「ema」の検索結果

Extract reusable Jacobian capabilities from a mathematical solution corpus, rather than one agent trajectory.

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。

Audit a bounded mathematical slice for missing or unusable Jacobian capabilities, beyond a single-operation review.

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。

Plans, creates, and optimizes digital marketing campaigns including content strategy, social media, email marketing, SEO, and AI visibility (GEO). Generates UTM parameters and tracking URLs. Helps develop go-to-market strategies, campaign messaging, content calendars, and performance measurement frameworks. Use when asked to "create a marketing campaign", "plan content strategy", "build email sequences", "set up UTM tracking", "analyze marketing metrics", "launch a product", "optimize for SEO", "improve AI visibility", "optimize for voice search", "set up IndexNow", "check accessibility for SEO", or "create social media previews". Supports both marketing beginners and experienced marketers needing templates.

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。

Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.

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。

Abstract - Email Verification API integration. Manage data, records, and automate workflows. Use when the user wants to interact with Abstract - Email Verification API 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。

Optimize slow PostgreSQL inserts in Python using psycopg2. Use when: (1) Row-by-row inserts are taking too long over network, (2) executemany() isn't providing speedup, (3) Migrating large datasets to PostgreSQL, (4) Network latency making individual INSERT statements impractical. The key is using execute_values() from psycopg2.extras instead of executemany() or individual execute() calls.

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 a codebase to a new language version, framework version, or library update while ensuring all tests still pass. Use this skill when migrating Python 2→3, upgrading framework versions (React, Django, Angular), updating dependencies, or performing any version migration where tests define correct behavior. The skill analyzes failing tests caused by migration, categorizes errors (import errors, API changes, type errors, behavior changes), proposes systematic fixes, and verifies that test-observable behavior remains unchanged. Triggers when users ask to migrate code versions, upgrade dependencies while keeping tests passing, update framework versions, or perform test-guided version migrations.

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 abstract mathematical models from functional code (Haskell, OCaml, F#) for formal reasoning in Isabelle/HOL. Use when users need to: (1) Convert functional programs to Isabelle definitions, (2) Extract high-level algorithm essence from implementation code, (3) Generate formal specifications and properties from code, (4) Create verification-ready models that capture mathematical properties while abstracting away implementation details. Focuses on structural recursion, algebraic data types, higher-order functions, and invariant extraction.

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。

Detect semantic-level bugs by analyzing whether code behavior matches its intended purpose inferred from function/variable names, comments, docstrings, and documentation. Use when users need to: (1) Find logic errors where implementation contradicts stated intent, (2) Identify off-by-one errors and boundary mismatches, (3) Detect inverted logic or wrong operators, (4) Catch missing edge case handling, (5) Verify code matches its documentation. Highlights mismatches between intent and implementation across multiple programming languages.

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 formal specifications (definitions, predicates, invariants, pre/post-conditions) in Isabelle/HOL or Coq from informal requirements, source code, pseudocode, or mathematical descriptions. Use when users need to: (1) Formalize algorithms or data structures, (2) Create function specifications with contracts, (3) Generate predicates and properties for verification, (4) Translate informal requirements into formal logic, (5) Specify invariants for loops or data structures, or (6) Create formal definitions for mathematical concepts. Supports both Isabelle/HOL and Coq equally.

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 abstract mathematical models from imperative code (C, C++, Python, Java, etc.) suitable for formal reasoning in Coq. Use when the user asks to model imperative code in Coq, create Coq specifications from imperative programs, extract mathematical models for verification, or translate imperative algorithms to Coq for formal reasoning and proof.

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 complete partial code snippets while satisfying semantic constraints including variable types, invariants, pre/post-conditions, interface contracts, and expected input/output behavior. Use when users provide incomplete code with specific requirements like "complete this function that takes a list and returns sorted unique elements" or "fill in this method body that must maintain the invariant that x stays positive" or "implement this interface method with these type constraints." Produces compilable, executable code with tests and a constraint satisfaction report.

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。

Instant lead enrichment. Drop a name, company, LinkedIn URL, or email and get the full contact card with email, phone, title, company intel, and next actions.

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 literature review across multiple arXiv papers.

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。

产品发布邮件, 含 masthead、hero、CTA、规格表, table-fallback

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。

Token architecture, component specifications, and slide generation. Three-layer tokens (primitive→semantic→component), CSS variables, spacing/typography scales, component specs, strategic slide creation. Use for design tokens, systematic design, brand-compliant presentations.

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 writing, reviewing, debugging, or testing Pandera schemas and runtime validation for Polars DataFrame or LazyFrame pipelines installed with pandera[polars]. Trigger on pandera.polars DataFrameSchema, DataFrameModel, Column, Field, Check, PolarsData, decorators, coercion, strictness, lazy error collection, and validation-depth decisions. Do not use for pandas-backed Pandera, Pydantic object models, Polars transformations without Pandera, static dataframe typing alone, or generic data-quality platforms.

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。

Ultra-compressed code review comments. Cuts noise from PR feedback while preserving the actionable signal. Each comment is one line: location, problem, fix. Use when user says "review this PR", "code review", "review the diff", "/review", or invokes /caveman-review. Auto-triggers when reviewing pull 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。

A luxury-automotive brand whose marketing surfaces read as cinematic editorial. The base canvas is **near-black** (`#181818`) holding pure white display type; white-canvas bands appear only inside specific editorial contexts (preowned listings, pricing tables). The single brand voltage is **Rosso Corsa** (`#da291c`) — the iconic Ferrari racing red — used scarcely on primary CTAs, the Cavallino mark, and Formula 1 race-position highlights. Type runs **FerrariSans** at modest weights (display 500, body 400) — never bombastic. Spacing follows an explicit 8px token ladder (`xxxs` 4px through `super` 128px); generous editorial pacing throughout. The brand's strongest visual signature is the **full-bleed cinematic hero photograph** that fills the viewport top with car photography, model details, or trackside livery — followed by a tighter editorial body layout below.

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。

| Type-safe ORM for Cloudflare D1 databases using Drizzle. Use when: building D1 database schemas, writing type-safe SQL queries, managing migrations with Drizzle Kit, defining table relations, implementing prepared statements, using D1 batch API, or encountering D1_ERROR, transaction errors, foreign key constraint failures, or schema inference issues.

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 quality check for a completed rebuttal document before submission. Use after Stage 3 document compilation or after Stage 5 final remarks writing to verify completeness, tone, factual accuracy, and structural integrity. Catches common rebuttal errors before they reach reviewers.

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 validate model-facing tool definitions with clear names, action-oriented descriptions, bounded JSON Schema parameters, explicit side effects, safe defaults, idempotency, errors, and realistic tests. Use when creating function-calling tools, MCP tools, agent actions, structured tool inputs, or when a model selects the wrong tool, invents arguments, or causes unsafe side effects.

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。

Think and work like an expert Knowledge Representation Researcher. Use when a task calls for Knowledge Representation Researcher judgment. Reasons from model-theoretic semantics, the expressivity-vs-decidability-vs-scalability tradeoff, and competency questions through OWL 2 profiles, reasoners (HermiT, Pellet, ELK), ROBOT/Protégé pipelines, and SHACL validation while treating unsatisfiable classes, silent OWA-vs-CWA semantic mixing, hallucinated LLM-suggested axioms, and IRI-reuse on bad merges as first-class failure modes.

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。

Think and work like an expert Logician. Use when a task calls for Logician judgment. Reasons from syntax-versus-semantics and object-versus-metalanguage discipline, the ⊢/⊨ distinction, and matching each logic to its intended semantics through natural deduction and sequent calculus (cut-elimination, subformula property), compactness and Löwenheim–Skolem, forcing and diagonalization, and proof assistants (Lean 4/mathlib, Coq, Isabelle/HOL, Agda) with Z3/CVC5/Vampire/Mace4 automation while treating use–mention conflation, second-order misuse of compactness, mis-stated incompleteness hypotheses (ω-consistency vs consistency), and Gödel philosophical overreach as first-class failure modes.

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。