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概要と使いどころ
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
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Student semester onboarding — clinic procedures, tool walkthrough, practice exercises before real cases. Reads the handbook the professor uploaded at setup and teaches it interactively. Use when a new clinic student says "onboard me", "I'm new to the clinic", "getting started", or at the start of each semester; pass --card for the one-page reference.
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Guided customization of your legal clinic profile — change one thing without re-running the whole cold-start interview. Adjust clinic profile, jurisdiction, supervision style, practice-area templates, semester configuration, or output safeguards. Use when the user says "change my [thing]", "new semester", "add a practice area", "update my config", or "customize".
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。
Parses and processes SARIF files from static analysis tools like CodeQL, Semgrep, or other scanners. Triggers on "parse sarif", "read scan results", "aggregate findings", "deduplicate alerts", or "process sarif output". Handles filtering, deduplication, format conversion, and CI/CD integration of SARIF data. Does NOT run scans — use the Semgrep or CodeQL skills for that.
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, update, inspect, or repair Data Analytics semantic layers. Use when the user asks to save data context or create a semantic layer that future Data Analytics work can inspect and cite.
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。
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.
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Own concrete MSBuild ItemGroup and item-expression questions. USE FOR: Include, Remove, Update, item identity and metadata, transforms, filtering, batching, duplicate or overlapping items, and reviews that verify whether those operations are correct. Generated items stay in scope when the central defect is item identity, metadata, batching, duplicate declarations, or glob/Remove/Update semantics. For a generated artifact missing from compilation or output, wrong target timing/path, or FileWrites clean tracking without a broader item-semantics defect, use including-generated-files. The item operation may be broken, suspected, or already correct. Exclude property-only issues, general incrementality with no item question, broad reviews with no concrete item concern, and non-MSBuild systems.
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。
Paper Retrieval Agent - Multi-database paper fetching from Semantic Scholar, OpenAlex, arXiv Handles rate limiting, deduplication, and PDF URL extraction Use when: fetching papers, searching databases, paper retrieval Triggers: fetch papers, retrieve papers, database search, Semantic Scholar, OpenAlex, arXiv
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。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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。
⚠️ CRITICAL USER EXPERIENCE-BASED SKILL - ALWAYS CONSULT BEFORE DATA PREPROCESSING ⚠️ Prevents catastrophic errors (88.9% error rate in V1.0 case study) through multi-level feature analysis, data leakage detection, and semantic validation. MANDATORY for: data preprocessing, feature engineering, standardization, normalization, interpolation, missing value handling, feature selection, or ANY data transformation task. Covers grouped time-series, cross-sectional, panel data. Detects: time travel leakage, causal inversion, ID misuse, semantic-numeric fallacies, distribution blindness. User's hard-won lessons from real project failures.
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。
Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI
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Query the Ensembl database to resolve gene, transcript, and protein IDs, fetch genomic or protein sequences, retrieve gene structures (exons), and get variant consequence and effect predictions (VEP). Use this skill as a primary ID translator, genomic sequence database and variant effect prediction tool.
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The reference agents' cache-stable request assembly, covering the static system and per-request context split, the fixed tool list, the rolling conversation breakpoint, which config fields are prompt bytes, and verification. Load when writing or reviewing a commerce agent's prompt assembly, when cache reads are zero, or when a turn's latency or cost is the question.
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。
Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets. Triggers: "azure-search-documents", "SearchClient", "SearchIndexClient", "vector search", "hybrid search", "semantic search".
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Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET", "SearchClient", "SearchIndexClient", "vector search C#", "semantic search .NET", "hybrid search", "Azure.Search.Documents".
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。
Polish assemblies and call variants from Oxford Nanopore data using medaka. Uses neural networks trained on specific basecaller versions. Use when improving ONT-only assemblies or calling variants from Nanopore data without short-read polishing.
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 arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu 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。
Use when the user asks to "build a story bank", "collect our origin and customer stories", or "assemble reusable proof stories for the message"; assembles reusable narrative units — origin, founder, customer, transformation, and proof stories — each tagged to a claims-ledger ID and a message-house pillar, with every proof labeled Measured / User-provided / [needs source]. Not for authoring the message house or pillars — use message-system-architect; not for brand voice or naming rules — use brand-language-codifier; not for finished long-form prose — use content-writer; not for adjudicating whether a proof is true — use offer-claims-registry. 品牌故事库/起源客户转化/证据故事单元
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Guides migration, provisioning, search, log-analytics, trace-analytics, and Agentic AI Assistant workflows for Amazon OpenSearch Service and Serverless across six capabilities — migration (Solr/ES/self-managed into AOS/AOSS, schema/query translation, sizing, cutover); provisioning (domain + AOSS lifecycle, upgrades, FGAC, monitoring); search (vector / semantic / hybrid / RAG with Bedrock); log-analytics (PPL, OSI, anomaly detection, Dashboards); trace-analytics (OTel spans, service maps, Data Prepper); ai-assistant (natural language data exploration, incident investigation, root cause analysis). Triggers on OpenSearch, AOS, AOSS, Elasticsearch, Solr, vector/k-NN/semantic/hybrid search, RAG, log analytics, PPL, trace analytics, ISM, FAISS, HNSW, Migration Assistant, UltraWarm, OR1, query my data, analyze logs, investigate errors, root cause analysis.
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 native-feeling, benchmark-quality mobile app screens (Expo / React Native). Use when designing or implementing any mobile UI — screens, flows, onboarding, paywalls, tab bars, sheets, settings, empty states — or when polishing motion, navigation, typography, dark mode, or perceived performance. Enforces Apple HIG fidelity, semantic colors, native controls, anti-slop discipline, navigation semantics (push vs replace, modal vs sheet vs overlay, the one-way doors where back must not exist), purposeful Reanimated motion, a full-motion simulator-verified iteration loop, and a study-real-apps-first workflow (pairs with the Appllama MCP). Trigger on "build a screen", "make this screen better", "design the onboarding", "wire up this flow", "polish the UI", "make it feel native", or any mobile design/implementation 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。
Inspecteur des finances publiques IA. Simule un contrôle fiscal DGFIP complet sur les comptes d'une entreprise française (SASU, EURL, SAS, SARL). Analyse le FEC, la liasse fiscale, les charges déduites, le compte courant d'associé, la TVA, l'IS selon 8 axes de vérification. Identifie les chefs de redressement potentiels avec montants, base légale et niveaux de risque. Triggers: contrôle fiscal, redressement, vérification comptabilité, DGFIP, FEC, déductibilité, audit fiscal, tax audit
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。
Designs the warehouse and semantic layer — source-to-mart structure, dimensional modeling, grain, slowly changing dimensions, and the metric layer analytics reads through. Use this to design or restructure a warehouse, model a new source, decide on grain or table structure, build a semantic or metric layer, or diagnose why queries are slow, wrong, or impossible to write.
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 semantic consistency auditor for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.
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 evaluating crypto narratives, attention rotation, memecoin cycles, Solana-style ecosystem momentum, social distribution, and reflexive retail flows in an Ansem-style crypto market 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。