3,290 件 ・ 関連度順
概要と使いどころ
Think and work like an expert Earthquake Engineer. Use when a task calls for Earthquake Engineer judgment. Reasons from ASCE 7 DRS and SDC, capacity design (R, Cd, Ω₀), ELF/MRS/NRHA and ASCE 41 pushover; models in SAP2000/ETABS/OpenSees with PEER NGA-West2 motions; treats liquefaction, soft-story P-delta collapse, and record-scaling artifacts as first-class failure modes.
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Think and work like an expert Civil Engineer. Use when a task calls for Civil Engineer judgment. Reasons from load paths, factored load combinations, soil-structure interaction, and governing limit states through ASCE 7, ACI 318, AISC 360, and tools like SAP2000/ETABS, PLAXIS, and HEC-RAS while treating missing load paths, connection and foundation failures, differential settlement, and unvalidated FEA as first-class failure modes.
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Think and work like an expert Chemical Engineer. Use when a task calls for Chemical Engineer judgment. Reasons from closing mass, energy, and momentum balances and competing equilibrium/kinetics/transport regimes through Aspen Plus/HYSYS flowsheeting, McCabe-Thiele and Damkohler-based sizing, pinch analysis, and HAZOP/LOPA with API 520/521 relief, while treating azeotrope-pinched columns, reactor runaway, recycle impurity accumulation, and pump cavitation (NPSHa<NPSHr) as first-class failure modes.
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Think and work like an expert Brain–Computer Interface Engineer. Use when a task calls for Brain–Computer Interface Engineer judgment. Reasons from modality–paradigm fit (EEG, ECoG, Utah arrays), CSP/Riemannian decoding (pyriemann, MOABB), BCI2000/OpenBCI pipelines, and charge-density stimulation safety; validates within- vs cross-session claims and treats muscle ICA, impedance drift, and IDE/IRB gates as first-class failure modes.
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Think and work like an expert Astrochemist. Use when a task calls for Astrochemist judgment. Reasons from gas-grain reaction networks, H₂ ortho/para and CR ionization rates through KIDA/kida.uva.2024, CDMS/JPL/Splatalogue line lists, Nautilus/UCLCHEM gas-grain models, ALMA/JWST/LIDA ice–gas linkage, XCLASS LTE fitting, and line-blending discrimination—not generic chemistry.
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Think and work like an expert Aeronautical Engineer. Use when a task calls for Aeronautical Engineer judgment. Reasons from airfoil polars, drag buckets, and static margin through AVL/DATCOM stability derivatives, wind-tunnel blockage and wall corrections, FAR 25 §25.101–25.207 compliance matrices, and AC 25-7 flight-test evidence—not generic aerospace or pure CFD aerodynamics.
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Think and work like an expert Antenna Engineer. Use when a task calls for Antenna Engineer judgment. Reasons from gain–directivity–efficiency, Chu–Harrington bandwidth limits, and array factor through HFSS/CST/FEKO synthesis, IEEE 149-2021 NF/FF/CATR metrology, CTIA TRP/TIS/ECC OTA, and Friis link budgets while treating ground-plane truncation, active impedance in arrays, range ripple, and S₁₁≠pattern conflation as first-class failure modes.
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Think and work like an expert Algorithms Researcher. Use when a task calls for Algorithms Researcher judgment. Reasons from separating problem, model, and cost model (comparison, word-RAM, arithmetic, online) through exchange/matroid greedy proofs, subproblem-DAG dynamic programming, max-flow min-cut and Goemans–Williamson primal-dual rounding, Karp–Rabin fingerprinting, competitive ratio and Yao's principle, PTAS/FPTAS (Williamson–Shmoys), and Instance Space Analysis over DIMACS10/MIPLIB 2017/SuiteSparse while treating amortized-versus-average-case conflation, unproven greedy killed by a 4-node counterexample, Monte Carlo without a false-match probability, DIMACS10 suite overfitting, and 'linear time' hiding word-size tricks over bit-length L as first-class failure modes.
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Think and work like an expert Astronautical Engineer. Use when a task calls for Astronautical Engineer judgment. Reasons from the rocket equation, staging, and mass–power–Δv budgets through NPR 7120.5/ECSS-E-ST-10 subsystem specs, CEA/CEARUN and Sutton propulsion, CLA/pogo and TVAC I&T, GMAT/STK/SPICE mission design, CCSDS TM/TC link margins, and ADCS FDIR while treating unit/frame ICD mismatches, combustion instability, and MCO-class V&V gaps as first-class failure modes.
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Systematic trade idea generation framework, fundamental quantitative and qualitative screening pipeline, catalyst-driven trade structuring, macro-to-micro idea translation, 20-60 day long-short portfolio management process
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Institutional-grade equity research skills for AI agents. 80 Claude-type skills across 14 verticals (equity-research-core, models-and-pitches, bio-pharm, scenarios, quantitative-analysis, idea-generation, options-derivatives, business-intelligence, industry-analysis, macro-strategy, portfolio-strategy, technical-analysis, risk-and-psychology, trading-as-business) powered by agentii.ai's agent-use-ready SEC filing data plane — SEC filings and 15K+ earnings call transcripts, 15.99M XBRL facts, and company profiles for 1,146+ US-public-equity tickers. Features the three-layer retrieval protocol (Document Discovery → Page Map → Deep Read, with deep-outline escalation) — measured, not asserted: 97.5% saved against a sequential read, and the page map itself is 10.0% of one (spec 062 T030, 2026-09-23) — server-side parallel multi-period search via search_cross_period, a full Excel/PPT generation pipeline with 3-tier office backend support, and the spec-046 governance layer (constitution, thesis and single-skill modes, mechanical gates, and a template-owned disclaimer on generated reports).
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Experiment executor and monitor for academic research. 2-agent system covering code experiments (ML training, statistical analysis, ETL, simulation) and human studies (surveys, field studies, interviews). 4 modes: run (execute + monitor code), manage (track human studies), validate (statistical interpretation + reproducibility verification), plan (Socratic experiment design). Use when a researcher wants to run or monitor a research experiment, manage or resume a human-subject study, check the statistics or reproducibility of research results, or design an experiment or study, in English or Chinese (e.g., 跑實驗、管理研究、驗證結果、規劃實驗).
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Generate music with Suno (v5 / v5.5 era, 2026) and advise a production team on what they may legally do with the output. Use this skill when a user wants to create, extend, remaster, or stem-separate a track in Suno; craft Suno prompts (style field, lyrics/metatags, exclude-styles, personas, custom voices); choose a subscription tier for a given use; or get accurate answers about ownership, commercial rights, distribution/monetization on DSPs, the Warner Music Group settlement, the ongoing label/publisher litigation, and Suno's API situation. Do not use it for other music generators (Udio, ElevenLabs Music, Google Lyria) except by explicit contrast, and do not use it as a substitute for a lawyer on a specific commercial deal.
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Plan, generate, edit, reference, and quality-control still images with Kling AI's hosted IMAGE surfaces or Kuaishou's open Kolors checkpoints. Use for Kling IMAGE 3.0/3.0 Omni/O1/2.1 web, official Kling CLI/MCP or Open Platform integration, and local Kolors text-to-image, image-to-image, IP-Adapter, ControlNet, inpainting, or LoRA work. Do not use for Kling video, avatars, lip-sync, unofficial gateway APIs, or for assuming that current hosted Kling Image models are identical to the 2024 open Kolors weights.
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Computes and interprets treatment effect measures (OR, RR, RD, HR, NNT) with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen, MOVER, profile likelihood, Bender NNT) and reports marginal vs conditional estimands per FDA 2023 covariate adjustment guidance. Use when reporting treatment effects in confirmatory trials, comparing effect sizes across studies, or constructing forest plots.
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Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).
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Find cross-database references between NCBI databases using Biopython Bio.Entrez (ELink). Use when navigating gene to protein/structure, sequence to publication, PubMed to GEO, BioProject to SRA runs, or discovering all link relationships for a record. Covers linkname semantics, cmd= variants, asymmetric link warnings, neighbor_history for >200 input IDs, and per-database link tables.
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Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.
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Build a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineering, cross-sample comparison, or fixed-width peak counts; covers Corces 2018 iterative overlap (501 bp), DiffBind summit re-centering, and ENCODE consistency rules.
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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.
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Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.
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Run remote BLAST searches against NCBI servers using Biopython Bio.Blast.NCBIWWW. Use when identifying unknown sequences, finding homologs, picking the correct BLAST program (blastn/blastp/blastx/tblastn/tblastx/psiblast/megablast/dc-megablast), interpreting Karlin-Altschul E-values, avoiding the max_target_seqs trap (Shah 2019), choosing composition-based statistics, or limiting searches by organism. Covers RID lifecycle, database choice (nt/nr/refseq_select/swissprot), word-size and CBS taxonomy.
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Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.
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Discover and invoke 1,676 deduplicated biomedical AI agent skills from the Awesome Bio Agent Skills repository (20 source repos, 15 categories). Use this skill as a router whenever a user needs a bioinformatics/biomedical task (genomics, transcriptomics, single-cell, proteomics, protein design, clinical, epigenomics, multi-omics, pathway, metagenomics, database queries, visualization, workflows): search the index, locate the best-matching skill, fetch its SKILL.md, and follow 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。