537 件 ・ 関連度順
概要と使いどころ
Deploy smart contracts and bridge assets to Abstract (ZK Stack L2). Use when an agent needs to deploy contracts on Abstract, bridge ETH/tokens to Abstract, trade/swap tokens, check balances, transfer assets, or interact with Abstract mainnet. Covers zksolc compilation, Hardhat deployment, Relay bridging, DEX trading, and key contract addresses.
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 complete paid tasks on the 0xWork decentralized marketplace (Base chain, USDC escrow). Use when: the agent wants to earn money/USDC by doing work, discover available tasks, claim a bounty, submit deliverables, check earnings or wallet balance, or set up as a 0xWork worker. Task categories: Writing, Research, Social, Creative, Code, Data. NOT for: posting tasks (use the website), managing the 0xWork platform, or frontend development.
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。
Policy-aware Safe portfolio rebalancing assistant for 31Third ExecutorModule.
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。
Search real-time news with bias scoring, get live stock/ETF/crypto data with AI analysis, price options with ML models, and synthesize balanced news perspectives via the Helium MCP server.
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。
Produces structured asset and liability summaries from financial documents for legal proceedings. Extracts valuations, ownership classifications, and encumbrances, then outputs net-worth overview, categorized schedules, disputed items, and documentation gaps. Use when preparing financial summaries for divorce/dissolution, estate planning, bankruptcy, business valuation, or settlement negotiations. Trigger on: net worth summary, marital estate, balance sheet, asset schedule, liability schedule, community/separate property.
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。
Loads, lists, composes, or validates graded evaluation rubrics: PASS/WARN/FAIL grading with weighted criteria and balanced anchor examples for evaluator-style 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。
Use when loading, listing, composing, or validating graded evaluation rubrics — PASS/WARN/FAIL grading with weighted criteria and balanced PASS/FAIL anchor examples. Triggers on /mk:rubric, "load rubric", "compose rubric preset", "validate rubric", or evaluator-style code review that needs anchor-grounded judgments.
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。
Take a chosen visual to finished, production-ready state. Runs cleanup + refinement passes (fix artifacts, balance composition, color-grade, sharpen text, conform to spec/aspect) using image edit/regenerate. Use when the user says "polish this", "finalize", "clean up the image", "production-ready", "fix the artifacts", or has a selected visual that needs finishing. Final stage of the Creative Production set.
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 data from randomized controlled trials. Use when user mentions: treatment effects, ITT analysis, LATE, TOT, compliance analysis, attrition, balance checks, heterogeneous effects, RCT results, experimental analysis.
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 randomization methods for RCTs. Use when user mentions: random assignment, treatment allocation, stratification, blocking, permutation randomization, cluster randomization, randomization balance, misfit handling, re-randomization.
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。
Obtain official Italian documents through Openapi - chamber of commerce reports (visure camerali), financial statements/balance sheets, company files (fascicoli), bylaws, DURC, protest reports. Use when the user needs an official PDF document about an Italian company or person.
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 Kafka consumer groups that survive rebalances, hit the right delivery guarantee (at-most/at-least/exactly-once), and handle poison messages without stalling the topic. Use when picking a partition assignment strategy (Range vs Sticky vs Cooperative-Sticky vs the new KIP-848 protocol), tuning heartbeat / session / max.poll.interval timeouts, choosing manual vs auto offset commits, designing dead-letter or retry topics, or migrating from Classic to the next-gen Consumer protocol. NOT for Kafka cluster ops (broker tuning, partition reassignment), schema design (Avro/Protobuf/Schema Registry), or stream processing topology (use a kafka-streams skill).
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 deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits. Best for teacher-student compression, threshold design, and failure-aware deployment. Activate on "model compression", "teacher- student", "distillation score", "cascade model", "edge deployment", or "model calibration". NOT for generic deep-learning overviews, prompt optimization, or training work without a concrete distillation objective.
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。
API gateway and reverse proxy configuration with Kong, Nginx, Traefik, routing, and auth middleware. Activate on: API gateway, reverse proxy, Kong, Nginx, Traefik, load balancer, ingress, auth middleware. NOT for: rate limiting algorithms (use api-rate-limiting-throttling-expert), service mesh (use service-mesh-microservices-expert).
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。
Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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。
Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
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。
Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.
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。
Detect, date, and contextualize whole-genome duplication (WGD / paleopolyploidy) events using wgd v2 (Chen et al 2024), KsRates (Sensalari 2022 substitution-rate-corrected Ks dating), DupGen_finder (Qiao 2019), MAPS (Li 2018 phylogenomic), POInT (Conant 2008 ordered-block), SLEDGe (2024 ML-based), Whale.jl (Bayesian DL+WGD), and synteny-anchored paranome construction. Use when identifying ancient polyploidy from Ks distributions and synteny block analysis, positioning WGD events relative to speciation, distinguishing tandem from segmental from WGD duplications, dating the 2R/3R vertebrate / fish / salmonid WGDs, building paranome and Ks-age mixture models, applying KsRates substitution-rate correction across lineages, or testing alternative biased-fractionation / dosage-balance models post-WGD.
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。
Resolve subclonal copy number, whole-genome doubling, and copy-number tumor evolution from bulk sequencing with Battenberg, TITAN, and MEDICC2. Covers clonal versus subclonal copy-number states, haplotype phasing for subclonal resolution, cancer cell fraction, whole-genome-doubling detection and timing relative to mutations, mirrored subclonal allelic imbalance, and copy-number phylogenies. Use when a tumor is heterogeneous and bulk data shows non-integer copy number, when calling subclonal CNAs, detecting or timing whole-genome doubling, reconstructing copy-number evolution, or deciding between Battenberg and TITAN.
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。
Quantify homologous recombination deficiency (HRD) from tumor copy number using the three genomic-scar metrics — loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI) — with scarHRD, and via the whole-genome HRDetect and CHORD models. Covers the genomic instability score, the PARP-inhibitor clinical context, whole-genome-doubling correction, and the scar-versus-state distinction. Use when computing an HRD score for PARP-inhibitor eligibility, deriving LOH/LST/TAI scars from allele-specific copy number, deciding between scar-based and mutational-signature HRD methods, or interpreting an HRD result in a BRCA-reverted or low-purity tumor.
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。
Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.
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。
Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.
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。
Detect allele-specific chromatin accessibility from ATAC-seq using WASP, GATK ASEReadCounter, or RASQUAL. Use when mapping cis-regulatory genetic variants from heterozygous SNPs, separating cis from trans regulation, building chromatin QTL (caQTL) maps, validating GWAS variant function with allelic imbalance, or detecting reference allele mapping bias before downstream analysis.
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。
Validate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.
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。