「writing」の検索結果

Writes complete, publication-grade figure legends that can stand on their own. Use when writing or revising figure legends for any scientific figure — bar charts, line graphs, scatter plots, box plots, heatmaps, survival curves, flow cytometry plots, western blots, microscopy images, or schematic diagrams. Also triggers on "write a figure legend for", "help me describe this figure", "my figure needs a legend", "write Figure 1 legend", or "what should a figure legend include".

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 creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

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 the user is designing, prototyping, or rewriting a desktop app that must run on multiple OSes (macOS + Windows, optionally Linux) AND feel indistinguishable from a native app to its users — fast launch, native windowing, native input handling, native materials. Trigger words include "cross-platform desktop", "Electron alternative", "Tauri vs native", "WebView wrapper", "near-native performance", "Raycast architecture", "WebKit/WebView2 quirks", "WKWebView", "system tray app", "global hotkey app", "launcher app". Do NOT trigger this skill for pure web apps, pure mobile apps, or for greenfield projects that have no native-feel requirement.

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。

Prepare and run XQUIC local compilation. Use when asked to compile, build, configure, or diagnose the local build environment for XQUIC, especially across macOS/Linux. Detect intent and platform, then delegate actual compilation to scripts/validate.sh instead of hand-writing CMake 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。

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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 writing analytical SQL over time-series data with the TimescaleDB Toolkit (timescaledb_toolkit extension) hyperfunctions: approximate percentiles, statistical summaries, time-weighted averages, counter/gauge rates, uptime/heartbeat tracking, state durations, OHLC candlesticks, approximate distinct counts, top-N, and downsampling. **Trigger when user asks to:** - Compute percentiles/medians/p95/p99 over large or rolled-up time-series data - Compute rates or deltas from monotonic counters (Prometheus-style) or gauges - Compute time-weighted averages or integrals over irregularly sampled data - Track uptime/downtime from heartbeats, or time spent in each state - Build OHLC/candlestick or VWAP data for financial ticks - Store re-aggregatable summaries in continuous aggregates (two-step aggregation, rollup) - Approximate COUNT DISTINCT, find top-N / most frequent values, or downsample for charts **Keywords:** timescaledb_toolkit, hyperfunctions, percentile_agg, uddsketch, tdigest, approx_percentile, stats_agg, time_weight, counter_agg, gauge_agg, heartbeat_agg, state_agg, candlestick_agg, hyperloglog, approx_count_distinct, min_n, max_n, mcv_agg, lttb, asap_smooth, rollup, two-step aggregation

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。

Explore an existing PostgreSQL database before answering questions about its data or writing SQL. Use this skill whenever a user asks for a query or a data-backed answer against an unfamiliar schema (counts, missing or failed records, recent changes), asks where a business concept lives, or asks how tables, joins, views, routines, triggers, RLS, or extensions work. Find the relevant objects with read-only pg_catalog queries, then request approval before inspecting data-derived statistics or rows. Not a schema-design or migration guide.

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 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.

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。

Guide for writing idiomatic Scala when translating from Python. Use when the goal is not just syntactic translation but producing clean, idiomatic Scala code. Covers immutability, expression-based style, sealed hierarchies, and common Scala conventions.

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。

Use when working with Lean 4 (.lean files), writing mathematical proofs, seeing "failed to synthesize instance" errors, managing sorry/axiom elimination, or searching mathlib for lemmas - provides build-first workflow, haveI/letI patterns, compiler-guided repair, and LSP integration

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。

Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.

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。

Compare and implement eviction policies (LRU, LFU, FIFO, S3FIFO, ARC) for bounded-capacity caches. Use when choosing or implementing an eviction policy for a buffer pool, page cache, CDN edge, or LLM KV cache, or when writing a replay simulator that supports multiple policies. Clarifies recency vs frequency semantics, queue topology, saturating counters, ghost buffers, and the second-chance rule that distinguishes modern FIFO-family policies from classic LRU.

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。

Operational workflow for hard integer-programming optimization tasks: selecting an installed solver, preserving solver/incumbent certificates, extracting feasible schedules, recomputing metrics from final outputs, and writing consistent reports. Use when a task requires a MIP, solver status, objective value, bound, gap, formulation write-up, or benchmark output files.

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。

Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

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 reading sensor data from CSV files, writing simulation results to CSV, processing time-series data with pandas, or handling missing values in datasets.

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 reading or writing YAML configuration files, loading vehicle parameters, or handling config file parsing with proper error 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。

Drive feature development using Outside-In TDD with Hexagonal Architecture. Design emerges through inline code, in-memory fakes, interface extraction, and deferred I/O. Use when building features, writing tests, or structuring backend services. Triggers on: TDD, outside-in, hexagonal, ports and adapters, emergent design, acceptance test, component test, walking skeleton, in-memory fakes, component, contract test, adapter, fast tests, sub-second feedback. Language-agnostic (Go, Rust, Python, TypeScript, Java, C#).

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 implementing any feature or bugfix, before writing implementation code. Enforces RED-GREEN-REFACTOR cycle.

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 creating new skills, editing existing skills, or verifying skills work before deployment.

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 you have a spec or requirements for a multi-step task, before touching code. Creates bite-sized TDD implementation plans with dependency tracking.

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。

Write feature specifications as requirements and user stories with acceptance criteria, focusing on business value and testable conditions.

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。

STATE.md reading, writing, and field-level updates. Provides cross-session state persistence via .planning/STATE.md with structured fields for current task, completed phases, blockers, decisions, and quick 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。

Transform research findings into actionable implementation plans with stakes-based rigor, test-first strategy, and granular task decomposition.

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。