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概要と使いどころ
Auto-activate for pytest_databases, Docker DB fixtures, PostgreSQL/pgvector/ParadeDB, MySQL/MariaDB, Oracle/SQL Server, CockroachDB, MongoDB, Redis/Valkey, BigQuery/Spanner, MinIO, or RustFS. Not for mocked DBs.
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。
Runs a team of 50 LLM personas through a 30-round structured debate to predict market direction. Uses the Canvas Agent Swarm Service — the same shared infrastructure other skills use for smaller agent teams. Output is an influence-weighted consensus direction + trade recommendation with a transparent record of every argument, research query, and cross-examination.
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。
Route database tasks to the right library skill. Covers migrations, query optimization, ORM, transactions.
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。
法律事项的运营性计费执行。月度账单准备和计费指示、LC 发票审查和代垫款处理、客户账单质询回应、现金流建模（LC 付款义务与客户收款对比）、杠杆和消耗分析（人员配置组合、预测总成本、利润轨迹）。触发词：‘prepare the bill’（准备账单）、‘billing instruction’（计费指示）、‘end of month billing’（月末计费）、‘LC invoice’（LC 发票）、‘local counsel invoice’（本地顾问发票）、‘pass through as disbursement’（作为代垫款转付）、‘client querying the invoice’（客户质疑发票）、‘billing dispute’（计费争议）、‘cashflow gap’（现金流缺口）、‘when will we get paid’（我们何时能收到付款）、‘LC payment due’（LC 付款到期）、‘leverage analysis’（杠杆分析）、‘staffing mix’（人员配置组合）、‘predicted total cost’（预测总成本）、‘burn rate by grade’（按职级消耗率）、‘are we on track’（我们在轨道上吗）、‘what will this matter cost’（此事项将花费多少）。
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。
Capture, gate, query, and render long-term project knowledge through the gated `mewkit wiki` subsystem. Use to create a wiki, propose/approve candidates (scanner-gated), hand off a skill's terminal artifact as a scanned candidate, recall context, search the FTS index, or list pages. Agents may only PROPOSE candidates; canonical pages are written only via human approve. NOT for short-lived JSON memory (see mk:memory); NOT for fetching external sources (see mk:wiki-research); NOT for HTML snapshots (see mk:wiki-render).
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。
Structured logging conventions for DraftForge backend (Django/Celery/Daphne) and frontend (React). This skill should be used when adding log statements, creating new modules, debugging with logs, or reviewing logging patterns. Covers structlog usage, system/subsystem taxonomy, required fields, log levels, and Grafana query patterns.
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。
Manage database schema with Drizzle ORM and SQLite migrations. Use when adding tables, modifying columns, creating indexes, or running migrations. Activates for database schema changes, migration generation, and Drizzle query patterns.
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。
Plan database migrations before executing them: risk assessment, dependency mapping, multi-service sequencing, rollback budgets, and stakeholder communication plans. Use when deciding HOW to migrate, not when writing the actual DDL. NOT for: writing migration SQL (use database-migration-manager), data ETL pipelines (use data-migration-specialist), query optimization, or ORM configuration.
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。
Schema design, normalization decisions, indexing strategies, and migration safety for relational databases. Use when designing tables, choosing between normalize vs denormalize, selecting index types, planning zero-downtime migrations, or setting up connection pooling. Activate on "schema design", "normalization", "denormalization", "foreign key", "index strategy", "migration", "connection pooling", "composite key", "surrogate key", "soft delete", "polymorphic association". NOT for PostgreSQL-specific query tuning or EXPLAIN analysis (use postgresql-optimization), NoSQL or document database design, or cloud database provisioning.
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。
Safe database migration manager for zero-downtime DDL changes and rollback plans. Activate on: database migration, schema change, DDL, rollback plan, zero-downtime migration, Prisma migrate, Drizzle kit, Flyway, column rename, table alter. NOT for: query optimization (use database-optimizer), ORM modeling (use data-pipeline-engineer), backup/restore (use devops-automator).
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。
PgBouncer, connection optimization, and pooling strategies for database performance. Activate on: connection pool, PgBouncer, database connections, pool size, connection limit, Prisma pool, Drizzle pool. NOT for: query optimization (use data-warehouse-optimizer), database schema design (use dimensional-modeler).
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。
Redis caching patterns, cache-aside, write-through, TTL strategies, and invalidation. Activate on: caching, Redis, cache invalidation, cache-aside, write-through, TTL, CDN cache, stale-while-revalidate. NOT for: CDN/reverse proxy setup (use api-gateway-reverse-proxy-expert), database query optimization (use data-warehouse-optimizer).
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。
Design multi-tier caching architectures for web applications — cache-aside vs write-through vs write-behind, TTL design, cache invalidation, Redis patterns, CDN configuration, browser caching, and stampede prevention. Use when choosing a caching pattern, designing cache invalidation strategies, implementing Redis caching, configuring Cache-Control headers, or preventing cache stampedes. Activate on "cache invalidation", "cache-aside", "write-through", "TTL", "Redis cache", "CDN caching", "cache stampede", "stale data", "browser cache". NOT for database query caching within an ORM, memoization of pure functions, or CPU-level caching.
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。
Refactor apps so backend owns runtime truth and UI only projects snapshots. Use when tools have split authority. NOT for cosmetic UI work or new features before drift is removed.
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 Ensembl REST API for gene/transcript/protein lookup, sequence retrieval, comparative genomics (Compara), variant effect prediction (VEP), regulatory features, and cross-species ortholog/paralog calls. Use when pulling Ensembl-native data (Ensembl Gene IDs, version-pinned releases, archive endpoints for reproducibility), gene/transcript/exon structure with stable IDs, or VEP for variant annotation. Encodes the 15 req/sec rate limit, archive (e110.rest.ensembl.org) for reproducibility, Ensembl divisions (vertebrates / plants / fungi / metazoa / bacteria), and the symbol-vs-ID stability problem.
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 UpSet plots to visualize set intersections beyond 4 sets (where Venn fails) using ComplexUpset (modern, ggplot2-grammar) or the unmaintained UpSetR, with explicit cardinality vs degree sorting, attribute panels, and query highlighting. Use when comparing overlap across many gene sets, peak sets, variant lists, or any set membership matrix where Venn diagrams become illegible.
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。
Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma, Schwede & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2021 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.
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 BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.
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。
Interactive Archon integration for knowledge base and project management via REST API. On first use, asks for Archon host URL. Use when searching documentation, managing projects/tasks, or querying indexed knowledge. Provides RAG-powered semantic search, website crawling, document upload, hierarchical project/task management, and document versioning. Always try Archon first for external documentation and knowledge retrieval before using other sources.
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。
The primary tool for code-navigation questions in ANY language (frontend or backend) — walk the structural graph from an entry point (page, route, handler, command, `main`) down through imports to the IO boundary (network call, query, file read). Use this skill, in preference to the Explore agent or global grep, whenever the user asks "where does X come from?", "what API/data populates Y?", "what renders or wraps the Z panel?", "which file for feature F?", "how is this wired up?", or any code-navigation question mapping a UI element, route, or feature concept to source files. Also reverse queries ("what uses /api/foo?", "who consumes this service?"). Keyword search routinely picks up unrelated flows sharing a domain word and produces wrong answers; the walk follows the actual graph. The JS/TS `walk.mjs`/`discover.mjs` are an optional accelerator, not required — the walk is language-agnostic.
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。
Automate Attio CRM operations -- search records, query contacts and companies with advanced filters, manage notes, list attributes, and navigate your relationship data -- using natural language through the Composio MCP integration.
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。
Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.
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。
Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
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。