「arr」の検索結果

Modern Angular UI patterns for loading states, error handling, and data display. Use when building UI components, handling async data, or managing component states.

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。

Master modern Angular state management with Signals, NgRx, and RxJS. Use when setting up global state, managing component stores, choosing between state solutions, or migrating from legacy 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。

Migrate from AngularJS to Angular using hybrid mode, incremental component rewriting, and dependency injection updates. Use when upgrading AngularJS applications, planning framework migrations, or ...

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。

Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.

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。

Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessar...

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。

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on re...

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。

Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing ...

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。

Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

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。

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector s...

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。

Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that solve specific problems with AI. Cov...

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 production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

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。

Zod 4 — TypeScript-first schema validation with static type inference. Use when writing Zod schemas, validating data, defining types with Zod, parsing input, creating form validation schemas, defining API request/response schemas, working with z.object, z.string, z.number, z.enum, z.array, z.union, z.discriminatedUnion, z.file, z.jwt, z.email, z.uuid, z.url, z.codec, z.toJSONSchema, z.fromJSONSchema, z.int, z.stringbool, z.templateLiteral, z.record, z.partialRecord, or any other Zod API. Also use when migrating from Zod 3 to Zod 4, or when the user's package.json shows zod@^4. CRITICAL: Always use Zod 4 APIs. Never use deprecated Zod 3 patterns unless user explicitly requests Zod 3 compatibility.

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。

Diagnose and fix WhatsApp connectivity issues for OpenClaw agents. Use when: a PA is not responding, WhatsApp shows connected but messages don't arrive, the agent is online but not replying, or troubleshooting a new agent setup.

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 working with Aws Batch — aWS Batch compute environment management, job queue analysis, scheduling policy review, and array job monitoring. Covers compute resource utilization, job status tracking, failure investigation, Fargate vs EC2 comparison, and queue priority 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。

Ctrl k GitBook Assistant Ask License GitHub More Home AutoGPT Platform Integrations Contribute AutoGPT Classic GitBook Assistant GitBook Assistant arr

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。

AiZynthFinder retrosynthetic route planning (CASP) from AstraZeneca Molecular AI. Monte Carlo tree search guided by a template-based neural expansion policy recursively disconnects a target SMILES until precursors are found in a purchasable stock. Covers config.yml (v4 format), aizynthcli batch screening, the AiZynthFinder/AiZynthExpander Python API, one-step disconnections, custom stocks via smiles2stock, scorers, Retro*/breadth-first/DFPN search alternatives, and reading output.json.gz / trees.json. Use for synthesis route planning, synthesizability screening, and building-block/precursor search. For reaction barriers use neb-irc-activation-energy; for 2D reaction scheme drawing use rdkit-chemdraw-cdxml.

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。

Annotated matrices for single-cell genomics. Stores X with obs/var metadata, layers, embeddings (obsm/varm), graphs (obsp/varp), uns. Use for .h5ad/.zarr I/O, concatenation, scverse integration. For analysis use scanpy; for probabilistic models use scvi-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。

Route Tavily web-search, extraction, mapping, crawling, and cited-research requests to the narrowest `tvly` command. Use for command-line Tavily work, installation checks, authentication setup, or choosing among the specialized Tavily 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。

Write and debug spreadsheet formulas (Excel/Google Sheets), pivot tables, and array formulas; translate between dialects; use when users need working formulas with examples and edge-case checks.

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。

MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".

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。

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

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。

You are a code education expert specializing in explaining complex code through clear narratives, visual diagrams, and step-by-step breakdowns. Transform difficult concepts into understandable explanations for developers at all levels.

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。

Codified expertise for returns authorisation, receipt and inspection, disposition decisions, refund processing, fraud detection, and warranty claims management.

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。