427 件 ・ 関連度順
概要と使いどころ
Calculate code occupant loads by area with gross/net factors and jurisdiction checks. Use for "how many people can this space hold," IBC Table 1004.5, egress inputs, or occupancy-load reports; not for workplace headcount planning.
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。
Summarize, organize, and normalize messy research materials into an IEEE-ready writing brief before manuscript drafting. Use when Codex needs to process a folder or collection of experiment logs, code files, AI chat exports, idea notes, literature summaries, related-work notes, copied introduction excerpts, Zotero-derived paper summaries, tables, figures, or scattered Markdown/text records and convert them into structured inputs for ieee-writing, ieee-experiment, ieee-citation, ieee-reviewer, or ieee-response.
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。
Provides edge case, corner case, boundary condition, and limit testing patterns for Java unit tests. Validates minimum/maximum values, null cases, empty collections, numeric overflow/underflow, floating-point precision, and off-by-one scenarios using JUnit 5 and AssertJ. Use when writing .java test files to ensure code handles limits, corner cases, and special inputs correctly.
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 construction data inputs before processing: cost estimates, schedules, BIM data, field reports. Catch errors early with domain-specific rules.
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。
Automatically generate daily construction reports from field data, worker inputs, weather, and progress photos. Creates professional PDF reports.
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 CWICR data quality and estimate inputs. Check for errors, inconsistencies, outliers, and missing 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。
Pulls financial statement numbers for US public companies straight from SEC EDGAR's free official XBRL APIs (companyfacts, companyconcept, frames, submissions) into a cited table. Every value carries its accession number, form, fiscal period, filed date, XBRL tag and a link to the filing, and the tool handles fiscal-year ends, quarterly vs YTD vs TTM, derived Q4, restatements and units. Use this whenever the user wants 10-K numbers or 10-Q numbers, says "pull financials for", asks for public company revenue, net income, EPS, cash, debt or capex, wants comps or a competitor side-by-side, a credit check, prospect research, or model inputs, or mentions SEC filings or EDGAR, even if they never say XBRL. Free and primary-source, no paid data terminal needed. Do not use it for private companies, stock prices or valuation multiples.
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。
Deep-scan any tools you connect (CRM, email, support, reviews, analytics, billing, database) to produce a data-grounded Ideal Customer Profile and a reusable persona library. Read-only by default. Use when you need to define or refresh your ICP, build buyer personas from real data instead of guesses, or generate the persona inputs that the customer-panel-of-experts and prospect-panel-simulator skills consume.
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 starting or inheriting a radiology research project and need to know what it is. Classifies the project type, summarizes its current state, lists missing inputs, recommends next steps and scaffolds lightweight project memory files.
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 ClawBio for local-first bioinformatics agent workflows: install and run its skill library, route genomic or omics inputs, create reproducible reports, connect optional MCP or Nextflow integrations, and author or validate 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。
Use ColabFold for protein structure prediction workflows: validate FASTA/CSV/A3M inputs, generate MSAs, plan batch predictions, inspect outputs, and troubleshoot optional AlphaFold, MMseqs2, JAX, GPU, and OpenMM dependencies.
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。
Routes agents using or contributing to CrewAI, including crews, flows, CLI projects, tools, MCP, memory, RAG, LLM providers, observability, files, multimodal inputs, and repo development.
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Use AlphaFold 3 PyTorch for protein and biomolecular structure-prediction workflows, heterogeneous molecule inputs, PDB/mmCIF/MSA/template preparation, bounded model inference, training configuration, and local CLI or Gradio operation.
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。
Validates whether an API request is correct based on provided inputs (method, URL, headers, body, auth, query params).
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。
Implementation guide for secure multi-party computation enabling privacy-preserving analytics across organizations. Covers secret sharing, garbled circuits, reference frameworks MP-SPDZ and CrypTen, practical deployment patterns, and GDPR alignment for joint controller analytics without revealing individual party inputs.
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 implementing enhanced text inputs for AI prompts.
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。
Automatically reduces bug-triggering test cases to minimal form while preserving the failure. Use when debugging with large, complex test cases that need simplification, reproducing bugs with minimal examples, or creating regression tests from verbose failure scenarios. Takes a failing test and systematically removes unnecessary inputs, steps, assertions, and code using delta debugging and other reduction algorithms. Supports unit tests, integration tests, input files, and multiple programming languages (Python, JavaScript, Java, C/C++).
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。
Automatically updates regression tests based on interval analysis to maintain coverage of key program intervals. Use when code changes affect value ranges, conditionals, or control flow, and existing tests need updating to maintain interval coverage. Analyzes interval information from updated code, identifies coverage gaps, adjusts test inputs and assertions, removes redundant tests, and generates new tests for uncovered intervals. Supports Python, Java, JavaScript, and C/C++ with various test frameworks (pytest, JUnit, Jest, Google Test).
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。
Automatically identify potential boundary and exception cases from requirements, specifications, or existing code, and generate comprehensive test cases targeting boundary conditions, edge cases, and uncommon scenarios. Use this skill when analyzing programs, code repositories, functions, or APIs to discover and test corner cases, null handling, overflow conditions, empty inputs, concurrent access patterns, and other exceptional scenarios that are often missed in standard 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。
Generate test assertions from existing code implementation. Use when the user has implementation code without tests or incomplete test coverage, and needs assertions synthesized by analyzing the code's behavior, inputs, outputs, and state changes. Supports Python (pytest/unittest), Java (JUnit/AssertJ), and JavaScript/TypeScript (Jest/Chai). Handles equality checks, collections, exceptions, and state verification.
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。
Automatically generates executable test cases from model checking counterexample traces. Translates abstract counterexample states and transitions into concrete test inputs, execution steps, and assertions that reproduce property violations. Use when working with model checker outputs (SPIN, CBMC, NuSMV, TLA+, Java PathFinder, etc.) and needing to create regression tests, validate bug fixes, or reproduce verification failures in executable test suites.
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 vague or partially formed AI, computer-science, data-driven, and engineering-method research directions into evidence-grounded, falsifiable, paper-ready Research Idea Briefs through heuristic questioning, staged confirmation, literature positioning, novelty attack, and method-figure planning. Use when the user needs to clarify a research idea, formalize inputs/outputs/objectives/constraints, frame background and motivation, select 3-5 anchor papers, identify a defensible gap, derive 2-3 contributions, stress-test novelty, or design an input-to-output method framework and core-paper figure. Also trigger on requests such as 明确科研 idea、启发式梳理 idea、互动追问科研问题、梳理论文创新点、定义研究任务、找对标论文、把研究方向变成论文方案、画方法框架图. Do not use as the primary workflow for running experiments, exhaustive systematic reviews, drafting full manuscript sections, or polishing finished prose.
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。
TanStack AI (alpha) provider-agnostic type-safe chat with streaming for OpenAI, Anthropic, Gemini, Ollama. Use for chat APIs, React/Solid frontends with useChat/ChatClient, isomorphic tools, tool approval flows, agent loops, multimodal inputs, or troubleshooting streaming and tool definitions.
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。
Connect, verify, and troubleshoot Xquik's remote MCP server. Use when a user needs MCP setup, OAuth recovery, tool discovery, endpoint execution, or a connection check in ChatGPT, Claude, Codex, Cursor, VS Code, or another compatible client. Covers the endpoint, OAuth and the API key fallback, the docs, search, and execute tools with their inputs and outputs, errors, and the confirmation rules for private, metered, persistent, or state-changing calls. Not affiliated with X Corp.
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。