223 件 ・ 関連度順
概要と使いどころ
当用户被别人的期望压垮、容易愤怒、或感觉被工作/责任困住、问「怎么说不/怎么不生气/怎么更自由」时调用。 核心理念: 期望不是你的合同(约定才是); 愤怒是自我惩罚; 就业自由来自低生活成本; 自由=免于被迫反应的自由。 不适用于: 违约/欠债等真正的约定责任。 Triggers: 期望/愤怒/自由/取悦别人/说不/就业自由/freedom/anger/expectations
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。
Patent research and IP landscape: prior-art search, claim mapping, landscape analysis, and freedom-to-operate. Use when conducting a prior-art search, mapping an IP landscape, evaluating patentability, or running an FTO 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。
Applies the existentialist and feminist philosophy of Simone de Beauvoir, French philosopher and author of The Second Sex. Reach for this skill whenever the user is grappling with existential dilemmas, moral ambiguity, gender dynamics, societal expectations, or relationship ethics. Trigger this skill for questions about freedom versus responsibility, bodily autonomy, marginalization, or when the user feels trapped by external labels or "immanence." Use this to help the user reclaim their agency, recognize the social construction of their situation, and make authentic choices that expand their transcendence while respecting the interconnected freedom of others.
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。
Map the intellectual property landscape for a technology domain or product area. Covers patent cluster analysis, white space identification, competitor IP portfolio assessment, freedom-to-operate preliminary screening, and strategic IP positioning recommendations. Use before starting R&D in a new technology area, when evaluating market entry against incumbents with strong patent portfolios, preparing for investment due diligence, informing a patent filing strategy, or assessing freedom-to-operate risk for a new product.
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。
Find out whether a chemical series is already claimed, using SureChEMBL's patent-extracted compound corpus and, where a key is available, PatentsView for legal status and assignee history. Use this skill to trace a structure to the patent documents that disclose it, survey an assignee's filings around a target, and understand what the freedom-to-operate question requires that a structure search cannot answer. Also trigger on SureChEMBL, patent chemistry, Markush structure, freedom to operate, composition of matter, assignee, priority date, patent family, or PatentsView.
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。
Elite frontend image-direction skill for generating premium, conversion-aware website design references. CRITICAL OUTPUT RULE — generate ONE separate horizontal image FOR EVERY section. A landing page with 8 sections produces 8 images. Never compress multiple sections into one image. Enforces composition variety (not always left-text / right-image), background-image freedom, varied CTAs, varied hero scales (giant / mid / mini minimalist), narrative concept spine, second-read moments, and a single consistent palette across all images. Optimized for landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.
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。
Elite frontend image-direction skill for generating premium, conversion-aware website design references. CRITICAL OUTPUT RULE — generate ONE separate horizontal image FOR EVERY section. A landing page with 8 sections produces 8 images. Never compress multiple sections into one image. Enforces composition variety (not always left-text / right-image), background-image freedom, varied CTAs, varied hero scales (giant / mid / mini minimalist), narrative concept spine, second-read moments, and a single consistent palette across all images. Optimized for landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.
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。
Edit any video by conversation. Transcribe, cut, color grade, generate overlay animations, burn subtitles — for talking heads, montages, tutorials, travel, interviews. No presets, no menus. Ask questions, confirm the plan, execute, iterate, persist. Production-correctness rules are hard; everything else is artistic freedom.
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。
当用户纠结时间分配、问「这个琐事/外包值不值」「怎么安排时间」「离财务自由还差什么」时调用。 核心理念: 给时间定一个高得离谱的时薪, 低于时薪的事外包或不做; 退休=不为想象中的明天牺牲今天; 不随收入升级生活方式。 不适用于: 需要享受慢节奏的时刻（休息/家庭时间按定义不是浪费）。 Triggers: 时薪/时间管理/外包/琐事/排队/财务自由/退休/hourly rate/delegate
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。
Freedom-to-operate triage — a structured first look at potentially blocking patents, not an FTO opinion. Use when a product, process, or feature is being evaluated for blocking patents, when asked whether anything stops a launch, or to build a claim-chart first pass against the most plausible patents before patent counsel review. This skill never concludes a product is clear to launch.
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。
Read biological sequence files (FASTA, FASTQ, GenBank, EMBL, ABI, SFF) using Biopython Bio.SeqIO. Use when parsing sequence files, iterating multi-sequence files, random access to large files, or high-performance parsing.
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。
Gene Ontology over-representation analysis using clusterProfiler enrichGO. Use when identifying biological functions enriched in a gene list from differential expression or other analyses. Supports all three ontologies (BP, MF, CC), multiple ID types, and customizable statistical thresholds.
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。
Meta-agent that routes bioinformatics requests to specialised sub-skills. Handles file type detection, analysis planning, report generation, and reproducibility export.
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。
Reactome pathway enrichment using ReactomePA package. Use when analyzing gene lists against Reactome's curated peer-reviewed pathway database. Performs over-representation analysis and GSEA with visualization and pathway hierarchy exploration.
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。
Detect and quantify translated ORFs from Ribo-seq data including uORFs and novel ORFs using RiboCode and ORFquant. Use when identifying translated regions beyond annotated coding sequences or quantifying ORF-level translation.
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。
Generate and interpret quality reports from FASTQ files using FastQC and MultiQC. Assess per-base quality, adapter content, GC bias, duplication levels, and overrepresented sequences. Use when performing initial QC on raw sequencing data or validating preprocessing results.
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。
Data-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools. Use when analyzing DIA mass spectrometry data with library-free or library-based workflows for deep proteome profiling.
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。
Extract, process, and deduplicate reads using Unique Molecular Identifiers (UMIs) with umi_tools. Use when library prep includes UMIs and accurate molecule counting is needed, such as in single-cell RNA-seq, low-input RNA-seq, or targeted sequencing to distinguish PCR from biological duplicates.
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。
Protein grouping and inference from peptide identifications. Use when resolving protein ambiguity from shared peptides. Handles protein groups and protein-level FDR control using parsimony and probabilistic 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。
Load and parse mass spectrometry data formats including mzML, mzXML, and quantification tool outputs like MaxQuant proteinGroups.txt. Use when starting a proteomics analysis with raw or processed MS data. Handles contaminant filtering and missing value assessment.
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。
Quality control and assessment for proteomics data. Use when evaluating proteomics data quality before downstream analysis. Covers sample metrics, missing value patterns, replicate correlation, batch effects, and intensity distributions.
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 molecular descriptors and fingerprints using RDKit. Computes Morgan fingerprints (ECFP), MACCS keys, Lipinski properties, QED drug-likeness, TPSA, and 3D conformer descriptors. Use when featurizing molecules for machine learning or filtering by drug-likeness criteria.
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。
Build, manage, and search spectral libraries for proteomics. Use when creating or working with spectral libraries for DIA analysis. Covers DDA-based library generation, predicted libraries (Prosit, DeepLC), and library formats.
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。
Filter reads by quality scores, length, and N content using Trimmomatic and fastp. Apply sliding window trimming, remove low-quality bases from read ends, and discard reads below thresholds. Use when reads have poor quality tails or require minimum quality for downstream 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。