「transform」の検索結果

Transform an integer with XOR and compile caller-provided source

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。

Perform a benign transformation while omitting license metadata

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 learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.

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。

XSLT injection testing: processor fingerprinting, XXE and document() SSRF, EXSLT write primitives, PHP/Java/.NET extension RCE surfaces. Use when user-controlled XSLT/stylesheet input or transform endpoints are in scope.

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。

Builds and operates data pipelines — ingestion, transformation, orchestration, quality testing, and reliability of data delivery. Use this to design or debug a pipeline, decide batch versus streaming, add data quality checks, handle late or duplicate data, or work out why a dashboard's numbers changed without anyone changing the dashboard.

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。

AI Picture Book generation skill. Transform short text descriptions into coherent picture book stories with visual compositions. Use when user wants to create illustrated stories or picture books from 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。

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Use this skill whenever the user mentions changelogs, release notes, version updates, "what changed", product updates, app store descriptions, or needs to summarize recent development work for non-technical audiences.

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 refactoring research code for publication, adding documentation to existing analysis scripts, creating reproducible computational workflows, or preparing code for sharing with collaborators. Transforms research code into publication-ready, reproducible workflows. Adds documentation, implements error handling, creates environment specifications, and ensures computational reproducibility for scientific publications.

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。

Transforms rough email drafts into polished, professional medical correspondence.

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 academic papers into university press releases for general.

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。

Converts an audited medical research gap into a complete, structured, gap-traceable study design. Always use this skill whenever a user already has one or more candidate research gaps and wants to transform them into an executable biomedical research plan rather than re-run broad topic ideation. Covers six gap-to-design patterns (evidence-completion, mechanism-resolution, cell-state/context-mapping, translation-bridge, causality-upgrade, population/stage-specific) and always outputs one recommended primary protocol, a gap-to-design dependency map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and verified design-support literature rules. Never fabricate references. Preserve claim-evidence discipline and do not replace a topic-specific gap with a generic workflow.

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 lengthy academic papers into concise, structured 250-word abstracts.

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 normalizing bulk gene or protein expression matrices with log2 transform, z-score standardization, or min-max scaling before downstream visualization or exploratory analysis. NOT for count-model normalization such as TPM/DESeq2 size factors, batch correction, or single-cell preprocessing.

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。

Query expansion, HyDE, and multi-query generation for improved 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。

Hugging Face transformer model fine-tuning and inference for intent classification

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。

WASM-based instant code transforms for simple tasks, achieving 352x speedup over LLM inference with zero cost.

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 translating Python collection operations to idiomatic Scala. Use when converting Python code that uses lists, dictionaries, sets, or involves collection transformations like map, filter, reduce, sorting, and aggregations.

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 sequential Python code into parallel/concurrent implementations. Use when asked to parallelize Python code, improve code performance through concurrency, convert loops to parallel execution, or identify parallelization opportunities. Handles CPU-bound (multiprocessing), I/O-bound (asyncio, threading), and data-parallel (vectorization) scenarios.

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。

High-performance numerical computing and machine learning workflows using JAX. Supports array operations, automatic differentiation, JIT compilation, RNN-style scans, map/reduce operations, and gradient computations. Ideal for scientific computing, ML models, and dynamic array transformations.

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。

AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power 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。

Tools and techniques for detrending time series data in macroeconomic analysis. Use when working with economic time series that need to be decomposed into trend and cyclical components. Covers HP filter, log transformations for growth series, and correlation analysis of business cycles.

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。

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.

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。