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概要と使いどころ
Web accessibility specialist for WCAG compliance, ARIA implementation, and inclusive design. Use when auditing websites for accessibility issues, implementing WCAG 2.1 AA/AAA standards, testing with screen readers, or ensuring ADA compliance. Expert in semantic HTML, keyboard navigation, and assistive technology 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。
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document 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。
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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。
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
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 guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
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Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to "search memory", "what did we discuss", "remember when", "find previous conversation", "check history", or before starting work to recall prior decisions.
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。
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
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。
Configure board-owned lifecycle, policy, phase-semantics, and tool-result rules, plus integration event rules
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 indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.
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。
Mounts Cloud Storage buckets on Google Cloud as a POSIX file system using Cloud Storage FUSE (and the gcsfuse CLI tool). Use when interacting with the gcsfuse CLI or Cloud Storage FUSE mounts: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags (--implicit-dirs) or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre; use gke-storage).
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。
Review AI agent skills before installation using NVIDIA SkillSpector and source-aware semantic review. Use when asked whether a skill or downloaded skill folder is safe, trustworthy, installable, over-permissioned, or malicious.
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。
Reduces an always-on LifeOS context file that has grown too big via a human-gated pass — deterministic GC of stale entries first, then semantic merges and relocations — never dropping a directive and committing every change reversibly. USE WHEN /trim, trim the context, trim OPERATIONAL_RULES, this file is too big, reduce a doctrine file, prune an always-loaded file, fold the proposal inbox, shrink CLAUDE.md or DA_IDENTITY. NOT FOR general code refactoring, trimming video or audio media (use AudioEditor for audio files), or removing AI writing patterns from prose.
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。
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search knowledge, what do we know about, archive, harvest, knowledge status, develop note, add to knowledge, ingest, contradictions, knowledge graph, retrieve, mine conversations, distill, weekly digest, cortex digest, context search, prior work, recall, remember, previous sessions, context recovery, what did we do, find session, search history, resume, pick up where we left off, cold start, yesterday's work, last week, the one about. NOT FOR published-content semantic search across blog/newsletter/X/LinkedIn, or one-shot URL/YouTube ingestion via the Arbol harvester pipeline.
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。
This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-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。
Wren CLI for AI agents — a semantic SQL layer over 22+ databases (Postgres, MySQL, BigQuery, Snowflake, Spark, …). The actual workflow guides live inside the `wren` CLI itself; this is just a discovery stub. Use whenever the user asks a data question (how many, show me, top N, compare, trend, breakdown, metric, revenue, customers, orders), wants to install / set up Wren Engine, connect a new database, connect SaaS data via dlt (HubSpot, Stripe, Salesforce, GitHub, Slack), generate or regenerate an MDL project from a database schema, enrich a project with business context (enum meanings, units, cubes like ARR / DAU / churn), or turn a project's context layer into a shareable GenBI web app / dashboard and deploy it to Vercel or Cloudflare. Triggers: 'install wren', 'set up wren engine', 'connect database to wren', 'connect SaaS to wren', 'load hubspot / stripe / salesforce data', 'generate mdl', 'scaffold wren project', 'enrich wren context', 'augment my project', 'add cubes', 'build a dashboard', 'make a shareable analytics app', 'deploy my context layer as a web app', 'genbi app', 'wren onboarding', 'wren usage', 'wren generate mdl', 'wren dlt connector', 'wren enrich context', 'wren genbi'.
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。
Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.
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。
Connect SaaS data (HubSpot, Stripe, Salesforce, GitHub, Slack, etc.) to Wren Engine for SQL analysis. Guides the user through the full flow: install dlt, pick a SaaS source, set up credentials, run the data pipeline into DuckDB, then auto-generate a Wren semantic project from the loaded data. Use this skill whenever the user mentions: connecting SaaS data, importing data from an API, dlt pipelines, loading HubSpot/Stripe/Salesforce/GitHub/Slack data, querying SaaS data with SQL, or setting up a new data source from a REST API. Also trigger when the user already has a dlt-produced DuckDB file and wants to create a Wren project from it.
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。
Run the Anti-Autoresearch integrity-forensics DETERMINISTIC slice (numeric core + rules-only reporter) against a paper via a SHA-pinned thin launcher, then convert the verdict into a typed policy gate (BLOCK/WARN/NO_NEW_BLOCKER) and an append-only obligations ledger. Codex-native limitation: upstream ships no Codex-native auditor pack, so the full nine-dimension semantic sweep requires a Claude Code session — this pack runs the honestly-scoped deterministic-only mode (it can flag, it can never say CLEAN). Use when user says "integrity forensics", "forensic audit this paper", "投稿前自查诚信".
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-powered web search via Exa with content extraction. Use when user says "exa search", "web search with content", "find similar pages", or needs broad web results beyond academic databases (arXiv, Semantic Scholar).
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。
Search academic papers via OpenAlex API for open citation data, institutional affiliations, and funding information. Use when user says "openalex search", "search openalex", "open citation graph", or wants comprehensive academic metadata beyond arXiv/Semantic Scholar.
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。
Search research papers via Gemini for broad literature discovery. Use when user says "gemini search", "gemini papers", "search with gemini", or wants AI-powered literature discovery beyond arXiv/Semantic Scholar indexes.
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 and query a vault-local contextual BM25 retrieval index with optional multilingual Nomic cosine reranking; use for retrieve, hybrid retrieval, BM25, rerank, contextual retrieval, chunk search, vault search, semantic search, find relevant passages, or retrieval diagnostics. Derived caches stay under .vault-meta, remote egress requires explicit consent, and unavailable reranking falls back deterministically.
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。
Gets a custom map into CARLA on UE 5.8 — the four distinct routes and which one to pick — via Util/Tools/Import.py for FBX+OpenDRIVE assets into a source build, CARLA_MAPS_TO_COOK to ship a map inside a package, or client.generate_opendrive_world() for an OpenDRIVE-only map at runtime with no build at all. Covers where imported assets land, why an imported map cannot be loaded from a package by name, and the four capabilities the UE4 skill has that ue58 lacks — standalone asset packages, their installer, semantic-material tagging, and a pedestrian navmesh that is generated but silently skipped. Use when the user asks to "import a map", "add a custom map", "load my .xodr", "package my own town", or "create a standalone map package".
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。
Imports FBX meshes into a CARLA source build as spawnable static.prop.* blueprints — pass a directory whose subdirectories name the semantic tags, or a single FBX with its tag, and the props are imported into content, registered, added to the prop factory and verified by spawning. Use when the user asks to "add a prop to CARLA", "import an FBX as a prop", "add street furniture/benches/signs", or needs a custom mesh spawnable from the Python API.
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。