2,918 件 ・ 関連度順
概要と使いどころ
Teaches how to reason from a transaction protocol’s rules to a working model. Use when analyzing a concurrency-control paper, spec, or algorithm to identify state, metadata, invariants, operation rules, examples, counterexamples, guarantees, false aborts, unsafe commits, or tradeoffs before inspecting concrete traces.
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Detect pauses and silence in audio using local dynamic thresholds. Use when you need to find natural pauses in lectures, board-writing silences, or breaks between sections. Uses local context comparison to avoid false positives from volume variation.
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Reference guide for translating Python syntax constructs to Scala equivalents. Use when converting Python code to Scala and need mappings for basic syntax elements like variable declarations, control flow, comprehensions, string formatting, and common operators.
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Detect long-term trends in time series data using parametric and non-parametric methods. Use when determining if a variable shows statistically significant increase or decrease over time.
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。
Lomb-Scargle periodogram for finding periodic signals in unevenly sampled time series data. Use when analyzing light curves, radial velocity data, or any astronomical time series to detect periodic variations. Works for stellar rotation, pulsation, eclipsing binaries, and general periodic phenomena. Based on lightkurve library.
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A toolkit for fuzzy string matching and data reconciliation. Useful for matching entity names (companies, people) across different datasets where spelling variations, typos, or formatting differences exist.
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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.
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Reference for the TRL (Transformer Reinforcement Learning) library codebase. Use proactively before reading or editing any file under `trl/` so you have the intended contracts and invariants in mind, not just what the current code says. Covers trainer hierarchy (SFT, DPO, GRPO, KTO), shared utility functions (selective_log_softmax, decode_and_strip_padding, padding helpers), configuration system, model wrappers, and how data flows through any TRL trainer.
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Permutation and sequencing integer-programming formulations with ordered local-window variables, prefix/suffix continuity, and overlap penalties. Use when assigning exams, jobs, tasks, visits, blocks, or resources to ordered positions and costs depend on adjacent pairs, sliding triples, n-grams, short-horizon pressure, or overlapping local patterns.
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Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.
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.
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Subtour-elimination methods for TSP, VRP, pickup/dropoff routing, and routing MIPs with binary arc variables. Use when route-continuity constraints may permit disconnected cycles and the model needs MTZ constraints, flow-based connectivity constraints, DFJ subset cuts, or lazy/iterative subtour cuts.
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Generate environment variable to CLI argument mapping with prefix support, type conversion, and fallback chains for configuration.
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。
Configure cross-env for cross-platform environment variable setting in npm scripts and CLI commands.
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。
Integrate dotenv for environment variable loading with validation and type coercion.
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。
Integrates MCP App UI with host theming system. Applies host CSS variables, handles onhostcontextchanged, safe area insets, display mode detection, and fullscreen configuration.
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Maintain and match against a library of classic dynamic programming patterns. Provides pattern matching, template code generation, variant detection, and problem-to-pattern mapping for DP problems.
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Structured prompt template creation with variables, formatting, and version control
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Coordinate external AI tool integration (OpenAI Codex, Google Gemini) for cross-model adversarial review and delegated implementation.
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Template loading, variable filling, and scaffolding for all GSD artifacts. Manages 22+ templates covering every document type in the GSD system, from PROJECT.md to milestone archives.
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Bug condition/postcondition formalization as testable Behavior Contracts. Defines invariants that must be preserved across fixes.
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Adversarial plan review by 3 independent reviewers (Feasibility, Completeness, Scope & Alignment) before presenting to user.
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。
Execute work units through the rigorous 4-phase Metaswarm cycle (Implement -> Validate -> Adversarial Review -> Commit) with independent quality gate enforcement.
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。