3,730 件 ・ 関連度順
概要と使いどころ
Turn sales history and current stock counts into reorder points, order quantities, and a this-week purchase list -- so a product business stops stocking out of winners and stops burying cash in losers. Handles seasonality, supplier lead times, and the dead-stock cleanup.
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。
Audit and sync design tokens between the source of truth (tokens.json, Figma variables export) and the code that should consume them (Tailwind config, CSS custom properties, theme files). Finds drift -- hardcoded values, orphan tokens, out-of-date copies -- and fixes it in the right direction.
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 the full customer journey from first touch to advocacy. Generates a comprehensive customer-journey.md with all stages, touchpoints, emotions, pain points, opportunities, Mermaid diagrams, and metrics. Use when mapping customer experience, designing onboarding flows, identifying churn risks, or optimizing conversion funnels.
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。
Audit npm dependencies for security vulnerabilities, outdated packages, and unused dependencies. Use when checking for security issues, updating packages, or cleaning up dependencies.
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。
Performs a deep code review of files, modules, a diff, or a branch - finding security vulnerabilities (mapped to OWASP Top 10:2025), correctness bugs, performance problems, and maintainability issues - and returns severity-ranked findings with evidence and concrete fixes. Use when the user asks to review, audit, or sanity-check code, asks "is this safe", "what's wrong with this", "find bugs", or wants a security or performance pass before shipping. For posting line comments on a GitHub pull request, use git-pr-reviewer.
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 a balance sheet (bilan) from a Well workspace. Use when the user asks for a balance sheet, bilan, assets/liabilities/equity, or financial position at a point in time.
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。
Fact lookup: Apple Human Interface Guidelines corpus (greppable markdown). Query on demand for accessibility and ergonomic minimums (hit-target sizes, Dynamic Type), platform terminology, system component capabilities, and App-Review-relevant conventions. Not design direction.
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。
Applies NIST/CWE security hardening to Python and Rust code. Use when auditing code for vulnerabilities or proposing concrete security remediations.
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。
Detects architectural clusters and coupling boundaries via community detection on the code graph. Use when identifying module groupings or refactoring targets.
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。
Searches the code knowledge graph by function, class, or type using FTS5 full-text search. Use when locating code entities by name or qualified path.
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。
Applies hexagonal architecture isolating domain from infrastructure. Use when designing systems where testability and port/adapter separation are priorities.
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。
Applies microkernel architecture with minimal core and plugin extensibility. Use when building platforms where third parties extend core functionality.
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 the faster-whisper package for CTranslate2-backed Whisper transcription, model selection, CPU/CUDA setup, audio utilities, VAD, timestamps, and conversion guidance.
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 face_recognition to detect faces, extract landmarks and encodings, compare identities, run the face_recognition and face_detection CLIs, and troubleshoot dlib/model installation.
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。
Operate the Fengshenbang-LM Fengshen package for pipelines, model families, data/training utilities, examples, and conversion planning.
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。
Route DLTK 0.2.1 medical-imaging workflows across legacy TensorFlow 1.x data, models, Estimators, and deployment utilities.
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。
cuML operating skill for GPU-accelerated classical ML, cuml.accel, Dask multi-GPU workflows, data utilities, and native source-build guidance.
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 CleanRL's single-file reinforcement learning scripts, evaluation utilities, experiment operations, and repository maintenance workflows safely.
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。
Route ControlNet 1.0 source-checkout tasks for annotators, Gradio inference apps, training datasets, and model/weight utilities.
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。
Guides core Autodistill workflows for foundation-model auto-labeling, target-model distillation, CLI/plugin registry use, custom model interfaces, and utilities.
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。
Routes substantive ML, AI, data, scientific-computing, and software-engineering requests to the smallest useful set of managed repository skills. Invoke proactively when a request names or implies a package, framework, model family, dataset, modality, workflow, backend, deployment target, evaluation method, or implementation approach that may benefit from repository guidance, even if no repository is named. Narrow progressively from area to family to repository root: inspect only the one or two most likely area pages; compare candidates by capability, task surface, model/data format, training versus inference versus evaluation intent, runtime constraints, and root-skill description; then open only the selected root and relevant sub-skills, references, or scripts. Select multiple repositories only when each adds a distinct capability. Do not load the whole collection, treat dependencies or incidental integrations as capabilities, choose by name alone, or force a match when no exact taxonomy family applies.
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。
Same pixels, fewer tokens — for user-facing deliverables where visual polish is the spec. Use when building or editing a landing page, marketing site, hero, pricing/feature section, dashboard, or any HTML/CSS UI component. Keeps the full rendered design (layout depth, hierarchy, motion, responsive richness, a11y) and cuts tokens by expressing that design densely — CSS custom properties, shared classes, shorthand, fluid units — instead of by cutting the design. The honey core trims code and prose; this trims how the design is *written*, never how it looks. Reach for it whenever output is user-facing markup, even if the user never says "minimal".
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。
Go outside the founder brief and research the real market before the plan is written: the exact communities, newsletters, and venues where the user's ICP already sits, how comparable products launched and what worked, the problem in strangers' own words, and the rivals and openings the user never mentioned. Saves docs/gtm-cofounder/market-research.md with a link for every finding. Use right after start-here and before strategy-and-roadmap, before picking channels in first-50-users or planning launch-it, or whenever the plan feels like it only repeats what the user already said.
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 and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about AI coding agents, agent runtimes, or similar internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
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。