57 件 ・ 関連度順
概要と使いどころ
Use when building GBA games with the BPCore Lua engine - entity, sprite and tilemap functions, SRAM save and load, link cable multiplayer protocol, camera and scrolling, or optimization patterns
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。
Adaptive learning for moflo agents via ReasoningBank: trajectory storage, verdict judgment, memory distillation, consolidation, and MMR retrieval. Use when building agents that should improve from experience across runs.
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。
Theatre.js アニメーションツールキットリファレンス。 @theatre/core / @theatre/studio / @theatre/r3f / @theatre/dataverse。 getProject, Sheet, Sequence, Object, val, onChange, prop-types（number / rgba / compound / image）。 studio initialize / extend / transaction / scrub / createPane。 SheetProvider, editable, useCurrentSheet, useVal, usePrism, useAtom。 keyframe アニメーション、audio sync、state JSON、React Three Fiber 統合。
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
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 adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
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。
Wrap getTokenOptimizer().getCompactContext() to retrieve compacted ReasoningBank context for cost-analysis queries; report bridge-reported tokensSaved
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。
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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。
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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。
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
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。
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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。
Release and publish the dsh-web-ui monorepo (DSH Web GUI plugin family + skin collection) — bump all packages to one unified version, commit and tag, push the vX.Y.Z tag that triggers the GitHub Actions publish pipeline, and verify the npm publish + GitHub Release. Covers post-release verification and bad-version recovery. Use when the user asks to 发布/发版/release/bump 版本/publish a new version of dsh-web-ui or any @linxin666/dsh-* package.
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 a new skin for the dsh-web-ui skin collection (DSH Web GUI) and publish it into the skin-center plugin — scaffold with scripts/dsh-skin-new, author skin.json plus the apply/dispose + scoped-CSS contract, build and test with the official standalone bundle standard (turtle-ui shape), regenerate the skin-center registry and gallery, and submit the PR. Use when the user asks to create, add, develop, scaffold, or publish a new skin for the dsh web GUI skin collection.
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 you need to implement or improve Java logging and observability — including selecting SLF4J with Logback/Log4j2, applying proper log levels (ERROR, WARN, INFO, DEBUG, TRACE), parameterized logging, correlation context, secure logging without sensitive data exposure, environment-specific configuration, log aggregation, monitoring, and alerting. This should trigger for requests such as Improve logging; Apply logging; Refactor logging; Add logging support; Review SLF4J structured logging in Java code. Part of Plinth Toolkit
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。
Query the ZINC22 virtual compound library (CartBlanche API, billions of make-on-demand + purchasable molecules). Look up substances by ZINC ID, resolve a SMILES to its ZINC ID (exact match), inspect purchasability/catalogs, and assemble compound sets for docking. Property (MW/logP) filtering is done locally with RDKit. For bioactivity use chembl-database-bioactivity; for approved drugs use drugbank-database-access.
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。
Query openFDA REST API for adverse events (FAERS), labeling, product info, recalls, enforcement. Search by drug name, ingredient, MedDRA, or NDC. 1k req/day no key; 120k with free key. For trials use clinicaltrials-database-search; for structures use drugbank-database-access or chembl-database-bioactivity.
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。
Cross-reference compound IDs across 20+ databases (ChEMBL, DrugBank, PubChem, ChEBI, PDB, SureChEMBL, HMDB, DrugCentral, BindingDB) via UniChem REST API. Resolve InChIKeys to source IDs, translate between source-specific IDs, find structurally related compounds by connectivity. POST with a JSON body for all cross-reference queries; only /sources is GET. No auth required.
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。
Parse HMDB (Human Metabolome Database) local XML for metabolite info, chemical properties, biological context, disease links, spectra, and cross-DB mapping. No REST API — uses ~6 GB XML download. Use drugbank-database-access for drugs; pubchem-compound-search for live lookups.
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。
Complete reference for DTG Base module utilities and helpers. DTGBase is an abstract model providing common utility methods for date/time handling, barcode generation, timezone conversion, file operations, and more.
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。
Kontextspezifische Marketinghypothesen entwickeln, vorprüfen und als messbare Experimente priorisieren. Nutze diesen Skill, wenn Ideen für Kanäle, Kampagnen, Angebote oder Wachstum gesucht werden, wenn eine Ideenliste auf Risiko und Machbarkeit geprüft werden soll, oder wenn aus Vorschlägen Experimentsteckbriefe mit Metrik, Laufzeit, Budget und Stopkriterium werden sollen. Sortiert vor der Bewertung aus, was rechtlich, plattformseitig oder finanziell nicht tragbar ist, und trennt Evidenz von Vermutung. Führt keine Kampagne aus und schaltet keine Anzeigen.
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。
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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。
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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。
将一个或多个 Markdown 文档转换为格式优美的 Word（.docx），基于 Pandoc + 内置 reference.docx 模板（可选自定义模板），并确保不修改任何原始 Markdown 文件。内置模板已修复命名空间兼容性问题，支持 RGBA 图片自动转换。
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。
Turn a set of structures into absorption, distribution, metabolism, excretion, and toxicity estimates with ADMET-AI, and read them as a developability verdict rather than a table of numbers. Use this skill to run batch prediction over a library, interpret each endpoint against its DrugBank-approved percentile, and flag the liabilities that stop a series — hERG blockade, CYP inhibition, poor Caco-2 permeability, high clearance, and plasma protein binding. Also trigger on ADMET-AI, admet_ai, Chemprop-RDKit, hERG liability, CYP3A4 inhibition, Caco-2, bioavailability prediction, or developability triage.
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。
Systematically explore and test any software project (CLI, API, Backend, Library, etc.) to find bugs, usability issues, and edge cases. Produces a structured report with full reproduction evidence (exact commands, inputs, logs, and tracebacks) for every issue.
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。