「data flow」の検索結果

Interpret GA4 and GSC data with benchmarks, status indicators, and actionable insights

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。

Advanced content and topic research skill that analyzes trends across Google Analytics, Google Trends, Substack, Medium, Reddit, LinkedIn, X, blogs, podcasts, and YouTube to generate data-driven article outlines based on user intent 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。

OI-3 spike harness — heavy consumer skill, INLINE arm. Builds an incident post-mortem and sources the readability standard mid-workflow via an inline (non-forked) guidance skill. Use only when explicitly invoked by the spike harness with a TRIAL_ID and data path.

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。

OI-3 spike harness — heavy consumer, ADVERSARIAL arm. Worst-case early-exit test: the mid-workflow Skill call has no continuation guardrail and the guidance skill ends with a final-sounding anchor. Use only when explicitly invoked by the spike harness with a TRIAL_ID and data path.

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。

Performs deep architectural analysis of a specified module, directory, or feature area by examining structural coupling, data flow, concurrency patterns, risk, and SOLID alignment. Use when the user wants to assess, evaluate, or review the architecture, design quality, dependency structure, coupling, cohesion, or technical debt of an existing part of the codebase. Not for investigating specific bugs, runtime errors, or failures — use investigate. Not for test planning — use automated-test-planning. Not for file-level code review — use code-review. Not for researching open-ended options, prior art, or how something works — use research. Not for designing a new interface or contract — use design-an-api. Not for planning the change its findings imply — use plan-a-change. Not for discovering bounded contexts, ubiquitous language, or where code boundaries diverge from domain boundaries — use ddd-analysis. Not for writing documentation or architectural decision records.

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。

OI-3 spike harness — heavy consumer skill, BASELINE arm. Builds an incident post-mortem and sources the readability standard mid-workflow by reading a file directly, with NO Skill-tool call. Use only when explicitly invoked by the spike harness with a TRIAL_ID and data path.

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。

OI-3 spike harness — heavy consumer skill, FORKED arm. Builds an incident post-mortem and sources the readability standard mid-workflow via a forked (context fork) guidance skill. Use only when explicitly invoked by the spike harness with a TRIAL_ID and data path.

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。

Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.

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。

Fix macOS app hard crashes (EXC_CRASH / SIGKILL) caused by accessing TCC-protected APIs without the matching Info.plist usage description key. Use when: (1) App crashes immediately when touching Photos, Contacts, Calendar, Reminders, Location, Microphone, Camera, Screen Recording, etc., (2) Crash report shows `Exception: EXC_CRASH (SIGKILL)` with `Termination namespace: TCC`, (3) Crash stack includes `TCC.__TCC_CRASHING_DUE_TO_PRIVACY_VIOLATION__`, (4) Termination details contain "This app has crashed because it attempted to access privacy-sensitive data without a usage description", (5) Flutter macOS publish / gallery-save flow crashes on Gal.putVideo, PHPhotoLibrary, CNContactStore, etc. Critical distinction: macOS does NOT return a deny error — it SIGKILLs the process. This skill also covers the NSPhotoLibraryUsageDescription vs NSPhotoLibraryAddUsageDescription distinction that trips many apps.

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。

Extract language-agnostic pseudocode from formally verified programs (Isabelle/HOL, Coq) while preserving verified control flow, data dependencies, and algorithmic logic. Use when: (1) Users have verified code and need readable pseudocode, (2) Documenting verified algorithms for broader audiences, (3) Translating verified implementations to other languages, (4) Creating algorithm specifications from verified code, (5) Preserving verification guarantees in pseudocode form, or (6) Abstracting proof-heavy code to essential logic. Maintains semantic faithfulness to verified 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。

Translate Python programs to equivalent Lean4 code while preserving semantics and ensuring type safety. Use when users ask to convert, translate, or port Python code to Lean4, or when they need to verify Python algorithms using Lean4's theorem proving capabilities. Handles functions, classes, data structures, control flow, and ensures the generated Lean4 code is well-typed, executable, and can successfully run.

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。

Converts pseudocode descriptions and algorithm specifications into complete, executable Java code. Use this skill when you need to implement algorithms from pseudocode, translate algorithm descriptions to Java, generate Java code from specifications, convert textbook algorithms to working code, or create executable implementations from high-level descriptions. Preserves logic and control flow while handling Java idioms, data structures, and includes test cases for 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。

Performs abstract interpretation over source code to infer possible program states, variable ranges, and data properties without executing the program. Reports potential runtime errors including out-of-bounds accesses, null dereferences, type inconsistencies, division by zero, and integer overflows. Use when analyzing code for potential runtime errors, performing static analysis, checking safety properties, or verifying program behavior without 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。

Automatically instruments source code to collect runtime information such as function calls, branch decisions, variable values, and execution traces while preserving original program semantics. Use when users need to: (1) Add logging or tracing to code for debugging, (2) Collect runtime execution data for analysis, (3) Monitor function calls and control flow, (4) Track variable values during execution, (5) Generate execution traces for testing or profiling. Supports Python, Java, JavaScript, and C/C++ with configurable instrumentation 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。

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。

Write detailed feature specifications with functional requirements, edge cases, data models, API contracts, and UX flows. Create comprehensive technical specifications that enable clear 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。

Complete, populate and fill out 3-statement financial model templates (Income Statement, Balance Sheet, Cash Flow Statement) . Use when asked to fill out model templates, complete existing model frameworks, populate financial models with data, complete a partially filled IS/BS/CF framework, or link integrated financial statements within an existing template structure. Triggers include requests to fill in, complete, or populate a 3-statement model template

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。

Authoritative SwiftUI best practices from Apple: @Observable data flow, identity in ForEach/List, animations, environment values, and performance.

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。

Read a known webpage or execute a discovered workflow or data-provider capability. Use for page content or structured results once the URL or tool is selected.

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 financial projections, P&L statements, DCF models, and valuation analyses from assumptions and historical data. Use when the user requests financial modeling or provides relevant inputs for this workflow.

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。

Optimize a webpage for search engines by analyzing on-page elements, improving technical performance, and implementing structured data for maximum visibility. Use when the user requests seo optimization or provides relevant inputs for this workflow.

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。

Extract, validate, and categorize invoice data against purchase orders and GL codes. Use when the user requests invoice processing or provides relevant inputs for this workflow.

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。

Classify expenses by category, department, and tax deductibility from transaction data. Use when the user requests expense categorization or provides relevant inputs for this workflow.

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。

Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.

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。