6,375 件 ・ 関連度順
概要と使いどころ
Use when targeting Environmental Pollution or deciding whether a contamination/pollutant-impact manuscript fits this venue. Encodes the journal's fit, the source-fate-impact evidence bar, measurement-and-controls expectations, house style, official-submission re-check, and desk-reject heuristics.
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 designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.
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 building the robustness section for a 《会计研究》 (Accounting Research) manuscript — replacing accounting measures with alternatives, adding governance / institutional controls, placebo tests, and endogeneity follow-ups — so the main result survives an accounting referee's scrutiny. Use after mechanism is settled.
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 appraising the cumulative evidence and ensuring balance in an Academy of Management Annals (Annals) review — weighing conflicting findings by credibility, steelmanning rival schools, and handling the author's own work even-handedly. Audits evidence quality and fairness; it does not design the framework (amann-organizing-framework) or build exhibits (amann-tables-figures).
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Use when strengthening AAMAS reproducibility evidence for interaction claims, including proofs and game definitions, opponent and population sets, self-play protocols, random seeds, compute, uncertainty on strategic outcomes, baselines, and consistency between what the paper claims about agents and what the artifact can actually show.
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Design review and validation workflow for KiCAD projects via MCP tools. Triggers on: "review my design", "check for errors", "audit", "DRC", "ERC", "find problems", "design review", "is this ready", "validate", "check my schematic", "check my PCB", "what's wrong", "run checks", "pre-fab 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。
Manufacturing and fabrication workflow for KiCAD projects via MCP tools. Triggers on: "send to fab", "order boards", "gerbers", "JLCPCB", "manufacturing", "export for production", "pick and place", "assembly files", "generate fabrication outputs", "BOM for fab", "production files", "fab house".
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。
Workflow skill for KiCAD PCB layout and routing via MCP tools. Triggers on: "layout the board", "route traces", "PCB", "place footprints", "copper pour", "board outline", "differential pair", "board setup", "track width", "via", "zone", "design rules", "stackup", "silkscreen".
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。
Mandatory operating rules for ANY task involving KiCAD projects. Loaded when the user mentions KiCAD, schematics, PCBs, or any .kicad_* file. Prevents file corruption by routing all changes through Konnect MCP 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。
Workflow skill for KiCAD schematic design via MCP tools. Triggers on: "design a circuit", "add a component", "wire up", "connect pins", "build schematic", "place resistor", "place cap", "place IC", "schematic", "add symbol", "net label", "power rail".
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。
Find and install skills from the Orthogonal skill library. Use when you need capabilities you don't have, want to discover available skills, or need to add new tools to your agent.
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。
Track what key opinion leaders (KOLs) in your space are posting on LinkedIn and Twitter/X. Surfaces trending narratives, high-engagement topics, and early signals of emerging conversations before they peak. Chains linkedin-profile-post-scraper and twitter-mention-tracker. Use when a marketing team wants to ride trends rather than create them from scratch, or when a founder wants to know which topics are resonating with their audience.
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 detailed, differentiated content briefs at scale. Each brief includes SERP analysis, competing page breakdown, unique angles from real customer language (reviews, Reddit), internal linking plan, and SERP feature targets. Batch mode produces 10-50 briefs in one run. Crushes generic "keyword density" briefs from tools like Surfer or Clearscope.
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。
Find ICP-fit leads from KOL audiences on LinkedIn. Given a list of KOLs, scrapes their most relevant high-engagement post from the last 30 days, extracts engagers (reactors + commenters), pre-filters by position, enriches top profiles, and ICP-classifies. Cost-controlled: 1 post per KOL. Use when someone wants to "find leads from KOL audiences" or "scrape engagers from influencer posts" or after running kol-discovery.
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。
Extract competitor and customer intelligence from any company's landing page HTML. Discovers tech stack, analytics tools, ad pixels, customer logos, SEO metadata, CTAs, hidden elements, and more. No API keys required.
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。
Reverse-engineer a company's sales and marketing tech stack from public signals. Detects CRMs, cold email tools, people databases, ad pixels, email delivery services, and outbound sending domains via DNS records, website source inspection, Apify technology profiling, blacklist checks, and public spam complaint searches. Works on single companies or batches. Outputs a structured markdown report per company.
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 3-5 messaging variants for a value proposition, design structured A/B tests, and analyze results to determine which framing resonates most with ICP. Tests can run via LinkedIn organic posts, cold email subject line splits, or both. Pure reasoning for variant generation and analysis — the user deploys the tests through their own tools. Use when a team can't decide between messaging angles and needs data, not opinions.
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。
Check that the UI you actually rendered uses the design system, by reading computed styles in the running app against the project's design tokens. Catches hardcoded hex colors, off-palette backgrounds, invisible or unusable controls, and animations that never ran. Use after building or restyling a component, when a design review is wanted, when a UI looks slightly off but nobody can say why, or when a design system exists and nothing checks whether the code follows 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。
Use when integrating legacy code, custom code, or C/C++ code into Simulink via C Function blocks. Configure Simulink C Function blocks programmatically using the SymbolSpec API. TRIGGER when the user: - Asks about calling or wrapping C/C++ code or libraries in Simulink - Wants to create, configure, or use a C Function block - Mentions integrating custom code, legacy code, or external C/C++ into a model - Asks to integrate a C++ class into a Simulink model or test bench - Mentions inputs/outputs/parameters for a C++ algorithm block
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。
Predict B-cell and T-cell epitopes for vaccine antigen design and epitope mapping with BepiPred-3.0, DiscoTope-3.0, the IEDB tools, and EL-mode MHC presentation. Encodes the load-bearing asymmetry that T-cell epitope prediction is mature (it reduces to MHC presentation, AUC>0.9) while B-cell prediction is unreliable (linear predictors ~AUC 0.6 because ~90% of real epitopes are conformational) — so structure-based DiscoTope-3.0 on AlphaFold models is the only defensible B-cell path, propensity scales are obsolete, and NetChop is largely redundant on EL-trained models. Use when mapping epitopes or selecting vaccine antigens. MHC binding lives in mhc-binding-prediction.
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。
Balances Hi-C contact matrices (ICE via cooler.balance_cooler, KR/SCALE/VC context), computes distance-decay expected with cooltools (expected_cis per-diagonal P(s), expected_trans scalar), builds observed/expected (O/E) matrices, and diagnoses polymer state from the P(s) log-derivative. Covers the within-matrix-vs-cross-sample distinction (balancing is NOT a normalizer), the equal-visibility assumption that CNV/aneuploidy violates (use raw counts for copy-number), cis-only balancing, mad_max/blacklist masking before balancing, multiplicative cooler weights vs divisive juicer weights, and the resolution-vs-depth budget. Use when balancing a .cool/.mcool, computing expected or P(s), making O/E matrices for compartments/loops, deciding ICE vs KR vs SCALE, choosing a resolution for a given depth, or troubleshooting NaN/all-NaN balanced matrices; route cross-sample comparison to hic-differential.
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。
Rank and prioritize neoantigen/epitope candidates by likely T-cell response using NeoFox feature annotation, PRIME2.0, BigMHC-IM, the Łuksza/Balachandran fitness model (agretopicity + foreignness), and pVACtools tiering. Encodes the field's hard truths that immunogenicity is the least-solved layer (dedicated scores ~AUROC 0.6-0.7, modest PPV), that scores are valid only for RANKING within one patient (never absolute go/no-go or cross-patient), that DAI has anchor-inflation and WT-denominator traps, and that stacking weak correlated scores into one number is a red flag. Use when ordering a candidate list for a vaccine. Binding lives in mhc-binding-prediction; calling in neoantigen-prediction.
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。
Discovers, classifies, filters, and quantifies full-length transcript isoforms from PacBio Iso-Seq/Kinnex (HiFi) and Oxford Nanopore (cDNA/direct-RNA) long reads, using the isoseq+pigeon pipeline, SQANTI3, and ONT tools (IsoQuant, FLAIR, Bambu, StringTie2). Covers why a novel isoform is an artifact until proven otherwise (RT template-switching, intra-priming, and 5' degradation manufacture junctions and truncations), the SQANTI3 structural categories and their trust order, the Kinnex skera-split step, orthogonal CAGE/poly-A/short-read-junction validation, and why long-read isoform quantification needs EM. Use when building a full-length isoform catalog, classifying/filtering long-read transcripts, running Iso-Seq or ONT cDNA/dRNA analysis, or judging novel-isoform reliability.
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。
Analyze spatial cell-cell interactions, neighborhoods, and niches in IMC/MIBI data with squidpy and imcRtools, covering neighborhood-enrichment permutation nulls, the abundance-vs-density confound, inhomogeneous Ripley's K, cellular-neighborhood discovery, graph-construction (contact vs proximity), and edge effects. Use when testing whether cell types co-locate, choosing a spatial null, building a neighbor graph, discovering tissue niches, or deciding whether a spatial pattern is real or a density/segmentation artifact.
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。