562 件 ・ 関連度順
概要と使いどころ
Clinical Decision Support System (CDSS) development patterns. Drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), alert severity classification, and integration into EMR workflows.
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 QA testing for web applications, desktop apps, and workflows. Three tiers: QUICK (critical/high severity only — 5 min), STANDARD (+ medium severity — 15 min), EXHAUSTIVE (+ cosmetic — 30+ min). Tests user flows, finds bugs, reports with severity classification, and optionally auto-fixes with atomic commits. Produces before/after health scores and ship-readiness summary. Use when user says "test this", "QA", "find bugs", "does this work?", or "is it ready to ship?". Voice triggers: "quality check", "run QA", "test the app". For report-only (no fixes), use with --report-only flag.
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。
Drafts USMCA/NAFTA Certificates of Origin for preferential tariff treatment on US-Mexico-Canada goods. Verifies origin criteria, HS classifications, and regional value content. Use when preparing trade certificates, customs origin documentation, duty preference claims, or cross-border shipment certifications.
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。
Prevent MSHA citations at cement plants before the inspector arrives. Free CementOps Compliance Suite skill. 30 CFR Part 56 hazard classification, stop-work authority, citation defense strategies, walk-through prep checklists, and rebuttal letter drafting.
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。
Drafts a Bring Your Own Device (BYOD) policy for U.S. employers governing personal device access to company systems. Covers MDM enrollment, encryption, remote wipe authority, privacy expectations, data classification, and regulatory overlays (HIPAA, GLBA, SOX, GDPR). Use when creating or updating BYOD policies, mobile device security policies, or personal device programs.
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 a Rule 506(d) bad actor disqualification review for private securities offerings, including a covered persons register, tailored questionnaires mapping to each disqualifying event category, a documented reasonable-care diligence record, and classification analysis of flagged events with remediation or disclosure recommendations. Use this skill when conducting Rule 506(d) diligence, identifying covered persons for a Regulation D offering, drafting bad actor questionnaires, or analyzing potential disqualifying events involving criminal convictions, SEC orders, SRO sanctions, or state regulatory actions. Also trigger when the user mentions bad actor screening, covered persons analysis, Rule 506(d) questionnaire, disqualification waiver, or reasonable care defense. Even if the user just says "bad actor check" or "506(d) diligence," use this 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。
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。
Ticket/PRD intake: product-area classification, completeness scoring, RCA, technical assessment. Works with Jira, Linear, GitHub, or manual paste. Use to analyze or triage a ticket.
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。
Domain-aware complexity routing. Scans task description for domain keywords, matches against domain-complexity.csv, returns complexity level, workflow intensity, and execution tier. Extends Phase 0 orchestration.
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 task agent and complexity tier at Phase 0 orient. Invoked first in every workflow. Provider adapters select any model/runtime policy. NOT for runtime agent routing inside a single skill (see orchestration-rules.md).
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 code verification using multiple models with severity classification
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。
Plot per-gene mutation distributions on a protein-domain map (lollipop / needle plots) showing mutation position, recurrence count, and variant classification with maftools, g3-lollipop, trackViewer, and ProteinPaint. Use when visualizing recurrent mutation hotspots on a single gene's protein, marking domain boundaries from UniProt/Pfam, comparing missense vs truncating distributions, or contrasting two cohorts on the same lollipop.
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。
Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.
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。
Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.
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。
Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma, Schwede & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2021 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.
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。
Classify constitutional (germline) copy number variants for clinical reporting using the 2019 ACMG/ClinGen technical standards points-based framework, with ClassifyCNV and AnnotSV for semi-automated scoring. Covers the separate copy-number-loss and copy-number-gain rubrics, the five-tier classification, ClinGen haploinsufficiency/triplosensitivity and dosage-sensitive regions, de novo and segregation evidence, and population-frequency benign evidence. Use when assigning pathogenic/likely-pathogenic/VUS/likely-benign/benign to a constitutional CNV, scoring a CNV against ACMG/ClinGen criteria, or distinguishing the automatable evidence from the case-specific evidence requiring manual input.
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Applies ACMG/AMP 2015 framework with ClinGen SVI specifications, Tavtigian 2018/2020 Bayesian point system, Abou Tayoun 2018 PVS1 decision tree, Pejaver 2022 and Bergquist 2025 calibrated PP3/BP4 thresholds for REVEL/BayesDel/AlphaMissense, Brnich 2020 PS3/BS3 OddsPath, Walker 2023 SpliceAI splicing framework, and AMP/ASCO/CAP 2017 tumor tiers. Use when classifying germline variants P / LP / VUS / LB / B, applying VCEP-specific CSpec rules, computing Whiffin BS1, or assigning cancer Tier I-IV per Li 2017.
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。
Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains. Uses ChIPseeker (R), HOMER annotatePeaks.pl (CLI), pyranges (Python), GREAT/rGREAT (regulatory domain gene-set enrichment), ChIP-Enrich (locus-length-adjusted), ENCODE SCREEN cCRE classification (PLS/pELS/dELS/CA-CTCF/CA-H3K4me3), and ENCODE-rE2G for cell-type-specific enhancer-gene linking. Handles nearest-TSS vs host-gene ambiguity, promoter window definition, and feature priority. Use when assigning genomic context to peaks, linking enhancer peaks to target genes, classifying peaks against ENCODE cCRE registry, or running gene-set enrichment on peak-associated genes.
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。
Queries ClinVar for variant pathogenicity classifications, ClinGen VCEP curations, and somatic-vs-germline interpretations via REST API, weekly VCF, or bulk XML. Use when determining clinical significance, triangulating conflicting interpretations, or aggregating evidence against the ACMG/AMP framework with ClinGen SVI specifications.
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。
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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。
Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.
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。
Build image generation pipelines with Stable Diffusion, FLUX, ControlNet, LoRA, and ComfyUI workflows. Activate on: image generation pipeline, ComfyUI workflow, ControlNet, LoRA training, diffusion model. NOT for: video generation (ai-video-production-master), image classification (computer-vision-pipeline).
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 in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).
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。