1,824 件 ・ 関連度順
概要と使いどころ
Semantic code search using natural language queries. Use when users ask "where is X implemented", "how does Y work", "find the logic for Z", or need to locate code by concept rather than exact text. Returns file paths with line numbers and code snippets.
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Currency, language, region and unit selectors live in a low-profile header or footer spot. Use when designing locale switchers or product-wide preference controls.
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Settings for experts who are not developers — domain language, sane defaults, grouping by professional concept. Use when designing settings panels, solver configuration or constraint editors.
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。
Generate AI-friendly repository documentation with the SourceSage CLI. Use when Codex needs to run `sage` or `sourcesage` to create `.SourceSageAssets/Repository_summary.md`, analyze the current repository or another local repository, switch the output language between English and Japanese, use `--lite` to keep the summary small, change the output directory, or optionally create a deprecated tag-diff release report with `--diff`.
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Mindset theory, implicit theories of intelligence, and motivation research applied to learning. Covers fixed vs. growth mindset, the difference between process praise and ability praise, the "not yet" intervention, attribution patterns after failure, mindset critiques and the replication record, and the integration of mindset work with self-determination theory (autonomy, competence, relatedness). Use when a learner is stuck on motivation, disengaging after failure, or when a curriculum's feedback language is undermining persistence.
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。
First-order logic with quantifiers, predicates, functions, and identity. Covers syntax, interpretation over a domain, the universal and existential quantifiers, quantifier scope and binding, translation from natural language with multi-variable predicates, natural deduction for quantifiers, undecidability at first order, and the soundness/completeness theorems. Use when propositional logic is insufficient -- whenever "for all" or "there exists" matters to the argument.
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。
Pragmatics and communicative competence across languages -- speech acts (Austin/Searle), Grice's conversational maxims and implicature, politeness theory (Brown & Levinson), face-threatening acts, discourse structure, turn-taking, repair strategies, register and formality, cross-cultural communication, communicative strategies (circumlocution, approximation, appeal for assistance), and the gap between grammatical competence and communicative performance. Use when teaching communication skills, analyzing cross-cultural misunderstandings, building conversational fluency, or understanding why grammatically correct sentences can be pragmatically inappropriate.
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。
Extending propositional and predicate logic with modal operators -- necessity and possibility, and their relatives (obligation, knowledge, belief, time). Covers Kripke possible-worlds semantics, accessibility relations, the main modal systems (K, T, S4, S5), translation from natural language, and applications in epistemic, deontic, and temporal reasoning. Use when ordinary logic is insufficient to capture distinctions like "must" vs "might," "knows" vs "believes," "always" vs "sometimes."
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。
Vocabulary learning strategies and retention science for any language -- frequency-based word selection, spaced repetition systems (Ebbinghaus forgetting curve, Leitner system, SM-2 algorithm), cognate exploitation, word family networks, context-based learning, collocations, depth of word knowledge (form, meaning, use), reading progression from controlled to authentic texts, and productive vs. receptive vocabulary thresholds. Use when building vocabulary plans, optimizing review schedules, selecting learning materials, or diagnosing vocabulary gaps.
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。
Practical argument analysis and construction -- how to identify, evaluate, reconstruct, and build real arguments in natural language. Covers argument identification (premises, conclusions, intermediate steps), charity and steel-manning, argument mapping, the distinction between deductive validity and inductive strength, burden of proof, and the rhetorical context in which arguments operate. Use when the goal is to engage with real-world argument rather than to formalize it.
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。
Persuasion and rhetorical analysis grounded in classical and modern frameworks. Covers Aristotle's three appeals (ethos, pathos, logos), rhetorical situation (audience, purpose, context), argument structure (Toulmin model), logical fallacies, persuasive writing, propaganda analysis, and ethical persuasion. Use when constructing arguments, analyzing rhetoric, evaluating persuasive messages, detecting fallacies, or studying the relationship between language and power.
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。
Systems programming from memory management through networking. Covers memory models (stack vs heap, manual allocation, garbage collection, ownership/borrowing), concurrency (threads, mutexes, channels, async/await, actor model, data races vs race conditions), operating system concepts (processes, virtual memory, page tables, system calls, file descriptors, signals), compilation (lexing, parsing, code generation, linking, static vs dynamic libraries), networking fundamentals (TCP/IP, sockets, HTTP, DNS, TLS), and the hardware-software boundary (caches, cache lines, false sharing, memory-mapped I/O). Use when working with low-level code, diagnosing system-level bugs, understanding performance characteristics, or bridging between high-level languages and machine behavior.
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。
Remove AI-isms and artificial language patterns from text. Makes documentation, comments, commit messages, and prose sound more natural and human. Based on Wikipedia's "Signs of AI writing" patterns.
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多语言本地化配音：把中文带货视频本地化为英语、日语、韩语、东南亚语（印尼/泰/越/马来/菲律宾）、西语等版本——本地化改写口播（不是直译）、选对 edge-tts 音色、生成对应字幕与花字，并用同一份 storyboard 一键渲染多语言成片。Use when the user wants dubbing, 多语言, 本地化, 翻译配音, 出海, localize a product video for TikTok Shop US/UK/SEA, English voice-over, or multiple language versions of one video.
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。
Bria.ai image API — generate from text prompts, edit with natural language, remove backgrounds for transparent PNGs, and create product lifestyle shots. Authenticates via OAuth device flow, caches credentials in ~/.bria/credentials, calls 20+ endpoints. Commercially safe, royalty-free. Triggers on: remove background, transparent PNG, cutout, generate image, create banner, edit photo, product photography, upscale, restyle, inpainting, outpainting, lifestyle shot, background replacement, object removal, photo restoration.
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 when the user wants to discover, define, or refine their product's aesthetic identity. Triggers on "what should this feel like?", "help me figure out the vibe", "something feels off visually", "I want it to feel like [X]", "define the visual language", or when a project has no established design direction. This is the art director skill - it helps users articulate something they can feel but can't name. Also handles quick cohesion checks when the orchestrator needs one during cycle 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。
Formal oracle for abductive inference that selects the minimal hypothesis best explaining a set of observations. Provides three sub-oracles: MCMC via monad-bayes, Gemini for natural language, and propagator networks for constraint domains. Use when performing hypothesis selection, explaining observations, or solving inference-to-best-explanation problems.
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 independently assessing how bounded Python code could accomplish the same behavior with less maintained code, more Pythonic design, newer language/stdlib capabilities, or well-maintained ecosystem libraries.
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Guide for creating Claude Code plugin hooks — language selection heuristic (Node.js default, match project runtime when obvious), hooks.json configuration, event selection, prompt-based vs command hooks, ${CLAUDE_PLUGIN_ROOT} paths, stdio suppression, timeout sizing, and testing. Use when adding hooks to a plugin, creating PreToolUse/PostToolUse/Stop/SubagentStop/SessionStart/UserPromptSubmit hooks, or wiring hook scripts to hooks.json.
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Executes SAM Stage 5 — opens an attempt on a single ARTIFACT:TASK, dispatches it to a fresh stateless agent session, runs quality gates, and records an ARTIFACT:EXECUTION with implementation results and verification output on the task. Use when Stage 4 Task Decomposition is complete and tasks are ready for execution, when re-executing a task after Stage 6 returns NEEDS_WORK, or when dispatching a task to a language-appropriate specialist agent via the development harness pipeline.
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。
Reviews Node.js runtime behavior in JavaScript or TypeScript, including server entrypoints, route handlers, middleware, streams, processes, dependencies, and environment configuration. Loads alongside language and CLI checks when applicable; investigates blocking I/O, backpressure, injection, lifecycle leaks, and misleading failure signals.
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。
Opt-in recreation of the Bun + TypeScript framework used to build Vapi's landing-page voice agents from a ROUGH_DRAFT.md spec, including a scenario registry, language stacks, prompt composer, assistant builder, and idempotent bootstrap script. Use only when the user explicitly invokes vapi-bootstrap-framework or specifically asks to reproduce or experiment with the landing-page-agent architecture. Do not use for ordinary Vapi assistant builds; use create-assistant, create-tool, create-squad, and vapi-prompt-builder instead. Targets Bun + TypeScript + @vapi-ai/server-sdk.
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, create, run, monitor, and maintain Vapi Simulations for assistants and squads. Use for turning plain-language requirements into a coverage plan, simulation personalities, scenarios, structured-output success criteria, simulations, suites, extending an existing suite, chat or voice runs, tool mocks, target variables, lifecycle webhooks, regression coverage, launch readiness, CI quality gates, run-result analysis, and simulation API validation errors. Do not use for fixed-turn mock-conversation Evals unless the user is deciding between Evals and Simulations.
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。
Runs several language models as independent coders on one labeling or discovery task and treats disagreement as data. Covers diverse panel assembly, independent voting, consensus rules, Cohen’s and Fleiss kappa, Krippendorff’s alpha, correlated-error limits on validity claims, human validation, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, how to combine model labels, or what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.
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。