audience analysis, editorial evaluation, cross-channel adaptation, and persuasion design. Use when building a Claude prompt or system prompt for content creation, editorial workflows, or communications tasks. Trigger on: "build a prompt for writing," "prompt for content strategy," "prompt for copywriting," "prompt for blog posts," "prompt for email sequences," "system prompt for editorial," "prompt for messaging framework," "content brief prompt," "prompt for audience analysis," "prompt that produces better content." Do NOT use for writing content directly — this Skill builds prompts that produce better content. Do NOT use for evaluating or scoring existing prompts — use rootnode-prompt-validation if available. Do NOT use for general prompt compilation — use rootnode-prompt-compilation 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。
Use when the user needs to scope, write, or audit a product spec — a PRD, feature spec, user stories, acceptance criteria, or a "spec this out"/"break this into stories"/"definition of done" request — or wants an existing spec reviewed for gaps and anti-patterns. Triggers whenever a feature idea needs to become structured requirements that align a team on what to build. For the analytics behind success metrics, use data-analysis; for the technical design, use software-architect.
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。
Release a new version of an R package including version bumping, NEWS.md updates, git tagging, GitHub release creation, and post-release development version setup. Use when a package is ready for a new patch, minor, or major release, after CRAN acceptance to create the corresponding GitHub release, or when setting up the development version bump immediately after a release.
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 and maintain a product or project backlog with prioritized items, acceptance criteria, and estimates. Covers user story writing, MoSCoW prioritization, backlog grooming, item splitting, and status tracking. Use when starting a new project and converting scope into actionable items, during ongoing grooming before sprint planning, re-prioritizing after stakeholder feedback or scope changes, or splitting oversized items into implementable pieces.
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Derive a theoretical result step-by-step from first principles or established theorems, with every step explicitly justified and special cases checked. Use when deriving a formula or theorem from first principles, proving a mathematical statement by logical deduction, re-deriving a textbook result for verification or adaptation, extending a known result to a more general setting, or producing a self-contained derivation for a paper or thesis.
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Structured GitHub issue creation from review findings or task breakdowns. Groups related findings into logical issues, applies labels, and produces issues with standard templates including summary, findings, and acceptance criteria. Designed to consume output from review-codebase or similar review skills.
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Execute structural metamorphosis using strangler fig migration, chrysalis phases, and interface preservation. Covers transformation planning, parallel running, progressive cutover, rollback design, and post-metamorphosis stabilization for system architecture evolution. Use when assess-form has classified the system as READY for transformation, when migrating from monolith to microservices, when replacing a core subsystem while dependents continue operating, or when any architectural change must be gradual rather than big-bang.
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Run Proto UI's dynamic comprehension assessment and derive an unsigned local task-fit result. Use when an Agent needs review calibration or a fresh autonomous task and review ceiling. Do not use a self-result as identity, permission, or acceptance evidence.
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Independently review a Proto UI change against current authority, bounded scope, executable evidence, projections, provenance, and integration conditions. Use for pull-request review, acceptance review, pre-merge audit, or fresh-context verification. Do not use to approve one's own work.
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。
Measure the active Agent model with pinned offline ModelTrace sampling and return an unsigned closed-set attribution record. Use before Agent-originated commits, Issue/PR creation or material updates, comments, reviews, and current collaboration writes in either execution mode. Do not treat a fingerprint, declared model label, or record reference as authenticated identity, permission, independent review, or acceptance.
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 the Codex Optimized Development, Evaluation, and Remediation (CODER) Loop with an orchestration-only coordinator, non-overlapping task-family owners, fresh independent reviewers, evidence-scoped remediation, and final coordinator acceptance. Discover and compose optional MSW, Timebox, MSL, and Codex Voice Optimizer companions as the Slopware Dev Stack without making them dependencies. Use in Codex when the user invokes the CODER Loop, asks to divide a complex change into bounded implementation families with independent reviewers, requests multi-agent implementation without overlapping ownership, requires review and repair until every blocking gap is independently rechecked, asks for the CODER tutorial or Slopware Dev Stack, or wants CODER to install or use its companions. Do not trigger for an ordinary single-agent change, a review-only request, or a non-Codex host.
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 researching Claude Code or Codex upstream releases, selecting adaptations, or preparing Harness upgrades. Local-only; not for distribution.
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。
End-to-end automated model quantization and accuracy tuning workflow. Use when user asks for automated model quantization and accuracy tuning, e.g. "自动量化", "量化调优", "一键量化", "精度调优", etc. 当用户给出多卡（≥2 卡）卡号时，量化阶段通过 EP 并行适配（msmodelslim-ep-parallel-adaptation）保证多卡 EP 并行，调优主流程仍在本 Skill。
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。
Adapt torch_npu.profiler interface for AI frameworks and training/inference code on Ascend NPU. Use this skill whenever users need to integrate profiling, add performance data collection, or instrument PyTorch code with torch_npu.profiler on Huawei Ascend NPU platforms. Triggers on: "torch_npu profiler", "NPU profiling", "Ascend profiler", "profiler adaptation", "profile training", "profile inference", "add profiler to framework", "性能采集", "profiling 数据", "昇腾 profiler", and any request to add performance monitoring to Ascend NPU code.
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 NowStack Mobile changes across web, iOS, Android, and Convex with real runtime evidence. Use for `/ns verify`, cross-surface regression checks, acceptance proof, or release verification.
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Use when a user needs SCI/SCIE/ESCI/SSCI journal-submission help: evidence-backed candidate-journal discovery from a title, abstract, keywords, or manuscript text; public-metric and risk lookup for a known journal; or a pre-submission compliance check of a draft against a chosen journal's Author Guidelines and same-journal conventions. Also for Chinese requests such as 选刊、投稿期刊推荐、期刊查询、中科院分区、投稿前检查、期刊要求核对. This skill produces candidates, evidence, checks, and uncertainty; it does not judge research quality, predict acceptance, choose a uniquely best journal, draft prose, or simulate peer review.
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。