「aggregation」の検索結果

Think and work like an expert Biophysical Chemist. Use when a task calls for Biophysical Chemist judgment. Reasons from free energy landscapes, binding equilibria (K_d, ΔG = ΔH − TΔS), and probe–system coupling through ITC, SPR/BLI, smFRET, AUC, and global fitting (KinTek, SEDFIT) while treating probe perturbation, mass-transport-limited kon, aggregation-driven avidity, and two-state melting violations as first-class failure modes.

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。

Think and work like an expert Chemical Biologist. Use when a task calls for Chemical Biologist judgment. Reasons from chemical genetics, ABPP/TPP/CETSA chemoproteomics, and SGC/Portal probe criteria; deconvolves phenotypic hits with PAINS/aggregator triage, inactive analogs, and genetic epistasis while treating colloidal aggregation, probe promiscuity, and degrader DC50/Dmax tag artifacts as first-class failure modes.

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。

Think and work like an expert Bioprocess Engineer. Use when a task calls for Bioprocess Engineer judgment. Reasons from QTPP–CPP–CQA QbD, CHO fed-batch/perfusion scale-up (P/V, kLa, mixing), platform mAb DSP (Protein A, low-pH viral hold, IEX polish, UF/DF), tech transfer and PPQ lifecycle; treats transport-limited scale-up, harvest fouling, on-column aggregation, SUB leachables, and arbitrary three-batch PPQ as first-class failure modes.

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。

Think and work like an expert Biochemist. Use when a task calls for Biochemist judgment. Reasons from thermodynamics, enzyme mechanisms, and binding energetics; designs orthogonal purification and assay readouts while controlling oxidation, aggregation, coupled assays, and activity-vs-abundance confounds.

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。

Writes and executes SQL queries from simple SELECTs to complex multi-table JOINs, aggregations, and subqueries. Use when the user asks to query a database, write SQL, run a SELECT statement, retrieve data, filter records, or generate reports from database tables.

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。

Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.

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 SDS-PAGE or protein purification gel images using DNA sequence, protein sequence, base-pair length, expected protein size, and lane labels. Use when the user wants to judge whether a gel ran well, whether the main band matches the expected product, or whether there may be impurities, degradation, aggregation, or low expression.

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。

Guidance for cell-free protein synthesis (CFPS) optimization. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).

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。

Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation. For standard design, use proteinmpnn. For ligand-aware design, use ligandmpnn.

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。

Cell-free protein synthesis (CFPS) planning and optimization guidance. Use when: (1) Planning CFPS experiments, (2) Troubleshooting low yield or aggregation, (3) Optimizing DNA template design for CFPS, (4) Expressing difficult proteins (disulfide-rich, toxic, membrane).

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。

Cube is an open-source semantic layer that sits between a data warehouse and analytics applications and serves governed metrics over REST, GraphQL and SQL APIs. Use when a user asks to define cubes, measures and dimensions, add pre-aggregations, build a metrics API or embedded analytics, set up multi-tenant row-level security, or query Cube from React or the REST API.

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。

ClickHouse is a columnar OLAP database built for real-time analytical queries over massive datasets. It supports a familiar SQL interface with powerful aggregation functions, materialized views for pre-computed rollups, and high-throughput inserts that make it ideal for event tracking, time-series analytics, and ad-hoc exploration of billions of rows.

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。

Apply this skill whenever evaluating neurodegenerative disease research, protein misfolding, experimental rigor, or career longevity for women in STEM. Use this to channel the thinking of Virginia M.-Y. Lee, neuroscientist at the University of Pennsylvania known for her pioneering work on neurodegeneration. Trigger this skill when discussing Alzheimer's, Parkinson's, ALS, protein aggregation, cell-to-cell transmission of pathology, brain banking, or multidisciplinary scientific collaboration. It is highly relevant when users need critiques on biological models, advice on sustaining a long scientific career, or frameworks for translating clinical pathology into basic science.

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。

Team-theory primitives for cooperative agents: what each subagent sees, when to communicate, aggregation, Shapley credit, coalition topology. Use when organizing subagents.

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 when writing, reviewing, or modifying SQL queries — including SELECT, CTEs, joins, aggregations, window functions, and validating query correctness or 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。

Structural pattern analysis for writing-mode review. Performs paragraph-level decomposition and pattern aggregation. Invoked by the team lead during editorial 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。

Expert guide for structured logging (Pino, Winston), error tracking (Sentry), log aggregation (Axiom, Datadog), request correlation, and GDPR-compliant log management / Panduan ahli untuk logging terstruktur (Pino, Winston), pelacakan error (Sentry), agregasi log, korelasi request, dan manajemen log sesuai GDPR.

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。

Parallel agent fleet dispatch with progress tracking and result aggregation. Launch N agents, monitor completion, merge results. Proven at 7-agent 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。

Pick among candidate outputs (code, configs, plans) by running them on diverse inputs and clustering by behavioural fingerprint, rather than by textual aggregation or log-probability. Activates when an executor returns multiple plausible candidates that need disambiguation, when output-majority voting would be the default choice, or when reviewing generated code that has not yet been validated. The 2026 evidence (Semantic Voting, arxiv 2605.08680v1) is that any execution-based selector dominates output-majority voting by 19-52pp; sketch-generated inputs beat random fuzz by 11.3pp. Triggers: "pick the best candidate", "majority vote on code", "select from N samples", "validate the generated output", "behavioural verification".

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。

OpenStack monitoring operations skill for deploying, configuring, and operating the cloud health monitoring stack. Covers Prometheus metric collection and scrape targets, Grafana dashboard provisioning and visualization, Alertmanager notification channels and routing, alerting rules for service health and resource exhaustion, service endpoint health checks, log aggregation strategies, SLA tracking with availability and response time percentiles, and capacity trend analysis from historical metrics. Use when deploying monitoring via Kolla-Ansible, configuring alert thresholds, troubleshooting blank dashboards, tuning noisy alerts, or analyzing cloud performance trends.

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。

Enhanced web search and real-time content retrieval via Grok API with forced tool routing. Use when: (1) Web search / information retrieval / fact-checking, (2) Webpage content extraction / URL parsing, (3) Breaking knowledge cutoff limits for current information, (4) Real-time news and technical documentation, (5) Multi-source information aggregation. Triggers: "search for", "find information about", "latest news", "current", "fetch webpage", "get content from URL". IMPORTANT: This skill REPLACES built-in WebSearch/WebFetch with Grok Search tools.

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。

Decompose dense codebase-wide, multi-document, PDF, and aggregation work even when the input fits the context window, following Recursive Language Models (Zhang, Kraska, Khattab, 2025). Use when the user asks to analyse all files, a whole repo, all docs, large PDFs, or to aggregate or multi-hop across scattered sources. Skip one file, one function, a single needle, or a one-page PDF conversion. Triggers: long context, context rot, large codebase, many files, all files, big document, multi-document, PDF, aggregate, summarize everything, codebase-wide, multi-hop, recursive, sub-agents, map-reduce.

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。

Multi-agent swarm coordination patterns. Orchestrates parallel agent execution, manages agent communication, handles task distribution, and coordinates results aggregation.

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。

Implement pharmaceutical serialisation and track-and-trace systems compliant with EU FMD, US DSCSA, and other global regulations. Covers unique identifier generation, aggregation hierarchy, EPCIS data exchange, and verification endpoint integration. Use when implementing serialisation for a new product launch, integrating with the EMVS/NMVS, designing DSCSA-compliant transaction exchange, building an EPCIS event repository, or extending serialisation to additional markets (China, Brazil, Russia).

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。