211 件 ・ 関連度順
概要と使いどころ
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
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Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.
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Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.
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Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels to antibodies, extracting the event matrix, choosing a reader, or bridging FCS to the scanpy/AnnData ecosystem before preprocessing.
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Map nucleosome center positions, occupancy, and fuzziness from ATAC-seq fragment-size patterns using NucleoATAC, ATACseqQC, DANPOS3, or scprinter. Use when characterizing nucleosome organization at promoters and enhancers, calling +1/-1 nucleosomes flanking NFRs, generating V-plots for chromatin structure visualization, or comparing nucleosome positioning between conditions.
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南派三叔盗墓探险思维——下墓倒斗、悬疑探险、铁三角兄弟情、挖坑艺术。 触发词：「南派三叔视角」「像南派三叔那样写」「下墓」「粽子」「铁三角」「坑王」。 擅长：盗墓探险、悬疑氛围、第一人称叙事、挖坑填坑、兄弟情。
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Orientação LGPD para operações de tratamento no Brasil. Cobre bases legais (Art. 7º e Art. 11), RIPD, incidentes (Arts. 48-49), direitos dos titulares (Art. 18) e transferências internacionais. Triggers: LGPD, RIPD, ANPD, proteção de dados, dados pessoais, consentimento, encarregado, DPO Brasil, incidente de dados, legítimo interesse.
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Configure or change the approved worker models for codex-on-crack, inspect available models and compatibility, preview role files, or undo a setup. The user selects the lead in Codex. Setup preserves root settings, credentials, unrelated agents, and config.toml.
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Work with the opt-in codex-on-crack build panel. Report registered run and review status, submit new screenshot evidence for a registered review, and send the user to the panel to decide. Never record user feedback or visual approval yourself.
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Plan and execute substantial multi-file builds, features, migrations, and refactors with Astra as orchestrator and DeepSeek V4.1 Flash as the native Codex implementation worker. Use for phased planning and delegated build execution, including existing Superpowers or GSD plans. Skip trivial edits and explicitly single-agent tasks. Worker children must not invoke this orchestration skill.
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Start a substantial build with Codex on Crack. Inspect the user's own configured models and readiness, recommend a lead/worker/tool shape with reasons, and let the user decide before work starts. Use before delegating; keep small or solo tasks direct.
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Lead substantial Codex builds with one configured implementation worker and lead acceptance. Use when the user requests codex-on-crack, names a model to delegate work to, asks to run subagents through Claude, or requests a lead/worker split. Keep host-only work direct; honor explicit external model choices. Workers must not invoke this orchestrator.
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Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
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Dung khi mot CA NHAN can viet BAI DAI de xay uy tin, khong phai copy ban hang — 3 cau truc chuan cho thi truong VN gom PAS-Insight cho founder, Story-Lesson-CTA cho coach, Hook-List-Reveal cho creator; 6 cong thuc hook long-form; ky thuat nhip cau; do dai theo nen tang LinkedIn, Facebook, newsletter; ma tran tai su dung 1:5. Kich hoat khi user nhac 'viet bai LinkedIn', 'thought leadership', 'long form post', 'newsletter ca nhan', 'viet bai chuyen sau', 'khong biet viet gi tren trang ca nhan', 'muon duoc coi la chuyen gia'. Khong dung cho — copy quang cao tra tien thi dung skill 05-copy-quang-cao; caption ngan cho fanpage doanh nghiep thi dung skill 37-caption-social; kich ban video ngan thi dung skill 04-script-video.
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Dung khi can biet doi thu dang lam gi va minh dang dung o dau — phan tich 3 tang doi thu truc tiep, gian tiep, thay the; ban do dinh vi, SWOT, benchmark content va gia, khoang trong thi truong con bo trong. Kich hoat khi user nhac 'nghien cuu doi thu', 'phan tich canh tranh', 'doi thu dang chay gi', 'competitive analysis', 'sao doi thu ban re hon minh', 'doi thu moi noi', 'minh thua doi thu cho nao', 'benchmark thi truong'. Dung ca khi user chi gui link fanpage hoac website doi thu va hoi 'xem giup ho dang lam gi'. Khong dung cho — chot dinh vi cho chinh minh thi dung skill 58-positioning; theo doi lien tuc thao luan mang xa hoi thi dung skill 15-social-listening; nghien cuu tep de chay ads thi dung skill 51-audience-research.
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Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single-cell analysis.
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Harmony batch correction for scRNA-seq and other omics. Removes batch effects from PCA embeddings while preserving biology. Run after PCA, before UMAP. Scales to millions of cells. Python (harmonypy, scanpy) and R (Seurat).
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Annotated matrices for single-cell genomics. Stores X with obs/var metadata, layers, embeddings (obsm/varm), graphs (obsp/varp), uns. Use for .h5ad/.zarr I/O, concatenation, scverse integration. For analysis use scanpy; for probabilistic models use scvi-tools.
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Query CELLxGENE Census (61M+ cells). Search by cell type/tissue/disease/organism; get AnnData, stream out-of-core, train PyTorch models. For your own data use scanpy; for annotated data use anndata.
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Open-source FAIR biology data framework. Version artifacts (AnnData, DataFrame, Zarr), track lineage, validate via ontologies (Bionty), query datasets. Integrates with Nextflow, Snakemake, W&B, scVI. For scRNA-seq use scanpy; for ontology lookups use bionty.
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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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The telemetry each Active Directory technique generates and what alerts a defender: Kerberoasting produces Event 4769 with RC4 encryption (0x17) and an MDI alert, DCSync produces Event 4662 with the DS-Replication-Get-Changes GUID, AS-REP roasting produces Event 4768 with no pre-auth, LSASS dumping is blocked by EDR, plus a lateral-movement telemetry table by protocol (SMB/WMI/WinRM/RDP/DCOM). Use this whenever you run or plan an offensive AD technique and need to know what noise it makes, when writing the engagement's detection notes, or when a defender wants to know what to monitor. Teaches red teamers what is loud and blue teamers what to watch. Standard-tooling knowledge, no vendor engine.
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A high-level conceptual mapping from Active Directory attack techniques to the compliance controls they touch. Kerberoasting relates to authentication and logging, so it brushes ENS op.acc.5 / op.exp.8, NIS2 Art.21(2)(h), DORA RTS Art.9 / Art.21. Use this when a reader wants to understand which regulatory control an AD finding relates to, or to add an orientation note to a technical finding. This is a conceptual, orientative mapping only; it is NOT an auditor-defensible, curated, ID-by-ID control matrix. Covers ENS (op.acc.*, op.exp.*), NIS2 (Directive 2022/2555 Art.21), and DORA (RTS 2024/1774).
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The order of operations for an Active Directory penetration test: setup, collection, exploitation, post-processing. Use this whenever you are planning or driving an AD assessment and need to know what to run before what and why (map before you exploit; harvest easy credentials before spraying to avoid lockouts; collect the graph before you reason about paths). Covers phase sequencing with standard tooling: netexec/nxc, impacket, certipy, bloodyAD, kerbrute, BloodHound CE. Invoke it at the start of an engagement, when deciding the next phase, or when a step feels out of order.
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。