「value」の検索結果

Value insurance agencies. TRIGGERS - Use when user needs help with insurance-agency-valuation related 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。

Validate data quality in CSV/Excel files for vehicle insurance platform. Use when checking required fields, validating data formats, detecting quality issues, or generating quality reports. Mentions "validate", "check fields", "data quality", "missing values", or "quality score".

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。

Clean and standardize vehicle insurance CSV/Excel data. Use when handling missing values, fixing data formats, removing duplicates, or standardizing fields. Mentions "clean data", "handle nulls", "standardize", "duplicates", or "normalize".

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。

Database of insurance claim precedents and comparable settlements for evaluating claim values

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。

Audits Solidity liquidation mechanisms for denial of service vulnerabilities including unbounded loops over positions causing out-of-gas reverts, data structure corruption preventing liquidation, front-running to block liquidation via nonce changes or self-liquidation, pending withdrawals forcing reverts, malicious ERC721/ERC20 callback reverts, collateral in external vaults not seized, insufficient insurance fund blocking liquidation, fixed bonus exceeding available collateral, incorrect decimal handling, conflicting nonReentrant modifiers, zero value transfer reverts, token deny list issues, and single borrower edge cases

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 life insurance product structures with cash value analysis, cost comparisons, and suitability assessment. Use when analyzing life products, comparing insurance costs, or assessing product suitability.

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。

Inspect, schema, and fill true AcroForm PDFs using sciClaw's pdf_form_inspect, pdf_form_schema, and pdf_form_fill tools. Use when: (1) checking whether a PDF is a real fillable AcroForm, (2) listing form field names, types, and choices, (3) filling a supported form from structured values, (4) preparing reviewable insurance, legal, administrative, or clinical form drafts, (5) reporting skipped fields or unused input keys after a fill. Do not use for scanned PDFs, OCR tasks, or XFA-only forms.

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 health insurance plan structures with actuarial value, network analysis, and cost projection. Use when analyzing health plans, comparing coverage, or projecting healthcare costs.

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 customer onboarding — map the activation sequence, identify drop-off points, design the aha moment, and produce the onboarding email sequence. Use when asked to "fix onboarding", "improve activation", "time-to-value is too slow", or "customers aren't getting started".

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 pricing strategy and packaging — tiers, value metrics, enterprise pricing, freemium design, and pricing page copy. Use when asked to "design our pricing", "should we change our price", "how do we package the product", or "what should we charge enterprise".

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 guide for Domain-Driven Design (DDD). Covers tactical patterns (Aggregates, Value Objects), strategic patterns (Bounded Contexts), event storming, and CQRS / Panduan ahli Desain Berbasis Domain (DDD).

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。

Generates Domain Entities following DDD principles with factory methods, private setters, domain events, and proper encapsulation. Supports aggregate roots, child entities, and value objects.

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。

Filter rows of a Datagrok DataFrame via the datagrok_exec tool through the Filters panel — by range, equals/contains/in-set, multi-value, boolean, free-text row expressions, or substructure (SMILES / SMARTS / molblock). Also covers clearing, inverting, the show-only-filtered vs destructive-drop split, and the filter event lifecycle (onRowsFiltering / onFilterChanged / onRowsFiltered). Use whenever the user says "filter", "show only", "hide rows where", "narrow to subset", "find rows that", "contains", "substructure search", "categorical filter", "range filter", "invert", "clear the filter", "clear filters", "drop rows", or asks for the filtered subset as a new table. Does NOT cover selection (separate skill) or generic DataFrame cloning (datagrok-df-and-columns).

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, describe, add, remove, rename, clone, or set metadata on columns of a Datagrok DataFrame via the datagrok_exec tool. Use whenever the user asks to locate "the X column", summarize a column, add a typed/empty/values-filled/virtual column, set semantic type / units / format / friendly name, apply linear or categorical or conditional color coding, drop or rename columns, or copy a DataFrame. Covers everything in DataFrame.columns and Column.meta — but not row filtering/selection (datagrok-filtering, datagrok-selection) and not formula-only columns (datagrok-calc-column).

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。

Insert schedule rows into the local database (schedules) from a JSON array. Use when bulk-loading schedule entries with prompt_name, input_value, and scheduled_date via scripts/add_schedules.py.

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 a Pavlok stimulus via the Pavlok API using scripts/pavlok.py. Use when you need to trigger vibe/beep/zap with a numeric value in this repo and print the API 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。

rem, px or relative units for each kind of value. Use when defining spacing and sizing tokens, control heights or media queries, or when a layout breaks under enlarged text.

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。

The discipline of keeping a field notebook — words, pictures, numbers, metadata, and questions captured during sustained outdoor observation. Covers the Laws notebook method, the sit-spot practice, phenology recording, sketch-first-name-later discipline, and the long-term research value of accumulated journals. Use when the task is to teach or structure ongoing field observation rather than answer a one-shot identification.

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。

Conjecture generation, sequence analysis, and experimental mathematics for mathematical discovery. Covers sequence analysis (OEIS lookups, recurrence relations, generating functions), pattern detection methods (finite differences, ratio analysis, modular patterns), conjecture generation workflow, classical integer sequences (Fibonacci, Catalan, partition numbers, Bernoulli numbers), combinatorial identities, the probabilistic method (Erdos, expected value arguments, Lovasz Local Lemma), AI-assisted discovery (FunSearch, Ramanujan Machine), and confidence calibration with cautionary tales (Mertens conjecture, Polya conjecture). Use when discovering patterns, analyzing sequences, generating conjectures, or working with combinatorial identities.

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。

Understanding and integrating multiple perspectives in historical analysis. Covers historical empathy (reconstructing how past actors understood their world), analyzing events from multiple viewpoints, constructing inclusive narratives that recover marginalized voices, and avoiding presentism (the imposition of contemporary values on past societies). Use when interpreting historical actors' motivations, assessing whose stories are told and whose are silenced, or guarding against anachronistic judgment.

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 analysis of combinational and sequential digital circuits using Boolean algebra, truth tables, K-maps, CMOS gate implementation, flip-flops, registers, counters, and finite state machines. Covers De Morgan's theorems, canonical sum-of-products and product-of-sums forms, minimization techniques, propagation delay, setup and hold times, metastability, synchronous design discipline, and FSM encoding. Use when designing, minimizing, verifying, or debugging any circuit whose signals take only discrete logic values.

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。

Core programming concepts from variables through recursion. Covers data types (integers, floats, strings, booleans, arrays, objects), variables and scope (lexical, dynamic, block, function, global), control flow (conditionals, loops, pattern matching), functions (parameters, return values, closures, higher-order functions), recursion (base cases, call stack, tail recursion, mutual recursion), type systems (static vs dynamic, strong vs weak, type inference, generics), and error handling (exceptions, Result types, defensive programming). Use when teaching, reviewing, or diagnosing issues with fundamental programming constructs.

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。

Structured approaches to decisions under uncertainty and complexity. Covers expected value, decision trees, multi-criteria decision analysis, System 1 vs System 2 allocation, pre-mortems, reversible vs irreversible decisions, and the distinction between good decisions and good outcomes. Use when choosing among alternatives with uncertain or multi-dimensional consequences, especially when the stakes justify a deliberate rather than intuitive process.

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。

Operations management and lean production techniques for turning inputs into outputs under variability. Covers the Toyota Production System, seven wastes, just-in-time, kanban, jidoka, single-minute die exchange, 5 Whys, value stream mapping, bottleneck theory, and the distinction between mass production and flexible production. Use when diagnosing operational waste, designing production flows, setting up quality systems, or comparing batch-and-queue to one-piece flow.

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。