「llm」の検索結果

Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges everything into the template that already contains Intro + Related Work from Step 3. TRIGGER when the orchestrator delegates Step 4 or when the user asks to "write the methodology and experiments sections" or "fill in the rest of the paper".

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。

Security architecture and threat modeling. OWASP Top 10 2025 analysis, OWASP Agentic AI Top 10 (ASI01-ASI10), AI/LLM security patterns, supply chain security, modern API authentication (OAuth 2.1, DPoP, Passkeys/WebAuthn), vulnerability assessment, and security review for code and infrastructure.

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。

Guide developers in creating Model Context Protocol (MCP) servers. Use for building MCP tools that enable LLMs to interact with external services. Covers TypeScript (primary) and Python FastMCP (secondary), tool annotations, Zod/Pydantic validation, and evaluation question creation.

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。

Hugging Face Hub operations, model inference, dataset management, PEFT/LoRA fine-tuning, and Spaces deployment via MCP tools and Python APIs

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。

AI-powered code vulnerability analysis and dependency scanning using Gemini CLI security extension patterns. Detects hardcoded secrets, injection attacks, weak cryptography, authentication flaws, and LLM prompt injection. Also scans dependencies against the OSV.dev vulnerability database.

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。

Background orchestrator that drains the system-wide cron-actions-queue.jsonl queue safely, preventing LLM context pollution in the primary router.

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 apps with the Claude API or Anthropic SDK. TRIGGER when: code imports `anthropic`/`@anthropic-ai/sdk`/`claude_agent_sdk`, or user asks to use Claude API, Anthropic SDKs, or Agent SDK. DO NOT TRIGGER when: code imports `openai`/other AI SDK, general programming, or ML/data-science 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。

Generate context-aware quality checklists for code review and QA using IEEE 1028 base standards plus LLM contextual additions

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。

AI and ML expert covering PyTorch, TensorFlow, Hugging Face, scikit-learn, LLM integration, RAG pipelines, MLOps, and production ML 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。

Interact with 80+ websites and desktop apps from the CLI via Chrome session reuse — zero LLM cost, deterministic JSON/YAML/CSV output. Covers Twitter/X, Reddit, YouTube, HN, Instagram, Spotify, Amazon, LinkedIn, and more; also automates browser actions and registers local CLI tools. Triggers on 'opencli', 'CLI for website', 'scrape without API', 'browser automation', 'operate command'.

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。

Intercept, inspect, and modify HTTP/HTTPS traffic via a Rust MITM proxy with Lua scripting hooks. Supports API debugging, LLM call capture for evals, endpoint mocking, CORS/auth header injection, latency simulation, and telemetry blocking. Triggers on 'proxy', 'intercept traffic', 'inspect requests', 'mock API', 'capture API calls', 'MITM', 'network debug', 'HTTP inspection'.

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。

Scrape web pages to clean markdown using Firecrawl v2 — handles JS-heavy pages, site crawls, URL mapping, document parsing (PDF/DOCX/XLSX), LLM-powered extraction, autonomous agent scraping, and post-scrape browser interaction (Interact API). Free keyless mode available for search/scrape/parse/interact (no API key needed). Prefer over WebFetch for quality and completeness. Triggers on scrape URL, fetch page, crawl site, extract content, parse document, web to markdown, DeepWiki, Firecrawl.

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Analyze a repository's type system and generate type-safe DAG execution pipelines with GraphSentry-style certificate verification. This skill should be used when building LLM-driven workflows that need deterministic type safety, when composing typed operations into validated execution graphs, or when adding contract-checked pipeline orchestration to any codebase. Supports Python (Pydantic) and TypeScript (Zod).

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。

Strip AI writing patterns from prose across five registers — casual, professional, academic, technical, and narrative. Applies 200+ pattern rules to eliminate predictable LLM tells. Triggers on clean up writing, remove AI slop, edit prose, make this sound less AI.

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Package codebases into LLM-optimized markdown, and run the Deep Think Authority pipeline (context-builder → Deep Think review → structured AUTHORITY → Flash/Antigravity implementation agent). Use when generating project context, preparing Deep Think reviews, or binding a weaker coding agent to a senior reasoning pass.

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 and score legitimate external authority and backlink opportunities as a dry-run register, using relevance, platform rules, risk, target-page fit, and explicit user approval gates. Use this skill when the user asks where links to a target site could be placed effectively, but never post, submit, DM, email, open PRs, or edit external accounts without separate explicit approval.

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。

Validate proposed or existing backlinks for relevance, editorial legitimacy, link attributes, spam/toxicity risk, platform compliance, anchor quality, target-page fit, and monitoring evidence. Use this skill when the user asks whether an external link opportunity or placed backlink is safe and useful, and reject toxic, irrelevant, paid-undisclosed, fake, or spam placements.

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。

Analyze and tune a running llama.cpp server for local coding agents: propose config (ctx, prompt cache, MTP), benchmark before/after, report deltas. Use to optimize llama-server for Claude Code or Aider. Don't use for vLLM, ollama, or non-llama.cpp.

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。

Apply WIT (Writing Is Thinking) as a human–LLM collaborative scientific reasoning skill for scientific question formulation, finding-driven research planning, next-experiment selection, Results or Discussion review, claim–evidence and reviewer stress tests, manuscript logic audits, deadline closure, and researcher growth. Use when the user invokes WIT or asks for these question-driven scientific reasoning workflows; do not use for generic copyediting, summarization, or literature search alone.

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Step-by-step guidance for DACH-specific governmental procedures including Anmeldung, Finanzamt registration, health insurance enrollment, and social security coordination. Use after arriving in a DACH country and needing to complete mandatory registrations, before a specific appointment to understand what to expect, when an initial registration attempt was rejected, when transitioning between DACH countries, or when handling registrations for dependents alongside your own.

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。

Install and configure the putior R package for workflow visualization. Covers CRAN and GitHub installation, optional dependencies (mcptools, ellmer, shiny, shinyAce, logger, plumber2), and verification of the complete annotation-to-diagram pipeline. Use when setting up putior for the first time, preparing a machine for workflow visualization tasks, when a downstream skill requires putior to be installed, or restoring an environment after an R version upgrade or renv wipe.

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。

Configure the putior MCP server to expose 16 workflow visualization tools to AI assistants. Covers Claude Code and Claude Desktop setup, dependency installation (mcptools, ellmer), tool verification, and optional ACP server configuration for agent-to-agent communication. Use when enabling AI assistants to annotate and visualize workflows interactively, setting up a new development environment with putior MCP integration, or configuring agent-to-agent communication via ACP for automated pipelines.

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 and build reactive, type-safe, production-grade backends on Convex. Covers schema, queries/mutations/actions, indexes, auth, file storage, scheduling, real-time multiplayer, mobile backends, and LLM/agent workflows on Convex's one-platform stack.

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。

arXiv論文URLからスライドを作成する。論文を取得・分析し、スライド構成JSONを保存、ユーザー確認後にgenerate_pptxツールでスライドを生成する。