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概要と使いどころ
Designs and tests controls over financial reporting — segregation of duties, approval limits, evidence, and preparing for audit. Use this to design controls for a process, prepare for an external audit, respond to an audit finding, set approval thresholds, or assess where a small team's segregation of duties is genuinely broken.
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Establishes what something actually costs — fixed, variable and mixed cost behavior, absorption versus variable costing, job-order, process and activity-based methods, breakeven and operating leverage, and price/volume/mix decomposition. Use this to set a price floor, explain why revenue or margin moved, decide make versus buy, judge whether a product line earns its place, or work out how an allocation basis is distorting a reported margin.
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。
Pressure-tests a decision, plan, or idea before it is committed to — surfacing the assumption it rests on, the case against it, and what would have to be true for it to work. Use this when weighing options, when a plan needs challenging before commitment, when you have already decided and want a genuine gut check, when processing meeting notes into decisions, or when competing initiatives need ranking. Distinct from `chief-executive`, which owns direction; this one interrogates a specific decision.
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。
Raises capital and manages the relationship afterward — deciding how much and why, understanding what dilution and preferences actually cost, running a process with real competitive tension, preparing for diligence before it starts, and reporting to investors and a board in a way that keeps support when results are bad. Use this to plan a raise, evaluate a term sheet beyond the valuation, prepare a data room, or fix reporting that is producing surprises.
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。
Execute shell commands for system operations, directory creation, and script execution. When you need to run system commands, execute scripts, install packages, or manage processes. Note - if commands fail consecutively, try different approaches.
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。
Execute a skill's script file with arguments. When you need to run skill functionality, execute specific operations, or process data with 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。
Get local file path of voice message sent by user. When user sends voice message, system auto-downloads it. When you need to process user's voice message or transcribe voice to text.
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。
Get local file path of image sent by user. When user sends image, system auto-downloads it. When you need to process user's image or analyze image content.
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。
使用Nutrient DWS API处理、转换、OCR识别、提取、编辑、签名和填写文档。支持PDF、DOCX、XLSX、PPTX、HTML和图像格式。
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。
使用 Nutrient DWS API 进行文档处理、转换、OCR、提取、脱敏、签名以及表单填充。支持 PDF、DOCX、XLSX、PPTX、HTML 和图像格式。
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。
使用 Nutrient DWS API 处理、转换、OCR、提取、脱敏、签名和填充文档。支持 PDF、DOCX、XLSX、PPTX、HTML 和图像。
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。
Builds robust Python MCP (Model Context Protocol) servers with FastMCP — tool design, error contracts, event-loop-safe blocking work, subprocess/CLI wrapping, single-file vs packaged distribution, global-state-free testing, and prompt-injection awareness. Use when writing an MCP server, exposing a tool or CLI to an LLM client, debugging tool registration/packaging, or testing MCP tools.
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。
A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML, reference sequence verification, or fragment processing.
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。
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, process FASTQ/BAM/VCF, for developing and executing genomics pipelines.
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。
Transform, clean, reshape, and preprocess data using pandas and numpy. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
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。
Process, clean, and compare mass spectrometry (MS/MS) spectra with Matchms; use when you need reproducible spectral filtering and similarity scoring for metabolomics workflows.
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Process CRISPR screening data to identify essential genes and hit candidates. Performs quality control, statistical analysis (RRA), and hit calling for pooled CRISPR screens including viability screens and drug resistance/sensitivity studies.
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Parse Flow Cytometry Standard (FCS) files v2.0–3.1 and extract events/metadata for preprocessing workflows (e.g., when you need NumPy arrays, channel info, or CSV/DataFrame export from cytometry files).
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。
Sequence alignment and alignment file processing with Biopython (Bio.Align/Bio.AlignIO), triggered when you need global/local pairwise alignment, MSA read/write/format conversion, or alignment statistics/filtering.
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。
Use Biopython to read/write/convert biological sequence files (FASTA/GenBank/FASTQ, etc.) and perform basic sequence operations; use when you need reliable sequence I/O, lightweight sequence manipulation, or scalable processing of large sequence datasets.
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Generates complete phenotype-scoring bioinformatics research designs for any disease context and any user-defined phenotype, pathway, process, signature, or molecular program. Use when a study centers on gene-set or feature-set definition, intersection with DEGs or candidate features, phenotype scoring, feature selection, diagnostic or stratification assessment, immune or cellular-resolution interpretation, network analysis, and optional orthogonal validation. Covers five study patterns (signature discovery, phenotype scoring, feature selection, immune/cellular interpretation, multi-layer validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
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。
Reverse-engineers the methods section of a biomedical paper into a structured, reproducible workflow. Use this skill when a user wants to understand how a study was actually executed, extract data sources, inclusion/exclusion logic, preprocessing, analytical sequence, software/tools, validation path, and critical parameters, or build a replication checklist from a paper, abstract, DOI, PMID, title, screenshot, or partial methods text. Do not treat this as generic summarization. Focus on reconstructing the operational method pipeline, surfacing missing reproducibility details, and distinguishing explicitly reported steps from inferred or unresolved ones. Never fabricate references, methods details, identifiers, software versions, parameters, datasets, or validation steps.
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Use when you need a standardized R CLI workflow to train a two-class random forest model from an expression-like feature matrix, rank variable importance, and generate reproducible error and importance plots. NOT for regression tasks, multi-class classification, missing-value imputation, preprocessing, or remote data fetching.
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。
Use when normalizing bulk gene or protein expression matrices with log2 transform, z-score standardization, or min-max scaling before downstream visualization or exploratory analysis. NOT for count-model normalization such as TPM/DESeq2 size factors, batch correction, or single-cell preprocessing.
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。