8,604 件 ・ 関連度順
概要と使いどころ
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
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。
Maps metabolomics results to biological pathways via over-representation (ORA), metabolite-set enrichment (MSEA/QEA), mummichog/PSEA on raw m/z peaks, and network-diffusion enrichment (FELLA), with correct background-set construction and honest interpretive ceilings. Use when interpreting differential metabolites or an untargeted LC-MS feature table in pathway context, choosing ORA vs MSEA vs mummichog vs topology, or setting the reference/background set. For annotation confidence levels feeding ORA see metabolomics/metabolite-annotation; for gene-set concepts see pathway-analysis/go-enrichment and pathway-analysis/gsea; for joint gene+metabolite pathways see multi-omics-integration/mofa-integration.
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。
Experimental data: discover and acquire raw or near-raw measurements, sample-level observations, and reusable datasets from sequencing, transcriptomics, single-cell, perturbation, proteomics, metabolomics, genomics, and structural-biology repositories. Use for study or sample metadata, matrices, reads, spectra, experimental structures, and downloadable research artifacts—not curated database assertions or reference marker collections.
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 to process automated PR review feedback (Copilot, Greptile, Codex) and iterate until all AI threads are addressed, checks are green, and the PR is merge-ready. Trigger when the user says "address review feedback", "recycle reviews", or references an automated reviewer.
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 errors, incidents, or product ideas using both internet research and repo-wide codebase research, then propose a concrete changeset plan with clarifying questions before any implementation. Use when a user asks to investigate, root-cause, or plan changes, or when an Issue or PR is referenced and needs a plan.
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。
Querying Stellar chain data via Stellar RPC (preferred) and Horizon (legacy). Covers RPC JSON-RPC methods, Horizon REST endpoints, streaming, pagination, historical queries, Hubble/Galexie for deep history, and the RPC/Horizon migration story. Use when reading balances, transactions, operations, ledgers, contract events, or building any indexer/analytics workflow.
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。
Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team.
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。
Compile your local stellar-build usage traces into sharper skills. Use when the user says "optimize my skills", "optimize-skills", "improve my skills from usage", "learn from my traces", "stellar-loop optimize", or wants the learning loop to refine installed skills based on how they've actually been used. DSPy-style prompt optimization over the local trace store at ~/.stellar-build/traces — fully local, reversible.
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。
Surface current and recent Stellar Community Fund (SCF) round activity. Use when a user says "current SCF round", "what's happening with SCF", "active SCF submissions", "should I apply to SCF this round", "SCF deadline", "who is applying to SCF right now", "SCF round status", or "show me this round's submissions". Fetches live data from communityfund.stellar.org and cross-references with the LumenLoop ecosystem DB to flag repeat builders and category trends.
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。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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。
React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.
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 maps, map prototypes, grids, placements, entity references, and serialized compatibility safely.
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。
Resolve localized text at presentation boundaries using English-source keys, with Russian localization only when requested, and correct lifecycle refresh.
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。
Implement BUI contracts, server validation, localized client windows, lifecycle cleanup, and state refresh.
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。
Maintain entity references, ownership links, target relationships, deletion cleanup, and network-safe relation state.
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。
Check documentation references, sync docs to code changes, and detect translation drift. Use for documentation maintenance in a repository.
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。
Ralph - persistent self-referential execution loop wrapping ultrawork with independent verifier verification
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。
Auto-detect project tech stack and generate stack-specific references for domain skills
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。
When the user wants to create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' or wants to avoid repeating foundational information across marketing tasks. Creates `.claude/product-marketing-context.md` that other marketing skills reference.
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。
Restructure existing code while preserving observable behavior. Use for targeted technical debt or hotspot work with characterization tests.
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。
Resize a product photo to Shopify's recommended 2048×2048px square format, sharpen, and convert to WebP for fast storefront load times.
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 applicant details, employment history, income, and references from a rental application form into structured JSON for tenant screening.
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 carrier invoice, shipment references, rates, accessorials, taxes, and total charges from freight bills.
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 shipper, consignee, order references, carton lines, weights, and package totals from packing lists.
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。