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概要と使いどころ
Expert guide for high-performance Vector Databases, Deep RAG architectures, pgvector 0.8+ HNSW, Reciprocal Rank Fusion (RRF), Cross-Encoder Re-ranking, and Late Chunking / Panduan ahli Vector DB, arsitektur Deep RAG, pgvector HNSW, RRF, dan Re-ranking.
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。
Expert guide for ephemeral WebAssembly (WASM) and isolated micro-runtime sandboxes to securely execute AI-generated code, validate database migrations in-memory, and perform sub-millisecond fuzzing / Panduan ahli sandbox WebAssembly (WASM) efemeral dan micro-runtime terisolasi untuk eksekusi kode AI yang aman, validasi migrasi database in-memory, dan fuzzing sub-milidetik.
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Expert-level skill for Go programming (Go 1.25+). Covers high-performance microservices, concurrency patterns, sqlc, net/http, Gin/Echo/Fiber, gRPC, and testing in English and Indonesian.
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Expert guide for reverse-engineering UI/UX interactions from screen-recordings and spatial video, extracting animation spring curves, and generating pixel-perfect Tailwind CSS v4 and Framer Motion code / Panduan ahli rekayasa balik interaksi UI/UX dari rekaman layar dan video spasial, ekstraksi kurva pegas animasi, dan pembuatan kode presisi Tailwind CSS v4 serta Framer Motion.
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 Hinge dating profile through research-backed screenshot audits, photo selection and ordering, conversational discovery, prompt and caption writing, settings review, or post-launch troubleshooting. Use for a full profile rebuild or a focused request about photos, copy, positioning, match quality, or profile performance.
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 the AI-script tells viewers hear in a TikTok spoken script and caption: 2026 vocabulary by density, reveal bridges, staccato stacks, stacked triads, performed sincerity, written-not-spoken phrasing, "hey guys" filler; caps em dashes. Includes --mode audit pre-film check (hook, completion design, caption fit) and --mode profile. Not for beating AI detectors (no edit reliably does). Not for writing from scratch (use tt-hook-scripter). Keywords: humanize script, de-AI, audit before filming.
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。
Spinners, skeletons and staggered reveals that manage wait times. Use when designing loading states, async data or the feel of a slow interface.
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。
Lighthouse audits and Core Web Vitals — LCP, CLS, INP. Use when optimising page load, layout shift, input delay, images, fonts or render-blocking resources.
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。
The philosophy and practical benefits of agent fungibility in multi-agent software development. Why homogeneous, interchangeable agents outperform specialized role-based systems at scale.
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。
Systematic materials selection for engineering design using performance indices, Ashby charts, multi-constraint optimization, and case-based reasoning. Covers the five-step Ashby method — function, objective, constraint, free variable, index — with worked examples for stiffness-limited beams, strength-limited ties, thermally shocked vessels, and minimum-cost components. Use when choosing among metals, polymers, ceramics, and composites under competing objectives.
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。
Symbolic manipulation, equation solving, and algebraic structures for mathematical reasoning. Covers distributive law, factoring, completing the square, linear through polynomial equation solving, systems of equations (substitution, elimination, Gaussian elimination, matrix methods), algebraic structures (groups, rings, fields), modular arithmetic, polynomial theory, and inequalities. Use when solving equations, simplifying expressions, working with algebraic structures, or performing symbolic manipulation.
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。
Vygotsky's Zone of Proximal Development and scaffolding as a working framework for learning support. Covers the ZPD definition, the distinction between independent and assisted performance, the six scaffolding functions (recruitment, reduction in degrees of freedom, direction maintenance, marking critical features, frustration control, demonstration), fading, sociocultural mediation, and the relationship between ZPD and deliberate practice. Use when deciding how much help to give, designing collaborative learning, or assessing a learner's ceiling rather than just their current floor.
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。
Sound systems of human language -- phoneme inventories, the International Phonetic Alphabet, articulatory and acoustic phonetics, phonological rules, suprasegmental features (stress, tone, intonation), and ear training for non-native sound perception. Covers place and manner of articulation, voicing contrasts, vowel space, minimal pair analysis, allophonic variation, phonotactic constraints, connected speech phenomena (assimilation, elision, liaison), and prosody across language families. Use when analyzing pronunciation, teaching sound systems, performing phonemic transcription, or diagnosing intelligibility problems in any language.
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。
Real-world problem formulation, mathematical abstraction, and applied mathematics for translating between practical problems and mathematical frameworks. Covers the modeling cycle (problem identification, assumptions, formulation, analysis, validation, interpretation), Polya's framework adapted for modeling, common model types (linear, exponential, logistic, periodic, power-law), dimensional analysis (Buckingham Pi theorem), optimization (linear programming, gradient descent, constraint satisfaction), probability models (Markov chains, queuing theory, Monte Carlo simulation), statistical modeling (regression, hypothesis testing, model selection), model criticism (overfitting, underfitting, sensitivity analysis), and real-world case studies. Use when formulating mathematical models, performing dimensional analysis, optimizing systems, running simulations, or evaluating model validity.
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。
Foundations of individual economic decision-making, market structures, and strategic interaction. Covers supply and demand analysis, price elasticity, consumer and producer surplus, market structures (perfect competition, monopoly, oligopoly, monopolistic competition), game theory (Nash equilibrium, dominant strategies, repeated games), and welfare economics. Use when analyzing individual markets, firm behavior, pricing strategies, consumer choice, or strategic interactions between economic agents.
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 engineering covering requirements management, the V-model lifecycle, integration planning, verification and validation (V&V), interface control, trade studies, and the NASA Systems Engineering Handbook. Spans the full lifecycle from concept through operations and disposal. Includes requirements decomposition, traceability matrices, configuration management, and technical performance measures. Use when managing complex system development, writing requirements, planning integration, or conducting V&V activities.
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。
The fifteen fundamental cooking techniques that together cover most home kitchen work. Organized by the transformation they perform (heat transfer, water management, emulsion, ferment, cut) rather than by cuisine. Covers knife work, sauté, sear, braise, roast, poach, boil, steam, grill, bake, stew, reduce, deglaze, emulsify, and pickle/ferment, each with the physics, the signal, the common failure, and a diagnostic question. Use when teaching a cook, substituting a technique, or diagnosing why a dish failed.
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.
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MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.
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Manages skill-creator integration with GSD workflows including skill loading, session observation, bounded learning guardrails, and pattern detection. Use this skill whenever: executing any GSD phase (to ensure relevant skills are loaded first), starting or resuming a work session (to check for pending suggestions), the user asks about skills, patterns, or skill-creator status, performing code changes that might represent repeating patterns, the user corrects Claude's output (highest-signal observation), or when skill refinement, creation, or suggestion review is discussed. Critical for maintaining the adaptive learning layer.
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。
Classify the information-need of a query and dispatch it to the appropriate retrieval or reasoning strategy. Use before read-side memory access, before multi-strategy retrieval, or any time you'd otherwise default to "one retriever for everything". Returns a strategy label, a token budget, and a retrieval depth so downstream handlers can be specialised. Backed by Pre-Route (arxiv 2605.10235v2) and MemFlow (arxiv 2605.03312v1), which together show LLMs possess latent routing ability elicitable via a structured prompt — and that externalising the routing decision improves small-model performance by ~2x. Triggers: "route this", "what strategy", "before retrieving", "intent classification", or any query whose ideal handling depends on what KIND of question it is.
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 this appropriateness check the moment you are about to integrate a retrieved long-term memory — a Grove content-addressed hit, a chroma/pgvector neighbour, a memory-consolidation digest, or a MEMORY.md line — into a response, especially anything touching private origins, Fox Companies IP, credentials, or Center Camp / consent-governed content. It answers a question intent-router never asks: not WHAT to fetch or HOW, but WHETHER a correctly-retrieved item should reach output. Relevance is not appropriateness — a perfect similarity match can still be a boundary violation. Default is FAIL-CLOSED: if in-context authorization is uncertain, the memory may inform behaviour but must not be surfaced. Backed by RBI-Eval (arxiv 2606.06055v1). Triggers on surfacing recalled sensitive memory into a response.
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。
Safe file operation patterns. Use when performing bulk file operations or writing deployment scripts.
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。