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概要と使いどころ
SQLite-based ticket tracking for structured development workflows. Use this skill when the user asks to initialize a ticket database, import requirements, list tickets, get the next ticket to work on, mark tickets as done, or check ticket status. Essential for managing epics, user stories, and acceptance criteria.
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MUST USE for building FastAPI/REST API applications with Aerospike database (aerospike-py Rust/PyO3 async client). Covers AsyncClient lifespan management, FastAPI Depends injection, aerospike_py exception-to-HTTP-status mapping (RecordNotFound→404, RecordExistsError→409, BackpressureError→503, AerospikeTimeoutError→504), POLICY_EXISTS_CREATE_ONLY/UPDATE_ONLY for CRUD semantics, NamedTuple attribute access (record.bins not tuple unpacking), client.ping() readiness probe, batch_read returning dict, batch_write with in_doubt retry signal, and global AerospikeError handler. Triggers on: FastAPI + Aerospike, REST API + aerospike-py, CRUD API with Aerospike, client.ping() health probe, batch_read/batch_write endpoint, web server/HTTP service backed by Aerospike NoSQL, uvicorn + Aerospike.
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Bootstrap a complete .github/workflows/ set for a new project: CI (build/test/lint), CD (workflow_dispatch with env choice + OIDC + ECR), security-scan (gitleaks + dependency scan + CodeQL), and optional database-migration. Use this skill when the user wants to add GitHub Actions CI/CD to a project that does not already have it, or when they want to add missing workflows alongside existing ones.
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。
Three-file state management for session continuity. Maintains STATE.md, DECISIONS.md, and PROGRESS.md as human-readable session context alongside the SQLite ticket database. Use this skill when updating workflow state, logging decisions, tracking session progress, or resuming work after a context break.
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。
Add a new journal to the MedSci Skills profile database. Extracts metadata from author guidelines, generates write-paper (detailed) and find-journal (compact) profiles in canonical format with quality gates.
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。
MUST USE for deploying Aerospike on Kubernetes. Contains CE-specific YAML templates, validated AerospikeCluster CR examples, and critical constraints that prevent enterprise-only config mistakes (feature-key-file, security sections crash CE pods). Without this skill, deployments fail on first attempt due to CE 8.1 breaking changes (data-size not memory-size, no info port 3003) or webhook map/list shape rules (service/network must be maps; logging must be a list). Triggers on: deploy/create/set up Aerospike on K8s, kind, minikube, EKS, GKE; AerospikeCluster CR; ACKO operator; spec.operations / WarmRestart / PodRestart YAML; NoSQL database on Kubernetes. 9 ready-to-use YAML examples from minimal single-node to full-featured multi-rack.
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。
Configures integration tests with WebApplicationFactory and Testcontainers. Provides test database setup, authentication helpers, and utilities for testing API endpoints with real dependencies.
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Execute C# with Unity APIs when existing uloop tools cannot inspect or edit enough. Use for scene, prefab, SerializedObject, AssetDatabase refresh/.meta generation, menu, or PlayMode automation.
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DBマイグレーション作成時に使用。バージョン番号競合防止。mainブランチ確認必須。
Get started with your Open SaaS project — fetches docs, checks Wasp installation, and helps you start your database and app.
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。
Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context 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。
Tests whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.
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。
Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.
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。
Assigns isolate identity at the right resolution for the question -- ANI / Mash species triage, 7-locus MLST historical comparability, cgMLST / wgMLST outbreak resolution (chewBBACA, BIGSdb, Ridom SeqSphere, EnteroBase HierCC), in-silico serotyping (SISTR, SeqSero2 for Salmonella; SerotypeFinder for E. coli; Kaptive K/O for Klebsiella; SeroBA for pneumococcus; spa + SCCmec for S. aureus), and lineage callers (TB-Profiler / Mykrobe Coll-Napier barcode for MTBC, Pangolin + Nextclade for SARS-CoV-2, PopPUNK GPSC for S. pneumoniae). Use when typing bacterial isolates for surveillance or outbreak investigation, choosing between cgMLST allele distance and core-SNP distance for cluster definition, harmonising calls across schemas / database versions (chewBBACA vs Ridom vs EnteroBase), assigning MTBC lineage with the Napier 2020 90-SNP barcode, calling Salmonella serovar via SISTR with monophasic Typhimurium awareness, running Pangolin UShER mode with explicit pangolin-data version pinning, or selecting a typing resolution to match the surveillance question.
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。
Retrieve records from NCBI databases using Biopython Bio.Entrez (EFetch, ESummary). Use when downloading sequences, fetching GenBank/GenPept records, getting document summaries, parsing nested XML, navigating GI deprecation, choosing between rettype+retmode combinations, and parsing into Biopython SeqRecord/SwissProt objects. Covers nucleotide, protein, gene, pubmed, sra, gds, taxonomy, snp, clinvar.
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 NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell). Use when finding records by keyword, building reproducible field-qualified queries, navigating the Entrez Query Translator, exploiting the history server for large result sets, handling retmax caps, or interpreting weekly index lag. Covers PubMed, Nucleotide, Protein, Gene, SRA, GEO, Assembly, Taxonomy, ClinVar, dbSNP.
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。
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.
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。
Bulk-query Ensembl BioMart (and other BioMart instances) for cross-database ID mapping, gene/transcript/exon coordinates, and ortholog tables. Use when batch-converting Ensembl IDs to other namespaces (HGNC, RefSeq, UniProt, Entrez), pulling gene coordinate tables for thousands of genes, building ortholog wide-tables across species, or replacing slow Ensembl REST loops with one-shot bulk export. Encodes BioMart's XML query format, R biomaRt vs Python pybiomart trade-off, mart-vs-dataset hierarchy, and the URL endpoint that's BioMart-specific (separate from rest.ensembl.org).
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。
Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.
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。
Pull pre-computed ortholog calls from public databases (OrthoDB, Ensembl Compara, OMA browser, eggNOG, PANTHER, KEGG Orthology, HomoloGene) via their REST APIs. Use when orthologs are already curated upstream, when the question is "what is the X ortholog of Y" rather than "how to infer orthology de novo", when batch-mapping gene IDs across species, or when comparing the resources for consensus calls. Encodes confidence-level semantics, 1:1 vs 1:many vs many:many, HomoloGene deprecation, and when to defect to de novo computation.
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Calls microsatellite instability from WES/WGS/targeted-panel with MSIsensor, MSIsensor-pro, MSIsensor-ct (panel-aware), MSIngs, MANTIS, MSIPanel, MSIDetect, and ngsMSI for FDA pembrolizumab MSI-H pan-tumor / Lynch syndrome / dMMR ICI biomarker. Use when stratifying ICI eligibility (Le 2015), pairing MSI with TMB-H (Sha 2020 / Salem 2018), screening Lynch syndrome (universal IHC + MSI), or distinguishing MSI-H tumors from POLE-exo hypermutator with overlapping signatures.
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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.
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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。
Calculates tumor mutational burden from WES/WGS/panel data with Friends of Cancer Research harmonization equations, per-assay calibration (FDA 10/Mb = 7.8 TSO500 = 8.4 OncomineTML), synonymous/indel/germline filtering, hypermutator tiering, blood TMB, and integration with HLA-LOH and neoantigen quality (Luksza 2017 fitness). Use when assessing ICI eligibility under tumor-specific cutoffs (McGrail 2021), comparing tissue vs bTMB, or auditing TMB-H reporting against ESMO 2024 and FDA pembrolizumab pan-tumor 2020.
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。