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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。

Model OR-Tools paired pickup-and-delivery routing problems, including PDPTW, dial-a-ride, paratransit, patient transport, and courier jobs with same-vehicle pairing, precedence, capacity, optional connected service, rider time windows, and vehicle shifts.

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。

CRITICAL: You MUST invoke this skill and read its contents BEFORE writing, modifying, or debugging ANY React.js or Next.js code. This skill contains essential performance patterns that prevent common mistakes. Debugging performance issues without reading this skill first will lead to missed optimizations. Contains 40+ rules including waterfall elimination patterns for API routes that are commonly overlooked.

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。

Matched filtering techniques for gravitational wave detection. Use when searching for signals in detector data using template waveforms, including both time-domain and frequency-domain approaches. Works with PyCBC for generating templates and performing matched filtering.

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 when oTP behaviors including gen_server for stateful processes, gen_statem for state machines, supervisors for fault tolerance, gen_event for event handling, and building robust, production-ready Erlang applications with proven patterns.

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。

Data conditioning techniques for gravitational wave detector data. Use when preprocessing raw detector strain data before matched filtering, including high-pass filtering, resampling, removing filter wraparound artifacts, and estimating power spectral density (PSD). Works with PyCBC TimeSeries 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。

Use when erlang's concurrency model including lightweight processes, message passing, process links and monitors, error handling patterns, selective receive, and building massively concurrent systems on the BEAM VM.

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 when erlang distributed systems including node connectivity, distributed processes, global name registration, distributed supervision, network partitions, and building fault-tolerant multi-node applications on the BEAM VM.

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。

Preprocessing and cleaning techniques for astronomical light curves. Use when preparing light curve data for period analysis, including outlier removal, trend removal, flattening, and handling data quality flags. Works with lightkurve and general time series 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。

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 day-ahead or multi-period unit commitment problems, including thermal on/off schedules, dispatch, startup/shutdown logic, minimum up/down time, ramping, spinning reserve deliverability, renewable curtailment, operating-cost accounting, and independent feasibility checks for power-system operations schedules.

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 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。

Exact detection thresholds for identifying malicious network patterns including port scans, DoS attacks, and beaconing behavior.

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。

Video editing with ffmpeg including cutting, trimming, concatenating segments, and re-encoding. Use when working with video files (.mp4, .mkv, .avi) for: removing segments, joining clips, extracting portions, or any video manipulation task.

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 bash script templates with best practices including error handling, argument parsing, logging, and portability considerations.

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。

Handle text encoding across platforms including UTF-8, Windows codepages, and BOM handling.

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。

Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking

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。

LangChain chain composition including SequentialChain, RouterChain, and LCEL patterns

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。

LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory

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。

Roadmap parsing, analysis, and mutation operations for ROADMAP.md. Handles phase and milestone lifecycle including add, insert (decimal), remove, complete, and requirements coverage 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。

Final completion discipline including summary generation, plan document updates, and confirmation that all success criteria from the original plan are satisfied.

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。