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概要と使いどころ
How to change the database schema in this repo. Current engine: node-pg-migrate, with SQL generated from Drizzle schema changes when possible. Load whenever you add/alter/drop a table, column, enum, index, constraint, RLS/function/grant, or any file under packages/db/migrations.
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。
Hourly warehouse health sweep for {{warehouse_schema}}. Checks every monitored table's freshness against its SLA, compares row counts to the table's own trailing baseline, diffs the schema for drift, and checks for failed or stalled loads. Posts anomalies to {{alert_channel}} and drafts a GitHub issue in {{incident_repo}} with the likely cause. Read-only across the warehouse — never modifies data, tables, or pipelines.
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。
Legal document drafting -- contracts, memos, briefs, complaints, demand letters, opinions, discovery, settlements, ToS, privacy policies. Full pipeline: document structure, per-section writing, Bluebook citation, case law lookup (CourtListener API), regulation lookup (eCFR API), DOCX output, and TDD-style verification (defined terms, cross-references, placeholders, boilerplate, citation format). Triggers on: 'draft a contract', 'write a legal memo', 'create an NDA', 'write a brief', 'legal document about', 'draft a complaint', 'terms of service', 'privacy policy', 'demand letter', 'settlement agreement', 'legal opinion', 'discovery requests', any request to produce a legal or law-related document.
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 shared-inbox triage runbook — how to read a new inbound email, label it, look up {{help_doc}} for a standard answer, draft a reply in Gmail or file a ticket in {{linear_team}}, and when to leave a thread label-only for a human.
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。
Ethical people-finding and identity verification via parallel research agents across people-search sites, social media, public records, and reverse phone/email/image/username lookups, with confidence-scored results requiring 3+ matching identifiers. USE WHEN find person, locate person, reconnect, lost contact, old friend, reverse phone lookup, who owns this email, reverse image search, find by username, verify identity, people search, public-data background check, who is this caller. NOT FOR structured due-diligence or company/entity intelligence investigations, or general web research synthesis (use Research).
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。
Detect, validate, or generate Schema.org JSON-LD for a supplied page or entity. Use only when structured data or rich-result markup is requested.
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 image generation for SEO assets: OG/social preview images, blog hero images, schema images, product photography, infographics. Powered by Gemini via nanobanana-mcp. Requires banana extension installed. Use when user says "generate image", "OG image", "social preview", "hero image", "blog image", "product photo", "infographic", "seo image", "create visual", "image-gen", "favicon", "schema image", "pinterest pin", "generate visual", "banner", or "thumbnail".
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。
Core package for defining schemas, catalogs, and AI prompt generation for json-render. Use when working with @json-render/core, defining schemas, creating catalogs, or building JSON specs for UI/video generation.
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。
Professional email writing expert for tone, structure, clarity, and business communication
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。
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。
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific proof obligation benefits from formal verification; use proof-writer for ordinary mathematical drafting.
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。
Rigorous mathematical proof verification and fixing workflow. Reads a LaTeX proof, identifies gaps via fresh-agent Codex GPT-6-Astra ultra review, fixes each gap with full derivations, re-reviews, and generates an audit report. Base review is same-family provisional. Use when user says "检查证明", "verify proof", "proof check", "审证明", "check this proof", or wants rigorous mathematical verification of a theory paper.
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。
Rigorous mathematical proof verification and fixing workflow. Reads a LaTeX proof, identifies gaps via cross-model review (external reviewer backend, ultra reasoning), fixes each gap with full derivations, re-reviews, and generates an audit report. Use when user says "检查证明", "verify proof", "proof check", "审证明", "check this proof", or wants rigorous mathematical verification of a theory paper.
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。
Draft, rewrite, or condense emails with target tone, length, and audience; use for cold outreach, replies, status updates, or escalations where clarity and brevity matter.
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。
Draft Gmail in the user's own voice from the voice profile built by email-voice-profile. Drafts only; the user reviews and sends.
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。
Draft a reply, a follow-up, or a new email in Superhuman in the user's own voice — read the whole thread first, reuse an existing draft instead of adding a second one, keep the right people on the thread, and leave sending to the user unless they asked for it. Use when the user says reply to, answer, follow up with, draft an email to, or send, or when a triage turned up threads that need a response.
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。
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.
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。
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
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。
Optimizes SQL queries, designs database schemas, and troubleshoots performance issues. Use when a user asks why their query is slow, needs help writing complex joins or aggregations, mentions database performance issues, or wants to design or migrate a schema. Invoke for complex queries, window functions, CTEs, indexing strategies, query plan analysis, covering index creation, recursive queries, EXPLAIN/ANALYZE interpretation, before/after query benchmarking, or migrating queries between database dialects (PostgreSQL, MySQL, SQL Server, Oracle).
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。
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
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 designing GraphQL schemas, implementing Apollo Federation, or building real-time subscriptions. Invoke for schema design, resolvers with DataLoader, query optimization, federation directives.
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。
Draft clear, well-structured emails — replies, outreach, and follow-ups — in the right tone.
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。
Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.
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。