「chi」の検索結果

Top 20 GitHub Repositories for AI Agents in 2026 (Ranked by Stars) — 6. Apr. 2026 · Discover the 20 most popular open-source AI agent repositories on

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。

Role subprocesses for Dev/Tester/Reviewer/Architect tasks.

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。

Drive the user's desktop in the background — clicking, typing, scrolling, dragging — without stealing the cursor, keyboard focus, or switching virtual desktops / Spaces. Cross-platform: macOS, Windows, Linux. Works with any tool-capable model. Load this skill whenever the `computer_use` tool is available.

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 for pip CVE scanning and auto-patching via OSV.dev API.

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。

Sep 24, 2025 Deep Dive into Context Engineering for Agents Sep 18, 2025 Architectures for Multi-Agen

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。

In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for t

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。

With this cross-journal Collection, the editors at Nature Communications , Communications Psychology GAP: no mappable capability in latest signal (our

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。

Architecture Matters We often conflate different AI paradigms: LLMs (Transformers): Predict token se

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。

Artificial intelligence-driven transformative applications in disease ... — In cancer diagnostics, AI systems have achieved breakthrough performances

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。

Neuromorphic Computing - Fraunhofer Institute for … — Neuromorphic computing is a field of technology aimed at designing computer architectures that m

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。

MegaTrain Full Precision Training of 100B+ Parameter LLMs on a Single GPU A RAM-centric architecture that stores parameters in host memory and treats

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。

Hierarchical, Hybrid, and Omnimodal Models Jun 30 58 m Jun 30, 2026 58 m read Show 10 25 50 100 pe GAP: no mappable capability in latest signal (our t

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。

September 16, 2026 New ask Hacker News story: Open-sourced jev architecture last year with model,pap

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。

Agent Canvas Architecture - OpenHands Docs Documentation Index Fetch the complete documentation index at: /llms.

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 for Self-Rewriting Core: AST analyze, patch, commit.

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。

Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.

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。

Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG 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。

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

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。

Search and retrieve preprints from arXiv via the Atom API. Use this skill when searching for papers in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, or economics by keywords, authors, arXiv IDs, date ranges, or categories.

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。

Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API. Use when the user asks about non-coding variant effects, pathogenicity, clinical significance, disease associations, functional effects, gene expression changes, splicing disruption, or regulatory effects in promoters and enhancers. Also use for resolving biological terms to tissue/cell-type ontologies (UBERON/CL) or analyzing variants in chr:pos:ref>alt format.

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 for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.

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 Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

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。

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.

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 implementation plan by dispatching fresh subagent for each task, with code review between tasks

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。