1,181 件 ・ 関連度順
概要と使いどころ
Finds Fabric items across every workspace with the Catalog Search API when the workspace is unknown, locating an item by display name, inventorying one item type tenant-wide, and returning the workspace and item IDs later calls need. Dataflow Gen1 and Gen2 items are not supported. Use only to locate items; once found, query them with the owning item skill such as sqldw-cli, spark-cli, eventhouse-cli or fabriciq.
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。
Manages a Fabric SQL database item, the OLTP SQL Server engine, including running T-SQL through sqlcmd, temporal and vector similarity search, schema and sys.tables inspection, dacpac deployment, and Query Store, blocked sessions and regressed-plan investigation. Use for any query or change against a SQL database item. Warehouse, lakehouse SQL endpoint and mirrored items belong to sqldw-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。
Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.
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 NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell). Use when finding records by keyword, building reproducible field-qualified queries, navigating the Entrez Query Translator, exploiting the history server for large result sets, handling retmax caps, or interpreting weekly index lag. Covers PubMed, Nucleotide, Protein, Gene, SRA, GEO, Assembly, Taxonomy, ClinVar, dbSNP.
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。
Bulk-query Ensembl BioMart (and other BioMart instances) for cross-database ID mapping, gene/transcript/exon coordinates, and ortholog tables. Use when batch-converting Ensembl IDs to other namespaces (HGNC, RefSeq, UniProt, Entrez), pulling gene coordinate tables for thousands of genes, building ortholog wide-tables across species, or replacing slow Ensembl REST loops with one-shot bulk export. Encodes BioMart's XML query format, R biomaRt vs Python pybiomart trade-off, mart-vs-dataset hierarchy, and the URL endpoint that's BioMart-specific (separate from rest.ensembl.org).
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, review, or improve data persistence using SwiftData. Use when defining @Model classes with @Attribute, @Relationship, @Transient, #Unique, or #Index; when querying with @Query, #Predicate, FetchDescriptor, or SortDescriptor; when configuring ModelContainer and ModelContext for SwiftUI or background work with @ModelActor; when planning schema migrations with VersionedSchema and SchemaMigrationPlan; when setting up CloudKit sync with ModelConfiguration; or when coexisting with or migrating from Core 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。
Hit the Cloudflare REST API directly for operations that wrangler and MCP can't handle well. Bulk DNS, custom hostnames, email routing, cache purge, WAF rules, redirect rules, zone settings, Worker routes, D1 cross-database queries, R2 bulk operations, KV bulk read/write, Vectorize queries, Queues, and fleet-wide resource audits. Produces curl commands or scripts. Triggers: 'cloudflare api', 'bulk dns', 'custom hostname', 'email routing', 'cache purge', 'waf rule', 'd1 query', 'r2 bucket', 'kv bulk', 'vectorize query', 'audit resources', 'fleet operation'.
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。
Query the OpenAI developer documentation via the OpenAI Docs MCP server using CLI (curl/jq). Use whenever a task involves the OpenAI API (Responses, Chat Completions, Realtime, etc.), OpenAI SDKs, ChatGPT Apps SDK, Codex, MCP integrations, endpoint schemas, parameters, limits, or migrations and you need up-to-date official guidance.
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 when the user asks to "optimize SQL queries", "explore database schemas", "generate migration SQL", "analyze query performance", or "document database 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。
This skill should be used when the user asks to "optimize Snowflake queries", "analyze Snowflake SQL performance", "size Snowflake warehouses", "review Snowflake data models", or "troubleshoot Snowflake cost issues".
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。
Operational billing execution for legal matters. Monthly bill prep and billing instructions, LC invoice review and disbursement treatment, client billing query responses, cashflow modelling (LC payment obligations vs client receipts), and leverage and burn analysis (staffing mix, predicted total cost, margin trajectory). Trigger on: 'prepare the bill', 'billing instruction', 'end of month billing', 'LC invoice', 'local counsel invoice', 'pass through as disbursement', 'client querying the invoice', 'billing dispute', 'cashflow gap', 'when will we get paid', 'LC payment due', 'leverage analysis', 'staffing mix', 'predicted total cost', 'burn rate by grade', 'are we on track', 'what will this matter cost'.
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。
Your team's shared, AI-ready knowledge base — people, companies, meetings, SOPs, and decisions structured so an agent can answer on your team's behalf. Team-scope sibling to second-brain. Modes — capture, compile (wiki pages + INDEX.md), query (trust-weighted, saved to outputs/), review (verify / deprecate / supersede stale captures), lint, connect, search. Structured raw dirs (people/, companies/, meetings/, sops/, decisions/, customer-language/, sales-objections/). Every capture stamps author, timestamp, and trust status. Optional sync from call transcripts, Slack/email exports, CRM. Vault at ${COMPANY_BRAIN_VAULT:-$HOME/Documents/CompanyBrain}/. Triggers on "/company-brain," "/cb," "capture this into the team brain," "log this meeting," "save this SOP," "compile the company wiki," "query the team brain," "what does the team know about X," "review the company brain," "lint the company brain," "who's the internal expert on X."
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 patterns for Godot 2D physics including collision layers/masks, Area2D triggers, raycasting, and PhysicsDirectSpaceState2D queries. Use when implementing collision detection, trigger zones, line-of-sight systems, or manual physics queries. Trigger keywords: CollisionShape2D, CollisionPolygon2D, collision_layer, collision_mask, set_collision_layer_value, set_collision_mask_value, Area2D, body_entered, body_exited, RayCast2D, force_raycast_update, PhysicsPointQueryParameters2D, PhysicsShapeQueryParameters2D, direct_space_state, move_and_collide, move_and_slide.
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。
Bevy ECS skill for spatial queries. Keywords: Bevy, raycast, shapecast, picking, physics query.
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。
Bevy ECS skill for entity composition. Keywords: Bevy, ECS, Component, Query, 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。
SwiftData skills — class inheritance patterns for hierarchical models, type-based querying, polymorphic relationships, and inheritance-vs-enum decisions. Use when designing SwiftData model hierarchies.
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 knowledge for Azure Resource Graph development including troubleshooting, best practices, decision making, limits & quotas, configuration, and integrations & coding patterns. Use when querying via CLI/PowerShell/REST, using GET/LIST vs Query, handling paging/quotas, or Power BI ARG reports, and other Azure Resource Graph related development tasks. Not for Azure Monitor (use azure-monitor), Azure Policy (use azure-policy), Cost Management (use azure-cost-management), Azure Resource Manager (use azure-resource-manager).
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。
Local user-profile maintenance skill for Codex, Claude Code, OpenClaw, OpenCode, and other agent harnesses. Use only when the user explicitly invokes $user-profile-keeper to create, initialize, update, query, correct, delete, export, or audit a local persistent user profile. It can extract durable collaboration preferences, requirement-expression habits, capability boundaries, recurring omissions, risk preferences, privacy boundaries, and typical events from the current session into auditable, confirmable, retractable local profile data. Do not auto-invoke, upload profile data, or replace task-clarifier's normal clarification flow.
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。
Find which ChatGPT search queries mention a given brand. Tests long-tail queries against ChatGPT's web-search-enabled model and reports which ones surface the brand. Use when the user asks to "find queries for [brand]", "check GEO visibility", "which queries mention [brand]", "geo query finder", "find AI mentions", or "test ChatGPT queries for [brand]".
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。
Find the papers that answer a research query in Firecrawl's research paper index — a corpus of paper abstracts whose largest share is biomedical and life-science literature (PubMed, bioRxiv, medRxiv), alongside arXiv preprints in CS, physics, and math — using semantic search, semantic and structural expansion, and in-body verification. Use this skill for literature-finding and paper-retrieval tasks of any kind, including clinical, biomedical, drug, gene, disease, and other life-science questions, whether the answer is a single paper or a full multi-paper set. The index is reached only through the `firecrawl_research_*` MCP tools or the `firecrawl research` CLI subcommands. Calling `firecrawl_search` with its `categories` option set to `["research"]` is a different feature — it filters ordinary web search to research-affiliated websites (the list includes PubMed, bioRxiv, medRxiv, arXiv, and publisher sites) and returns page results from them, without querying the paper records in this index.
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。
Apply MongoDB data-modeling, indexing, and query rules from access patterns. Use when designing schemas, choosing embed vs reference, or tuning MongoDB query behavior.
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。
基于参考文本校准问答对中的查询（问题）。当用户提到校准问题、校准查询、语言风格校准、问题优化等需求时使用此skill。 即使用户没有明确说出"校准"，只要任务涉及根据参考文本调整问答对中的问题，就应该使用此skill。
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。
Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.
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。
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。