「spa」の検索結果

Expert patterns for Godot audio including AudioStreamPlayer variants (2D positional, 3D spatial), AudioBus mixing architecture, dynamic effects (reverb, EQ,compression), audio pooling for performance, music transitions (crossfade, bpm-sync), and procedural audio generation. Use for music systems, sound effects, spatial audio, or audio-reactive gameplay. Trigger keywords: AudioStreamPlayer, AudioStreamPlayer2D, AudioStreamPlayer3D, AudioBus, AudioServer, AudioEffect, music_crossfade, audio_pool, positional_audio, reverb, bus_volume.

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Bevy ECS skill for spatial queries. Keywords: Bevy, raycast, shapecast, picking, physics query.

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Bevy ECS skill for assets and scene spawning. Keywords: Bevy, AssetServer, Handle, Scene, bsn, spawn_scene.

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。

Guideline-4.3 anti-spam / originality gate — score whether an app (and each portfolio addition) is meaningfully distinct in function, content, and metadata before you invest or submit. Use at validation/new-app time and again before submission. Protects the whole developer account from 4.3 (spam/duplicate) rejections. NOT rejection-handler (that works an existing rejection) and NOT competitive-analysis (that positions in the market).

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SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontamin

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standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations.

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Provides S4 classes for general nucleases, CRISPR nucleases, CRISPR nickases, and base editors.Several CRISPR-specific genome arithmetic functions are implemented to help extract genomic coordinates of spacer and protospacer sequences. Commonly-used CRISPR nuclease objects are provided that can be readily used in other packages. Both DNA- and RNA-targeting nucleases are supported.

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Expert knowledge for Azure Service Connector development including troubleshooting, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when wiring Azure compute to Redis, databases, storage, messaging, AI services, or third‑party endpoints, and other Azure Service Connector related development tasks. Not for Azure API Management (use azure-api-management), Azure Connector Namespace (use azure-connector-namespace), Azure Logic Apps (use azure-logic-apps), Azure Service Bus (use azure-service-bus).

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Expert knowledge for Azure Synapse Analytics development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing Synapse workspaces, SQL/dedicated pools, serverless SQL, Spark pools, or Synapse Link for Cosmos DB, and other Azure Synapse Analytics related development tasks. Not for Azure Data Factory (use azure-data-factory), Azure Data Explorer (use azure-data-explorer), Azure Databricks (use azure-databricks), Azure HDInsight (use azure-hdinsight).

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Dispatch to the right problem-solving technique based on how you're stuck

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Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.

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Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.

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Speculative parallel implementation methodology — dispatch N candidate implementations, audit all, score, pick best. Auditability mandate: ALL candidates logged (not just winner).

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 customers should bring other customers — one-way versus two-way incentives, reward structure and economics, trigger moments, referral messaging, tracking and attribution, fraud controls, and anti-spam compliance by region: TCPA for US, GDPR for EU, PDPA for SEA, LGPD for LATAM. Trigger on 'referral program', 'refer a friend', 'word of mouth', 'viral loop', 'how do I get customers to bring friends', 'affiliate rewards'. Also use when the user has happy customers and no system to use them. Not for — running your own community space, see `28-community-building-global`; posting inside third-party communities, see `38-community-seeding-global`; email flow mechanics, see `14-email-marketing-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。

Use when a finished GSD workspace and its worktrees should be deleted. Confirms first, runs git worktree remove for each member repo, and refuses if any repo has uncommitted changes.

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Use when you need an isolated copy of one or more repos with its own .planning/, or want to work on several repos in parallel. Creates worktrees or clones plus WORKSPACE.md; then run /gsd-new-project inside.

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Decision guide for delegating to caveman-style subagents. Tells the main thread WHEN to spawn `cavecrew-investigator` (locate code), `cavecrew-builder` (1-2 file edit), or `cavecrew-reviewer` (diff review) instead of doing the work inline or using vanilla `Explore`. Subagent output is caveman-compressed so the tool-result injected back into main context is ~60% smaller — main context lasts longer across long sessions. Trigger: "delegate to subagent", "use cavecrew", "spawn investigator/builder/reviewer", "save context", "compressed agent output".

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Write correct coroutine scopes, lifecycle collection, and dispatcher injection in Android production code. Use for suspend functions, coroutine scopes, and dispatcher mechanics; defer ViewModel StateFlow/LiveData architecture, Fragment lifecycle recipes, persistence/notifications, and unit-test recipes to their specific skills.

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Tab/空格规范化工具。读取结构化数据文件，执行制表符展开、行尾空格清理与可选公共缩进去除，输出空白已统一的结构化数据文件。 当用户提到Tab转空格、制表符转空格、缩进规范化、行尾空格清除、公共缩进去除等需求时使用此skill。 即使用户没有明确说出"tab_space_normalizer"，只要任务涉及文本中的Tab/空格归一，就应该使用此skill。 不负责文本内容改写、拼写纠错、重复标点合并或其他非空白字符清理。

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Spawn Agentica multi-agent patterns

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检测并管理挂载在工作区 /root/.openclaw/workspace/ 下的网盘（TOS 存储桶挂载）。当你需要将文件保存到持久化存储、检查已挂载的 TOS 存储桶或列出可用的工作区网盘时使用此技能。

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Turn a game-level brief or existing scene snapshot into a reviewable spatial plan, then apply approved changes with coordinate, navigation and scene checks. Use for layouts, encounter placement and terrain plans.

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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]].

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Security-first SOP for multi-tenant RAG systems. Activate when a calling agent is building, reviewing, or debugging any retrieval pipeline whose vector store is shared across more than one user, organisation, workspace, customer, or permission scope. Encodes the single non-negotiable rule — **filter at the vector store query, never after retrieval / never after rerank** — together with the per-vendor query-time filter APIs (Pinecone namespaces + `$eq`/`$in`, Weaviate `multiTenancyConfig` + tenant handle, Qdrant `is_tenant` payload index + `Filter.must`, Chroma `where`, pgvector RLS), and the cross-framework adapters (LlamaIndex `MetadataFilters`, LangChain `filter=` dict). Frame the work as preventing CVE-2024-41892 / EchoLeak / Slack-AI-class cross-tenant leakage, not as "adding a filter for relevance".

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。