「debugging」の検索結果

Loads, converts, and manipulates Hi-C contact matrices in cooler format (.cool/.mcool/.scool) and Juicer .hic, using cooler (Python + CLI), hic2cool, and hictk. Covers the single-resolution mcool URI (file.mcool::/resolutions/<bp>), the load-bearing divisive-vs-multiplicative weight-naming rule (KR/VC/VC_SQRT auto-divisive vs cooler's multiplicative weight), what survives .hic<->.cool conversion (FRAG matrices and norm vectors do not), raw-vs-balanced coarsening, the .pairs upper-triangle/chromsize-order contract, and chrom-naming/bin-table provenance. Use when loading a cooler, converting .hic to .mcool, selecting a resolution, building a cooler from pairs or a matrix, coarsening/zoomifying, importing Juicer norm vectors, or debugging all-NaN balanced matrices and chr1-vs-1 empty fetches.

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Imports gene expression count matrices from featureCounts, HTSeq, STAR ReadsPerGene, Salmon/kallisto via tximport or tximeta, RSEM, 10X Genomics MTX/H5, AnnData H5AD, and RDS. Handles silent-miscounting traps (featureCounts -p v2.0.2 API break, STAR strandedness column choice, salmon NumReads-sum without tximport, RSEM non-integer expected_count, GENCODE _PAR_Y suffix, zero-length-transcript TPM divide-by-zero), and encodes the tximport countsFromAbundance decision tree with the "lengthScaledTPM is not TPM" warning. Use when assembling a gene-by-sample count matrix from aligner or quantifier output, importing salmon/kallisto for DESeq2 vs limma-voom, choosing strandedness column for STAR, debugging zero-count panics, or building tx2gene mapping.

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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.

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。

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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Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.

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。

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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Reads, writes, and converts molecular file formats (SMILES, InChI, SDF V2000/V3000, MOL2, PDB, MMTF) using RDKit and Open Babel with rigorous handling of aromaticity perception, stereochemistry, implicit/explicit hydrogens, kekulization, and salt/fragment separation. Use when loading chemical libraries, debugging parse failures, or preparing molecules for downstream standardization, descriptor calculation, or docking.

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。

Debug Spring Boot issues systematically. Use when encountering bean errors like NoSuchBeanDefinitionException, circular dependency issues, application startup failures, JPA/Hibernate problems including LazyInitializationException and N+1 queries, security misconfigurations causing 403 Forbidden errors, property binding failures, CSRF token issues, or any Spring Boot application requiring diagnosis with Actuator endpoints and JVM debugging.

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Guide for when to use and when not to use return validators in Convex functions. Use this skill whenever the user is writing Convex queries, mutations, or actions and needs guidance on return value validation. Also trigger when the user asks about Convex type safety, runtime validation, AI-generated Convex code, Convex AI rules, Convex security best practices, or when they're debugging return type issues in Convex functions. Trigger this skill when users mention "validators", "returns", "return type", or "exact types" in the context of Convex development. Also trigger when writing or reviewing Convex AI rules or prompts that instruct LLMs how to write Convex code.

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。

Guide for building and refactoring a FastAPI + Prefect + PostgreSQL/pgvector analytics pipeline that extracts insights from chat data using LLM classification, embeddings, UMAP/HDBSCAN clustering, and narrative generation. Use this skill whenever working on this project's codebase — including adding features, refactoring, debugging, creating new endpoints, modifying pipeline stages, or reviewing architecture decisions. Also trigger when the user asks about project structure, stack decisions, async migration, or best practices for this specific system.

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。

Investigate implementation failures using root-cause-first debugging. Use when an implementer is blocked, verification fails, or repeated remediation does not converge.

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。

Trace bugs backward through call stacks and execution flows to identify the original source of errors, invalid data, or incorrect behavior. Use when debugging complex issues, tracing error origins, investigating data corruption, following execution paths, identifying where invalid data enters the system, or finding root causes of cascading failures.

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Safety guardrails for destructive commands. Warns before rm -rf, DROP TABLE, force-push, git reset --hard, kubectl delete, and similar destructive operations. User can override each warning. Use when touching prod, debugging live systems, or working in a shared environment. Use when asked to "be careful", "safety mode", "prod mode", or "careful mode". (gstack)

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 debugging with root cause investigation. Four phases: investigate, analyze, hypothesize, implement. Iron Law: no fixes without root cause. Use when asked to "debug this", "fix this bug", "why is this broken", "investigate this error", or "root cause analysis". Proactively invoke this skill (do NOT debug directly) when the user reports errors, 500 errors, stack traces, unexpected behavior, "it was working yesterday", or is troubleshooting why something stopped working. (gstack)

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Restrict file edits to a specific directory for the session. Blocks Edit and Write outside the allowed path. Use when debugging to prevent accidentally "fixing" unrelated code, or when you want to scope changes to one module. Use when asked to "freeze", "restrict edits", "only edit this folder", or "lock down edits". (gstack)

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Build generative UI apps with OpenUI and OpenUI Lang — the token-efficient open standard for LLM-generated interfaces. Use when mentioning OpenUI, @openuidev, generative UI, streaming UI from LLMs, component libraries for AI, or replacing json-render/A2UI. Covers scaffolding, defineComponent, system prompts, the Renderer, and debugging OpenUI Lang output.

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Full safety mode: destructive command warnings + directory-scoped edits. Combines /careful (warns before rm -rf, DROP TABLE, force-push, etc.) with /freeze (blocks edits outside a specified directory). Use for maximum safety when touching prod or debugging live systems. Use when asked to "guard mode", "full safety", "lock it down", or "maximum safety". (gstack)

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Triage AEM Workflow issues on AEM as a Cloud Service by classifying symptoms, gathering the right logs and metrics, and mapping to runbooks or Splunk searches. Use when the user asks for workflow activity/errors on a Cloud Service host, needs to classify a Jira ticket, or wants to know what to collect for workflow debugging.

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。

Debug AEM Workflow issues on AEM as a Cloud Service — stuck workflows, failed steps, missing Inbox tasks, launcher failures, stale instances, thread pool exhaustion, queue backlogs, purge failures, and permissions errors. Use when the user reports workflow problems on Cloud Service, asks why a workflow is stuck or failed, needs step-by-step troubleshooting, or provides thread dumps, configuration status output, or Sling Job console output for analysis.

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。

Initialize an Adobe App Builder project end-to-end without Developer Console UI clicks. Creates the Console project and workspace, subscribes APIs (including those needing a product profile), maps user intent to the right template, runs non-interactive `aio app init`, and guides post-init customization. Use whenever the user mentions creating an App Builder app, scaffolding a project, `aio app init`, setting up an Experience Cloud extension, adding actions or web assets, creating a Console project or workspace, adding APIs, or bootstrapping App Builder — even if they don't say "App Builder". Also for SPA templates, AEM extensions, API Mesh, Asset Compute workers, and MCP server projects. Also handles debugging init failures — template not found, `aio app init` hangs or times out, Node version mismatches, npm install failures, post-init build errors, `aio login` issues, `aio app run` showing nothing, or `aio console project create` / `workspace create` / `workspace api add` errors.

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。

Single entry point for all AEM as a Cloud Service Workflow skills. Covers workflow model design, custom process step and participant chooser development, launcher configuration, workflow triggering, and production support including debugging stuck/failed workflows, triaging incidents with Cloud Manager logs, thread pool analysis, and Sling Job diagnostics for the Granite Workflow Engine.

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。

iOS and Swift/SwiftUI development guidance, including App Clips, haptics, UI patterns, Liquid Glass styling, performance audits, view refactors, Swift concurrency, and iOS debugging. Use when building or reviewing iOS apps, SwiftUI views, or troubleshooting iOS issues.

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。

Guides concurrency and async decisions in Rust. Use when encountering Send/Sync errors (E0277), choosing between threads vs async, designing shared state, or debugging deadlocks.

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Deep reasoning mode for complex problems requiring maximum cognitive depth. Use when facing difficult architectural decisions, complex debugging, multi-step problem solving, or situations requiring careful analysis before implementation.

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。