「across」の検索結果

Explicit Identity Across Boundaries

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。

Create or update continuity ledger for state preservation across clears

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。

Runs a hypothesis-driven debugging loop across any language or binary, escalating to orthogonal oracle angles and locking the fix with a failing test. Use for crashes, silent failures, hangs, wrong responses, memory leaks, async misbehavior, or reverse engineering.

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。

Finds, reads, and reconstructs coding-agent sessions across Codex, Claude, OpenCode, OMO/Senpi, and other local agent logs. Use when asked to find or search past sessions, transcripts, or subagent runs, or to recover what an earlier session did.

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。

Searches and rewrites code by AST shape across 25 languages. Use when the target is a syntax pattern (every call/class/import shaped like X, a codemod, a YAML rule) rather than literal text; for plain strings, comments, or filenames, use rg.

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。

Runs rigorous visual QA across web, terminal, and paginated surfaces: an Apple HIG-based checklist, paired light/dark captures at phone and desktop widths, screenshot evidence, and a per-item PASS/FAIL verdict. Use for any UI build or change, or when asked whether a page, component, or TUI looks right or follows the platform's design guidelines.

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。

Self-improving browser automation via the auto-research loop. Iteratively runs a browsing task, reads the trace, and improves the navigation skill (strategy.md) until it reliably passes. Supports parallel runs across multiple tasks using sub-agents. Use when you want to build or improve browser automation skills for specific website 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。

Analyze an existing web application, identify safe user-visible capabilities across routes, forms, server actions, handlers, and schemas, then implement first-party WebMCP tools and validate discovery and invocation with Stagehand. Use when the user asks to make a codebase agent-ready, expose website features as WebMCP tools, or add WebMCP directly to an app rather than generating a standalone injection script from a URL.

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。

Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For broader biological evidence lookup across databases, use bio-database-evidence. Use this for direct HTTP/REST work or UniProt-specific control.

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。

Perform cross-artifact consistency analysis across spec.md, plan.md, and tasks.md. Use after task generation to identify gaps, duplications, and inconsistencies before implementation.

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 evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.

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 and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.

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。

Produce a prioritized performance-optimization roadmap across frontend, backend, and infrastructure. Use as an explicit/manual helper after bottlenecks are known or suspected, not as the owner of regression detection, profiling capture, or test execution.

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。

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

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。

Property-based testing with Hypothesis for discovering edge cases and validating invariants. Use when implementing comprehensive test coverage, testing complex logic with many inputs, or validating mathematical properties and invariants across input domains. Triggered by: hypothesis, property-based testing, @given, strategies, generative testing.

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。

Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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。

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

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。

Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).

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。

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, 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。

Score Hong Kong and China A-share dividend candidates across yield quality, durability, cash flow, valuation and momentum. Use when the user asks to dividend strategy with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.

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。

Systematic debugging with persistent state across context resets

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。

Send and read messages across all channels. Routes based on arguments — whatsapp, email, slack, telegram, discord, notion, or natural language like "send [msg] to [contact]".

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。

Deploy status across all projects. Shows ECS service versions, Vercel deployments, recent deploys, pending deploys, and CI/CD pipeline 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。

Go-to-market strategy planner. Generates a complete GTM plan across paid, unpaid, marketing, sales, and AI-automation channels for any project — and hands executable campaigns off to /marketing.

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。