4,324 件 ・ 関連度順
概要と使いどころ
把已经确认的选题写成可以直接拍摄或发布的内容，包括选择形式、搭建结构、写口播逐字稿或图文正文，并让产品自然出现。用户要求把选题变成成稿、拍摄稿或发布稿时使用。 English: Turn an approved topic into shoot-ready or publish-ready content by choosing the format, building the structure, writing a spoken script or post, and integrating the product naturally. Use when the user wants a topic developed into a finished draft.
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。
Owns repositories, data sources and mapping for Compose and Compose Multiplatform apps: DTO to domain to UiModel boundaries, Ktor clients and bearer auth, WebSocket and SSE, Room, DataStore, Paging 3, offline-first and data-layer tests. Use when writing or reviewing a repository, data source, DTO, mapper, HttpClient, bearer token, WebSocket, SSE, Room DAO, migration, DataStore, PagingSource, RemoteMediator, offline cache or MockEngine test. Do NOT use for ViewModel or UI wiring (compose-feature), composables (compose-ui), the MVI contract or error tiers (compose-architecture), Gradle modules (compose-project), or commonMain vs platform splits (compose-platform).
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 writing, changing or reviewing code that touches the house contract for Jetpack Compose and Compose Multiplatform apps: module graph, MVI/BaseViewModel, error tiers, state ownership, naming, Koin DI, Navigation 3 and coroutines. The compose skill is the entry point. Do NOT use for Gradle-only work (compose-project), new screens (compose-feature), composable-only work (compose-ui), repositories or persistence (compose-data), or expect/actual splits (compose-platform).
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。
Remove signs of AI-generated writing from prose, and keep it out of a repo. Use when drafting, editing, or reviewing text to make it sound natural and human, or when auditing a whole docs folder for AI slop. Detects 41 patterns across content, language, style, communication, filler, and rhetoric, including: significance inflation, promotional language, -ing tails, vague attributions, AI vocabulary, copula avoidance, negative parallelisms, false agency, em dash overuse, chatbot artifacts, hedging stacks, staccato drama, and aphorism formulas. Includes voice calibration, a no-fabrication rule, statistical tells, and a draft -> audit -> final rewrite loop.
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 writing, testing, or preparing ANY contribution or pull request to the JSONbored/awesome-claude (HeyClaude) repo — adding a community content entry (agent/MCP server/skill/hook/command/rule/guide/collection/statusline), or making a platform/code change (website, registry package, MCP package, scripts). The repo has two separate review paths: a private, one-shot "HeyClaude submission gate" that decides single-file content PRs, and the shared gittensory-orb bot that posts an advisory readiness/linked-issue comment on platform/code PRs but does not itself merge or close content PRs. Unlike JSONbored/gittensory and JSONbored/metagraphed, a linked issue is OPTIONAL here — do not import their mandatory-linked-issue rule into this repo. Invoke for any "contribute to / open a PR against / submit an entry to / fix a bug in awesome-claude (HeyClaude)" 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。
Plugin shim for the mathmodel-skill competition workflow. Use when Codex invokes this plugin for a CUMCM, MCM/ICM, or Diangong Cup modeling-contest task, including problem selection, modeling, robustness, contest-paper writing, compliance, or final submission review. Do not use for generic data analysis or non-competition paper review.
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。
CUMCM 国赛、MCM/ICM 美赛与电工杯数学建模竞赛的端到端协作工作流。Use when a user explicitly works on one of these modeling contests or asks to run/review a modeling-competition paper from problem selection through modeling, solving, robustness, writing, compliance, and final submission review. Provides 10 stages, persistent decision state, competition-specific rules/templates, deterministic scoring helpers, numbered decisions, and Codex/Claude Code handoff. Do not trigger for generic model selection, ordinary data analysis, or non-competition paper review.
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 original short videos using the 人屏同尺度书写｜既有字面、实体补笔与完成姿态 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.
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。
Technical specification and design document expert. Use when writing design docs, RFCs, ADRs, or evaluating technology choices. Covers C4 model, system design, and architecture documentation.
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。
Audit, design, categorize, distribute, and measure agent skills using lessons from Anthropic's Lessons from building Claude Code: How we use skills. Use when reviewing an existing skill, deciding whether a workflow deserves a skill, planning a skill library, turning team knowledge into skills, choosing skill categories, writing trigger descriptions, designing progressive disclosure, or planning skill marketplace and usage measurement.
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。
Provides React patterns for hooks, effects, refs, and component design. Covers escape hatches, anti-patterns, and correct effect usage. Use when reading or writing React components with hooks or effects.
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 or audit SLOs, SLIs, alert rules, incident response steps, escalation paths, postmortems, operational runbooks, and customer-impact communication. Use when defining production reliability, preparing launch readiness, responding to an outage, writing a runbook, tuning alerts, or closing the loop after an incident.
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 writing, modifying, or reviewing Rust code. ALWAYS invoke before Rust edits; covers Microsoft Pragmatic Rust guidance for error handling, API design, performance, and idiomatic 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。
Complete testing strategy covering TDD workflow, test pyramid, unit/integration/E2E/property testing, framework best practices (Jest, Vitest, pytest), mock strategies, and CI integration. Use when writing tests, reviewing test quality, or establishing testing standards.
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 engaging video scripts for YouTube, TikTok, and other platforms. Creates complete scripts with hooks, main content, and CTAs. Supports various formats including tutorials, vlogs, reviews, explainers, and storytelling. Use when creating video scripts, writing YouTube content, or planning video structure.
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 asked to design a new feature or project
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 an agent needs to write user stories for a project
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。
Master requirements gathering, user story writing, acceptance criteria definition, and scope management. Transform insights into clear, actionable 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。
Create social media content for Twitter/X, LinkedIn, Facebook, Instagram including posts, threads, carousels, and engagement strategies. Use when writing social posts, planning content calendars, or creating viral content.
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。
A/B test subject line variations using proven copywriting frameworks. Predict open rates based on historical performance.
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。
Detects 55 AI writing patterns and rewrites text in five voice profiles so it reads like a specific human wrote it, with an optional 0-100 AI-tell score. Use when text sounds AI-generated or like a chatbot, when preparing a blog post, README, or LinkedIn post for publication, when auditing prose for AI tells, or when editing a Markdown file in place. Triggers on phrases like "humanize this", "make this sound less AI", "make this sound human", "remove AI tells", "does this read like ChatGPT", and "rewrite so it does not sound AI-generated". Pure Markdown, zero dependencies, no network calls.
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 writing or running PowerShell on Windows, especially native programs, quoted paths, escaping, pwsh, Start-Process, file operations, or shell troubleshooting.
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。
Build DSPy evaluation harnesses with rich-feedback metrics that are essential for GEPA optimization. Use when writing a metric function, calling dspy.Evaluate, splitting dev/val sets, debugging "why is my optimizer not improving?", or designing CI-ready DSPy eval suites.
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 dspy.RLM (Recursive Language Model) for reasoning over contexts too large to fit in an LLM's working window — entire codebases, long logs, massive documents, or multi-step data exploration that needs a sandboxed Python REPL. Use when the input is >100k tokens, needs recursive chunking, or benefits from the LLM writing and running code to probe 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。