「version」の検索結果

Content web architecture framework. Use when diagnosing offer doc usage, content-to-conversion pathways, or why someone isn't getting sales despite traffic.

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。

Transcribe audio and video from URLs (YouTube, direct media links) using WhisperKit locally. Optionally analyze transcripts with AI when explicitly requested. Use when users provide URLs to media content and request transcription or speech-to-text conversion.

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/audio/image processing with FFmpeg and ImageMagick. Tools: FFmpeg (video/audio), ImageMagick (images). Capabilities: format conversion, encoding (H.264/H.265/VP9/AV1), streaming (HLS/DASH), filters, effects, thumbnails, watermarks, batch processing, hardware acceleration (NVENC/QSV). Actions: convert, encode, resize, crop, compress, extract, merge, stream, transcode media. Keywords: FFmpeg, ImageMagick, video encoding, audio extraction, image resize, thumbnail, watermark, HLS, DASH, H.264, H.265, VP9, AV1, codec, bitrate, framerate, resolution, aspect ratio, filter, overlay, concat, trim, fade, batch processing. Use when: converting video/audio formats, encoding with specific codecs, generating thumbnails, creating streaming manifests, extracting audio from video, batch processing images, adding watermarks, optimizing file sizes.

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。

Statistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.

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Write high-converting landing page copy using proven frameworks like PAS (Problem-Agitate-Solution), AIDA, and StoryBrand. Creates headlines, value propositions, CTAs, and full page sections optimized for conversion. Use when users need landing page copy, sales page content, or marketing website 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。

Design and optimize pricing strategies for SaaS products including tiering, packaging, value metrics, and experimentation. Use when setting initial pricing, optimizing conversion, expanding revenue, or when users ask about pricing strategy, monetization, or revenue optimization.

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。

Create high-converting landing pages with persuasive copy, clear CTAs, social proof, and optimized structure. Use when building sales pages, product pages, lead capture pages, or conversion-focused pages.

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。

Create a release PR with version bump and changelog update

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Fix silent data loss when mutating Nostr replaceable events (Kind 0 profile, Kind 3 contact/follow list, Kind 10002 relay list, etc.) in client apps. Use when: (1) Following someone wipes the user's entire follow list, (2) Updating profile metadata loses existing fields, (3) Fresh browser session or mobile login causes data loss on first action, (4) Replaceable event mutation uses stale or null cached state. Root cause: Nostr replaceable events are full-replace (no partial update), so publishing based on stale/unloaded cache overwrites the canonical version. Applies to any Nostr client using React, Flutter, or similar reactive frameworks where query state may not be loaded when a mutation fires.

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。

Fix ClickHouse Cloud migration failures caused by multi-table RENAME statements. Use when: (1) Migration fails with "Database X is Shared, it does not support renaming of multiple tables in single query", (2) golang-migrate or other migration tools show dirty database version after a table-swap migration on ClickHouse Cloud, (3) Schema migration works on self-hosted ClickHouse but fails on ClickHouse Cloud. ClickHouse Cloud uses SharedMergeTree engine which has restrictions not present in regular MergeTree.

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。

Fix Fastly Compute Rust crate `cargo test` failures with "ld: symbol(s) not found" errors for Fastly SDK symbols like `_version_set`, `_body_new`, `_req_send` on native host builds. Use when: (1) `cargo test` on a Fastly Compute crate fails at link time with errors mentioning libfastly and symbols with names like `fastly::http::response::handle::...`, (2) You added a `#[cfg(test)]` module to a file in a Fastly Compute binary crate and the tests never seem to run or fail to link, (3) Existing `#[cfg(test)]` modules in src/main.rs or its sibling modules appear dead despite being present in the source tree, (4) `cargo test --lib` works but `cargo test` fails.

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Fix Fastly Compute Rust build failures caused by edition2024 dependencies. Use when: (1) cargo build fails with "feature `edition2024` is required", (2) wit-bindgen or wasip2 crates fail to download/parse, (3) Fastly SDK pulls in incompatible transitive dependencies, (4) Build worked before but fails after dependency update. Solves by pinning wit-bindgen, wasip2, and related crates to pre-edition2024 versions.

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。

Per-test JSON result files with metadata, versions, and infrastructure details

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Use when the user's task involves Ant Design (antd) — writing antd components, debugging antd issues, querying antd APIs/props/tokens/demos, migrating between antd versions, or analyzing antd usage in a project. Triggers on antd-related code, imports from 'antd', or explicit antd questions.

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。

Convert Chinese or mixed-language text, iMandalArt cards, Obsidian 九宮, 表格, Mandala notes, or eight-domain outlines into a Markdown 3x3 nine-grid table with an ◎ center, then copy, save, append, or hand it to apps that render Markdown tables such as Obsidian and AIDA. Use when the user asks for Markdown 九宮格, Obsidian 九宮, Markdown table 九宮, 曼陀羅表格, nine-grid clipboard, AIDA table output, or conversion between Markdown 九宮, iMandalArt, and 81-grid workflows.

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Convert structured project-note JSON manuscripts into validated EPUB and optional Kindle-clickable PDF files. Use when the user asks to turn project notes, book-system JSON, FIRE-indexed notes, Luhmann/Zettelkasten numbered chunks, table-of-contents cards, index cards, cross-linked project notes, or manuscript JSON into an EPUB/PDF with chapter directories, keyword index cards, weighted links, bidirectional backlinks, versioned filenames, chapter-splitting plans, and full link 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。

Orchestrate production-grade Blender work by probing the installed Blender version, retrieving relevant official Blender documentation, assigning visual design intent, classifying shape grammar and geometry/deformation/procedural/physics/shading needs, comparing direct Blender components with node systems, creating staged artifacts, and routing to specialist skills. Use for every non-trivial Blender creation, reconstruction, simulation, animation, environment, or scene-modification request, especially when the user does not know whether to use topology, Boolean, Array, curves, snapping, Displace, simulation, Geometry Nodes, or shading.

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。

Compares old and new requirement documents, analyzes code repository impact, and generates detailed modification plans. Use when Claude needs to: (1) Compare requirement versions and identify changes, (2) Map requirement changes to code components, (3) Identify components to modify, delete, or add, (4) Analyze dependencies and integration points, (5) Assess test impact, (6) Generate comprehensive modification plans in Markdown format. Supports text/Markdown requirements and analyzes feature-level, functional, and API changes.

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。

Analyzes and verifies semantic equivalence between two functions, classes, or modules by examining control flow, data flow, and observable behavior. Use when comparing code implementations (refactored vs original, different implementations of same functionality, migration verification), determining if two code artifacts produce identical behavior, identifying behavioral differences between code versions, or validating that code changes preserve semantics. Supports formal reasoning and symbolic execution approaches.

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。

Hardware verification tool for checking functional equivalence between two RTL designs (Verilog). Use when users need to: (1) Verify if two RTL versions are functionally equivalent, (2) Compare original vs. refactored RTL code, (3) Validate design changes or optimizations, (4) Identify semantic vs. cosmetic differences, (5) Generate counterexamples for non-equivalent designs. Analyzes interface alignment, state variables, logic differences, and produces detailed equivalence verdicts with plain language explanations. Particularly effective for design verification, code reviews, and regression testing of RTL modifications.

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。

Suggests rollback strategies for failed deployments across different platforms and failure types. Use when deployments fail and need to be reverted, including application code rollbacks, database migration reversions, infrastructure changes, and configuration updates. Supports Docker/Docker Compose environments with step-by-step procedural guidance for safe and effective rollback execution.

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 generates regression tests for Python codebases by analyzing changes between old and new code versions and their existing tests. Migrates tests to work with new code, generates tests for new functionality, and creates mocks for external dependencies. Supports unittest and pytest frameworks. Use when refactoring code, adding features, or ensuring backward compatibility.

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。

Updates Python test code to work with new versions of the code being tested. Use when Claude needs to: (1) Update tests after code changes, (2) Fix broken tests due to signature changes, (3) Update assertions to match new behavior, (4) Add test cases for new functionality, (5) Analyze code differences and their test impact, (6) Run tests and fix failures based on error messages. Takes old code, new code, and old tests as input, outputs updated tests that pass.

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 Python programs into equivalent Dafny code, preserving program semantics and ensuring the generated code is well-typed, executable, and verifiable. Use when the user asks to convert Python code to Dafny, port Python programs to Dafny, add formal verification to Python code, or create Dafny versions of Python algorithms with specifications.

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。