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概要と使いどころ
Best practices for structuring prpm.json package manifests with required fields, tags, organization, multi-package management, enhanced file format, eager/lazy activation, and conversion hints
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Design an adaptive assessment loop where each student response triggers the next instructional move. Use when building technology-enhanced formative assessment 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。
Analyze images, video, speech, motion, products, and websites; route local vision, audio, depth, tracking, matting, identity, and restoration models; auto-edit, replicate, enhance, caption, voice, assemble, annotate, validate, and export editable Timeline Studio projects and videos. Use for reference-video remakes, filter and repeated-shot reconstruction, subject-aware reframing, clip splitting, source-time speed curves, Color Wheels grading, ramps and holds, person or product cutout, person or object outline, authorized face swap, shot and timing reconstruction, timeline markers, chapters, beat cues, review notes and ranges, supplied or web-sourced footage, AI video platform selection, cleanup, highlights, product promotion, website walkthroughs, image-to-video assisted edits, optical-flow editing, depth-assisted relighting, grayscale depth-video conversion, depth/2.5D/transition finishing, AI voiceover, subtitles, short-form production, .timeline automation, or editor evaluation.
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。
Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.
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MCP server optimization and transport layer enhancement for claude-flow v3. Implements connection pooling, load balancing, tool registry optimization, and performance monitoring for sub-100ms response times.
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Amazon search ranking optimization and A9 algorithm mastery. Comprehensive SEO strategy, indexing optimization, keyword ranking improvement, and search visibility enhancement. Use when the user asks about Amazon SEO, A9 algorithm, search ranking, or product discoverability.
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。
Premium A+ and Brand Story — module design, lifestyle imagery, comparison charts, mobile 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。
Edit podcast audio or video — trim pre/post-show chat, remove filler words, cut silences, enhance audio quality, and cut a video version of the same edit. Use when the user asks to edit a podcast, clean up audio, remove fillers, trim a recording, or improve voice quality.
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。
Break down feature requests into detailed, implementable plans with clear tasks. Use when user requests a new feature, enhancement, or complex change.
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。
Enhanced Mikado Method for complex architectural refactoring - systematic dependency discovery, tree-based planning, and bottom-up 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。
Use when conversion TRACKING must be set up or fixed before spending — Meta Pixel and Conversions API, Google Ads conversions with Enhanced Conversions, GA4, TikTok Pixel and Events API, server-side GTM, consent mode for GDPR and CCPA, UTM conventions, iOS ATT attribution windows, and a pre-launch verification checklist. Trigger on 'tracking setup', 'pixel setup', 'CAPI', 'GA4 events', 'conversions are not being recorded', 'Meta and Shopify numbers do not match'. Also use when launch is imminent and nothing has been verified. Not for — auditing a live account broadly, see `21-ads-audit-global`; analyzing data once it flows, see `13-data-analysis-global`; the campaign hierarchy, see `52-account-structure-global`.
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。
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
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。
Enhanced academic paper review response workflow with Agy/CLI collaborative analysis and multi-perspective discussion. Produces structured rebuttal documents with evidence-based strategies. Triggers on "rebuttal", "respond to reviewers", "review response", "审稿回复".
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。
为中文毕业论文补强并核验引用：找出必须有引文支撑的句子，扩展候选文献，检查引用与论断是否匹配，并回写参考文献与正文引用。
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Build forms, validate data, and react to user input in Blazor. USE FOR adding forms, search boxes, filter panels, inline editing, data-entry UI, file uploads, validation (annotations or custom), handling form submissions, and binding input controls. Covers EditForm, built-in input components, DataAnnotationsValidator, custom validation, SSR form patterns (SupplyParameterFromForm, FormName, AntiforgeryToken, Enhance), and @bind for simple interactive controls. DO NOT USE for project scaffolding (see create-blazor-project) or prerendering issues (see support-prerendering).
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Guides conversion of a pre-.NET 8 Blazor Server app into a .NET 8+ Blazor Web App. USE FOR: migrating apps that use AddServerSideBlazor and MapBlazorHub to the AddRazorComponents/MapRazorComponents model, converting _Host.cshtml to an App.razor root component, replacing blazor.server.js with blazor.web.js, migrating CascadingAuthenticationState to a service, adopting new Blazor Web App features like enhanced navigation and streaming rendering. DO NOT USE FOR: apps that are already Blazor Web Apps (already use AddRazorComponents and MapRazorComponents), Blazor WebAssembly or hosted Blazor WebAssembly apps (different migration path), apps that should stay on the Blazor Server hosting model without converting, or apps still targeting .NET Framework.
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。
Volcengine VOD audio and video processing tools skill. Use when users need VOD-based media processing or editing: upload local/URL media, stitch videos, trim clips, flip frames, change playback speed, create image-to-video, compose audio/video, extract audio, mix audio, separate vocals/accompaniment, denoise audio, enhance quality, AI super-resolution, frame interpolation, ASR speech-to-text, OCR text extraction, subtitle removal, subtitle embedding, scene slicing, portrait/green-screen matting, highlight extraction, comic style transfer, video translation, drama recap narration, drama script restoration, media info lookup, or playback URL retrieval. The skill submits async VOD jobs, polls task status, and returns generated output links. Not for pure text generation, real-time streaming, or source-free generative video creation.
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 need to generate or improve Java project documentation — including README.md files, package-info.java files, and Javadoc enhancements — through a modular, step-based interactive process that adapts to your specific documentation needs. This should trigger for requests such as Improve the code with documentation; Apply documentation; Refactor the code with documentation; Generate README or developer documentation for a Java project; Document Java APIs architecture or project workflows. Part of Plinth Toolkit
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。
Enhancement-overlay SOP for adding sparse (BM25 / keyword) retrieval alongside dense (embedding) retrieval. Activate when a calling agent is building, reviewing, or debugging a retrieval pipeline whose corpus contains exact-match tokens — identifiers, error codes, SKUs, API/function names, proper nouns, citations, rare jargon — that pure dense embedding silently misses. Encodes the single decision rule (**hybrid is traffic-driven, not theoretical: add sparse only when the query share that depends on exact tokens is non-trivial**), the wiring of QueryFusionRetriever-style fusion (RRF vs alpha-weighted), and per-query-type alpha tuning. Frame the work as recovering lexical identity that dense pooling destroys, not as "add keyword search for completeness". Cross-links [[llamaindex]].
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。
Re-ingest-correctness SOP for production RAG. Activate when a calling agent builds, reviews, or debugs an ingestion pipeline that runs more than once over a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule — **ingestion must be idempotent: a document's content hash decides insert/update/skip, so re-running over unchanged docs is a no-op** — plus the docstore + doc-hash upsert machinery (LlamaIndex `IngestionPipeline` + `DocstoreStrategy`), the delete-propagation problem, and cross-framework equivalents (LangChain `index()` + `RecordManager`, manual hash table). ENHANCE overlay over [[llamaindex]]: the IngestionPipeline exists in the base skill but the re-ingest-correctness contract is not surfaced.
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。
Enhancement-overlay decision protocol for STREAMING the output of long-running LLM / agent runs from the *backend*, not just wiring a typing animation in the UI. Activates when a coder agent must stream final tokens to a chat client, surface intermediate agent steps (which tool, which node, partial reasoning), emit custom tool-progress events, choose a transport (SSE vs WebSocket), or decide what to do when the client disconnects mid-stream. The langchain / langgraph skills mention stream modes but stop at "you can stream"; this skill encodes *what to stream, over what transport, and how to fail safely*.
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。
Enhancement overlay — version the WHOLE deployable LLM-app artifact as one bundle: prompts + compiled programs + model snapshot pins + retrieval config + eval-set version, versioned together so a deploy is reproducible and rollback is atomic. Activate when preparing to deploy an LLM app, when asking "what exactly is running in prod right now?", when a deploy must be reproducible months later, or when an incident needs a clean rollback. The core reframe: an LLM app artifact is NOT an ML model — it is a manifest over many independently-mutable parts, not one weights file. Do NOT activate for one-off prompt edits with no deploy, for a single-component demo, or where a vendor owns the whole prompt lifecycle. For versioning ONE compiled prompt use [[agentsop-per-model-artifacts]]; for the CI comparison mechanism use [[agentsop-regression-gate]]. Search keywords: prompt versioning, reproducible deploy, what is running in prod, rollback LLM app, model pinning, prompt registry, version prompts and config.
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ENHANCE overlay on [[dspy]] — the upfront rubric for choosing a reasoning SHAPE (Predict / ChainOfThought / ReAct / ProgramOfThought) BEFORE you write a prompt or pick an optimizer. The local `dspy` skill lists the modules but never surfaces the *selection criterion*: reasoning shape is chosen by task structure, not by reflexively defaulting to CoT. Activate every time a new LM-calling node/step is added to a pipeline. Do NOT activate for one-shot prompts, optimizer/teleprompter choice (that is the dspy SOP's job), or non-LM control flow. Search keywords: chain of thought vs ReAct, when to use CoT, reasoning type, ReAct vs CoT vs PoT, which dspy module, predict vs chain of thought.
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。
Enhancement-overlay skill — the DECISION + WIRING layer for LM observability that the single-backend skills [[langsmith]], [[phoenix]], [[mlflow]] do NOT cover. Each of those installs one backend; none of them help you DECIDE which backend fits your stack/scale/budget, nor give you a one-line autolog that turns it on fast. Use when starting any LM project, before the first deploy, or the moment someone asks "why did it do that?" and there are no traces to answer with. The skill picks a backend by stack (LangSmith for LangChain/LangGraph; Phoenix for OSS/local OpenTelemetry; MLflow for ML-shops already on MLflow; Langfuse for self-host), wires one-line autolog, verifies traces land, and adds eval hooks — instrumenting BEFORE you need it. Cross-links the first-debug-move skill [[agentsop-prompt-history-inspect]]. Do NOT activate to re-teach a backend you already chose (defer to its own skill), or for non-LM ML experiment tracking with no LLM calls (that is plain MLflow).
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。