「constraint」の検索結果

Flip core assumptions to reveal hidden constraints and alternative approaches - "what if the opposite were true?"

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 the single constraint currently limiting a business's growth and the highest-leverage moves against it, rather than producing a list of everything that could be improved. Use this when growth has stalled or slowed, when deciding where to spend limited time or budget, when revenue rises but profit does not, when prioritizing growth initiatives, or when a business feels busy without compounding.

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。

Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like "make me a GIF of X doing Y for Slack."

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。

Constraint-based reconstruction and analysis (COBRA) for metabolic models; use when you need to simulate growth/production, analyze flux ranges, or run knockout and medium studies from SBML/JSON/YAML models.

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。

Adapt scientific abstracts to conference-specific word limits, section rules, and formatting constraints; use when reshaping an existing abstract for submission.

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。

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 2. Validate that the request matches the documented scope and stop early if the task would require unsupported as.

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。

Matches a user’s biomedical research direction, disease problem, study aim, data modality, and resource constraints to the most relevant recent algorithms and method papers. Always search real recent algorithm literature first, prioritize the last 12 months, expand to 1–3 years only when needed, and add canonical baselines only when necessary. Every formal algorithm recommendation must include the verified primary method paper, plus published downstream papers that actually cite/use the algorithm when such papers are found, with DOI when available. Never fabricate papers, algorithm names, authors, journals, years, DOI, PMID, links, or benchmark claims. If no directly verified algorithm paper is found, say so explicitly.

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。

Designs a realistic, execution-aware biomedical study version under explicit constraints of samples, time, budget, data access, lab capacity, team skill, and validation resources. Always use this skill when the user has a real study idea, a candidate route, or a partially framed project but cannot assume ideal conditions. If critical feasibility inputs are missing, first clarify what resources are currently available, what resources may be obtainable, and what resources are realistically unavailable. Do not invent access, capabilities, collaborations, or validation resources. Focus first on feasibility-constrained study framing, route narrowing, dependency control, and minimum viable study design.

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。

Assesses whether a medical research topic is worth starting now by separating true novelty from pseudo-novelty, auditing real feasibility under stated resource constraints, and forcing a concrete start / narrow / redesign / stop decision. Always require explicit assumptions and never fabricate references, datasets, resource availability, precedent studies, or publication claims.

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。

Spawn the comment-sicko subagent, fix accepted findings, and offer encodings for claimed constraints.

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 this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation 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。

Select optimal graph algorithm based on problem constraints

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 argument validation logic with type coercion, constraints, custom validators, and helpful error messages for CLI applications.

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。

Fetch and parse LeetCode problems with metadata, constraints, examples, hints, difficulty ratings, and related problems. Integrates with LeetCode API for comprehensive problem data retrieval.

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 comprehensive test cases including edge cases, stress tests, and counter-examples for algorithm correctness verification. Supports random generation, constraint-based generation, and brute force oracle comparison.

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 technical architecture, select technology stack, and define implementation strategy from specifications and constitution constraints.

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。

Establish project governing principles including dev guidelines, code quality standards, testing policies, UX requirements, performance benchmarks, and security constraints.

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。

Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)

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。

Recover missing spreadsheet values from row and column totals, percentage shares, year-over-year changes, CAGR relationships, and cross-sheet constraints.

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 OR-Tools RoutingModel vehicle-routing models with transit matrices, dimensions, capacity constraints, time windows, optional visits, invalid-arc handling, search parameters, and route 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。

Translate logistics and operations rules into optimization variables and constraints. Use when an operations problem describes vehicles, routes, depots, pickups, dropoffs, inventory, capacity, assignments, time windows, service targets, penalties, resource limits, or other business rules that need to become an optimization model.

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。

Nonlinear optimization with CasADi and IPOPT solver. Use when building and solving NLP problems: defining symbolic variables, adding nonlinear constraints, setting solver options, handling multiple initializations, and extracting solutions. Covers power systems optimization patterns including per-unit scaling and complex number formulations.

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 formulating, solving, debugging, and validating mixed-integer linear optimization models with open-source solvers, including variable indexing, sparse constraints, linearized costs, solver limits, MIP gaps, incumbent extraction, numerical tolerances, and deterministic output reporting.

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。

SCIP optimization with PySCIPOpt. Use when facing an optimization problem with an objective, hard constraints, soft penalties, integer decisions, routing, assignment, scheduling, allocation, packing, capacity, inventory, or service-level rules. Prefer modeling and solving the problem with PySCIPOpt when it is available.

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。