672 件 ・ 関連度順
概要と使いどころ
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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。
Analyse text complexity across quantitative, qualitative, and reader-task dimensions with scaffolding recommendations. Use when selecting texts, assessing readability, or planning reading support.
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。
Perform image-based product search on the 1688 platform. Use an image URL to find visually similar supplier products, returning title, price, minimum order quantity, monthly sales, repurchase rate, trade score, and seller identity. Triggered when users mention 1688 image search, 1688 visual search, find supplier by image, reverse image search 1688, 1688 product sourcing by image, or Alibaba 1688 image search. Even if the user does not explicitly mention "image search," this skill should be triggered whenever a user provides an image URL and wants to find matching or similar products on the 1688 wholesale platform.
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 HealthKit code for authorization patterns, query usage, background delivery, and data type handling. Use when reviewing code with import HealthKit, HKHealthStore, HKSampleQuery, HKObserverQuery, or HKQuantityType.
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 insurance claim adjuster with 15+ years in property/casualty. Specializes in coverage analysis, damage quantification (RC/ACV), liability determination, fraud detection, and settlement negotiation. Use when: insurance claim, damage assessment, coverage dispute, liability analysis, fraud investigation, claim settlement.
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。
Assessing anthropogenic environmental impacts — pollution pathways, habitat destruction and fragmentation, land-use change, invasive species, overharvest, and extinction debt. Covers environmental impact assessment (EIA) methodology, exposure-effect relationships, population viability analysis, IPAT and ecological footprint frameworks, and strategic environmental assessment. Use when quantifying or forecasting human impacts on ecosystems, designing monitoring programs, or evaluating a proposed intervention against a baseline.
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 analysis of DC and AC linear circuits using Ohm's law, Kirchhoff's laws, node and mesh methods, Thevenin and Norton equivalents, phasor analysis, and Bode plots. Covers series and parallel reduction, superposition, source transformation, impedance in the frequency domain, first and second order transient response, and the conversion between time domain and phasor representations. Use when quantitatively predicting voltages, currents, power, gain, phase, or time response of any lumped-element linear circuit.
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 digital signal processing — sampling, aliasing, the Nyquist criterion, quantization, convolution, the discrete Fourier transform, FFT, FIR and IIR filter design, windowing, spectral leakage, and practical issues in fixed-point versus floating-point implementation. Use when designing filters, analyzing spectra, choosing sample rates, reasoning about aliasing, or implementing DSP algorithms on a microcontroller, DSP chip, or host CPU.
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。
Atomic theory, periodic table organization, electron configuration, periodic trends, and isotopes/radioactivity. Covers Dalton through quantum mechanical models, electron shell filling (Aufbau, Hund, Pauli), periodic law and block structure, trend prediction (electronegativity, ionization energy, atomic radius, electron affinity), isotope notation, nuclear stability, and radioactive decay modes. Use when teaching, problem-solving, or reasoning about atomic-level chemistry.
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。
Deductive and inductive reasoning, formal and informal logical structure, validity, soundness, and rules of inference. Covers propositional logic, quantified reasoning, syllogisms, common inference patterns (modus ponens, modus tollens, hypothetical syllogism, disjunctive syllogism), and the distinction between deductive certainty and inductive probability. Use when the question is not whether a premise is true but whether the reasoning from premises to conclusion is logically valid.
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。
Balancing chemical equations, reaction classification, stoichiometric calculations, acid-base chemistry, oxidation-reduction (redox) reactions, and thermochemistry. Covers conservation of mass, mole ratios, limiting reagents, percent yield, Bronsted-Lowry and Lewis acid-base theory, pH calculations, oxidation states, half-reaction balancing, enthalpy, Hess's law, and calorimetry. Use when solving quantitative chemistry problems involving reactions, energy changes, or solution chemistry.
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。
Mathematical verification for physical calculations: unit tracking algebra (exponent maps), PhysicalQuantity pattern for compound units, SI/Imperial mixed-unit handling, Buckingham pi theorem for dimensionless groups, and common engineering dimensionless numbers. Activates for unit verification, dimensional consistency checks, scaling analysis, and calculation validation across all infrastructure domains.
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。
Calculus and continuous transformation — derivatives, integrals, ODEs, Taylor series, and optimization. Computes rates of change, accumulates quantities over intervals, solves differential equations, and classifies critical points. Use when computing derivatives or integrals, solving ordinary differential equations, performing Taylor series approximations, finding critical points, or analyzing continuous change and 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。
Physical applications of mathematics. Constants, quantum mechanics, measurement — where abstract meets embodied.
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。
Cognitive superposition synthesizing Riehl (∞-categories), Sutskever (compression), Schmidhuber (curiosity-driven), and Bengio (GFlowNets) into unified ASI framework with quantum-inspired measurement collapse.
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。
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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。
Optimize AgentDB with quantization, HNSW tuning, caching, batch ops, and pruning.
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。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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。
Quantitative cost measurement for technical research — token injection costs, payload sizes, context window consumption, and file-level counts from actual repo files. Use when a technical-researcher orchestrator needs the cost dimension of adding or changing something: how many tokens will it inject, how big are the artifacts, what is the context window impact? This is NOT blast-radius analysis (which files break) — it covers size, tokens, and performance cost only.
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。
Audits a methods section against a 45-item reporting checklist synthesized from the APSA Experimental Section rubric, JARS-Quant, CONSORT, and DA-RT, covering pre-registration, recruitment, randomization and treatments, sample flow and attrition, sample-size justification, three-tier results labeling, conjoint reporting, validity, and open-science infrastructure. Use when checking whether a methods section reports enough, preparing a submission or replication archive, asking what CONSORT, JARS, or DA-RT require, or documenting a deviation from a pre-analysis plan. Writing that plan before data collection goes to pre-registration-writing.
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 a methods section against a 45-item checklist drawn from the APSA Experimental Section rubric, JARS-Quant, CONSORT, and DA-RT. Use before submission or archiving, or to document a pre-analysis-plan deviation.
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。
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
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 A-share end-of-day market review reports with Pandadata data, covering trade-date checks, index performance and valuation, market breadth, limit-up/down sentiment, industries/concepts, 龙虎榜, block trades, margin financing, northbound holdings, risk notes, and optional scheduled after-close automation. Use when the user asks for 今日复盘, 收盘总结, 每日市场报告, A股复盘, 龙虎榜复盘, 北向资金动向, or to set up an automated daily market 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。