「sam」の検索結果

Audit, design, and migrate Identity and Access Management — cloud provider IAM (AWS, GCP, Azure), identity providers (Okta, Entra ID / Azure AD, Auth0, Google Workspace), application authorization (RBAC, ABAC, ReBAC), and federated identity. Use when the user mentions 'IAM,' 'identity,' 'access management,' 'least privilege,' 'role design,' 'SSO,' 'SAML,' 'OIDC,' 'OAuth,' 'JIT access,' 'just-in-time access,' 'break-glass,' 'service accounts,' 'RBAC,' 'ABAC,' 'privilege creep,' 'role explosion,' 'identity governance,' 'IAM strategy,' 'identity migration,' 'Okta,' 'Entra ID,' 'Azure AD,' 'Auth0,' 'Cognito,' or needs identity consultant-level guidance.

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Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step (never current-vs-final-of-baseline) and separates proxy metrics from downstream targets. Use when the user asks to compare runs, check if a run is improving, track lag against a baseline, rank experiments, or evaluate run-vs-run performance.

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Read from a UModel object-graph semantic layer with the `umctl` CLI (MCP alternative noted). Three kinds of read: (1) entities & relationships / topology (`.entity`, `.topo`) and (2) the model itself (`.umodel`, and `.entity_set` methods, including entity-linked `list_skills`) return real rows; (3) metrics & logs (`get_metrics` / `get_logs`) return an executable *plan* — PromQL / Elasticsearch DSL with the entity id pre-substituted — that you run against the backend. Against a PaaS endpoint the same calls return data rows instead of a plan. Use to query or read UModel entities, relations, topology, or model metadata; to read a service's metrics or logs; to look up services and dependencies; or to discover what objects, datasets, and methods exist. For root-cause analysis on top of these reads, see the `umodel-rca` skill. Triggers: UModel, object graph, .entity / .topo / .umodel / .entity_set, query entities, read topology, read metrics / logs, get_metrics / get_logs, list services / dependencies / datasets / skills, 实体查询, 关系/拓扑查询, 读模型, 读指标, 读日志, 查指标, 查日志, 查服务依赖.

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Market data preparation including OHLCV resampling, gap handling, anomaly detection, normalization, and multi-source merging

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Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

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Use when escalating privileges on a Windows host — SeImpersonate Potato chains (GodPotato/PrintNotifyPotato), service & DLL hijacking, UAC bypass (fodhelper/ICMLuaUtil), kernel EoP + BYOVD (CVE-2025-29824), token-rights abuse, LSASS/SAM/DPAPI credential harvesting

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Diagrams and checkable docs from a codebase. Draws ERDs, C4 context/container/component views, API endpoint catalogues, lifecycle/state machines, and service-to-service call maps as a validated JSON spec plus a standalone HTML viewer, with Mermaid export. Imports OpenAPI, GraphQL SDL and Prisma; otherwise reads the code (NestJS, TypeORM, Express) and writes the spec. Use for: database diagram, ERD, data model, table relationships, C4 or architecture diagram, system context, API map, endpoint list, "show me the endpoints", status flow, state machine, allowed transitions, "what happens after X is approved", how services call each other, microservice map, Mermaid diagram. Also writes documentation as a fact graph: each statement carries its source and a computed confidence, and a drift check fails CI when the code moves. Use to document a system, write architecture or onboarding docs, or explain how a system works.

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Coordinator-only native Codex issue-to-develop workflow. Specialists use only their assigned role and task.

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Use when handling external API rate limits (e.g., OpenAI 429s, HubSpot or Stripe rate limits), preventing duplicate work from rapid event bursts (debouncing user actions), spreading load over time, ensuring per-tenant fairness, processing events in batches, limiting concurrent runs of the same operation, or assigning priority to important runs. Covers Inngest flow control: concurrency limits with keys, throttling, rate limiting, debounce, priority, singleton, and event batching.

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GATK Best Practices for germline SNP/indel calling from WGS/WES BAMs. Per-sample HaplotypeCaller GVCFs, GenomicsDBImport, GenotypeGVCFs joint calling, VQSR or hard filters. Requires BWA-MEM2-aligned, markdup, BQSR BAMs. Use DeepVariant for a faster DL alternative; GATK is the NIH/ENCODE standard.

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GWAS and population genetics tool. Processes PLINK (.bed/.bim/.fam), VCF, and BGEN; runs QC (MAF, HWE, missingness), IBD estimation, PCA, and linear/logistic regression GWAS. Outputs Manhattan-ready summary stats. Use regenie or SAIGE for biobanks (>100k samples) needing mixed models.

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Guide to quality filtering raw VCF files before computing summary stats (Ts/Tv ratio, variant counts, AF distributions). Covers detecting raw VCFs via FILTER column and QUAL inspection, QUAL-based filtering with bcftools, Ts/Tv interpretation, and when NOT to filter. Read before any variant-level QC task. See bcftools-variant-manipulation for advanced filters, gatk-variant-calling for caller config, samtools-bam-processing for upstream alignment QC.

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ENCODE Portal REST API for regulatory genomics: TF ChIP-seq, ATAC-seq/DNase-seq peaks, histone marks, and RNA-seq across 1000+ cell types. Search experiments by assay/biosample/target; download BED/bigWig; retrieve SCREEN cCREs by region or gene. Use to annotate variants with regulatory tracks, find open chromatin in a cell type, or fetch peak files for ChIP/ATAC analysis. For regulatory variant scoring use regulomedb-database; for GWAS associations use gwas-database.

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Python library for genomic interval ML. Train/apply region2vec embeddings turning BED regions into vectors, index interval datasets for ML, search embedding space with BEDSpace, and evaluate embedding quality. Use for chromatin accessibility clustering, regulatory element classification, and cross-sample region comparison.

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NGS CLI for ChIP/RNA/ATAC-seq. BAM→bigWig with RPGC/CPM/RPKM, sample correlation/PCA, heatmaps/profiles around features, fingerprints. For alignment use STAR/BWA; for peak calling use MACS2.

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Query COSMIC for cancer somatic mutations, gene census, mutational signatures, drug resistance variants. REST API v3.1 supports gene/sample/variant queries; free registration. For germline use clinvar-database; for drug-target data use opentargets-database or chembl-database-bioactivity.

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Look up EMDB cryo-EM density maps and fitted atomic models via the entry REST API + EBI Search WS. Fetch entry metadata (resolution, method, organism, sample), map download URLs, fitted PDB IDs, and citations. Keyword search via EBI Search. No auth. For atomic coordinates use pdb-database; for AlphaFold predictions use alphafold-database-access.

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Constraint-based (COBRA) analysis of genome-scale metabolic models: FBA, FVA, knockouts, flux sampling, production envelopes, gapfilling, media optimization. Use for strain design, essential gene ID, flux analysis. For kinetic modeling use tellurium; for visualization use Escher.

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Multi-modal single-cell analysis with muon/MuData. Joint RNA+ATAC (10x Multiome), CITE-seq (RNA+protein), other multi-omics. MuData holds per-modality AnnData with shared obs. WNN joint embedding, per-modality preprocessing, MOFA factor analysis. Use scanpy-scrna-seq for single-modality RNA; use muon when combining 2+ omics from the same cells.

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Fast short-read DNA aligner for WGS/WES/ChIP-seq. 2× faster BWA-MEM successor; outputs SAM/BAM with read group headers for GATK. Primary plus supplementary records for chimeric reads. Use STAR for RNA-seq splice-aware alignment; Bowtie2 is a comparable alternative.

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Query OpenAlex REST API for 250M+ scholarly works, authors, institutions, journals, concepts. Search by keyword, author, DOI, ORCID, or ID; filter by year, OA, citations, field; retrieve citations, references, author disambiguation. Free, no auth. For PubMed use pubmed-database; preprints use biorxiv-database.

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PyTorch Geometric (PyG) for graph neural networks: node/graph classification, link prediction with GCN, GAT, GraphSAGE, GIN. Message passing, mini-batches, heterogeneous graphs, neighbor sampling, explainability. Supports molecules (QM9, MoleculeNet), social/knowledge graphs, 3D point clouds. For non-graph DL use PyTorch; for classical graph algorithms use NetworkX.

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Register, segment, filter, resample 3D medical images (MRI, CT, microscopy) via SimpleITK Python; DICOM, NIfTI, multi-modal. Rigid/affine/deformable registration, threshold/region-growing segmentation, Gaussian/morph filtering, label stats, format conversion. Use to align volumes across timepoints/modalities, segment fluorescence, or convert DICOM→NIfTI.

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Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.

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。