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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

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 and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

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 for Nauthilus configuration loading, config schema and defaults, Viper-backed config formats, config dump behavior, vim syntax generation, template validation, prompt guideline sync, documentation under README/IDP/server docs, config converters, and generated documentation or syntax artifacts.

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。

Collect this machine's local Claude Code conversation data with the asz collector. Resolves the local Claude data directory (honouring CLAUDE_CONFIG_DIR and XDG_CONFIG_HOME), builds the binary, lists what is discoverable, and lands everything into the storage root. Use when asked to collect, backfill, re-collect, or inspect local Claude Code sessions, or to check what the collector can see.

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。

Model sports rules, event formats, prediction types, scoring implications, lock rules, data requirements, and provider constraints for FriendlyBet. Use when adding or comparing sports such as football, basketball, American football, tennis, tournaments, leagues, playoffs, brackets, weekly picks, or World Cup-specific logic.

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。

Transforms user prompts into optimized prompts using established frameworks (RTF, RISEN, Chain of Thought, RODES). Analyzes intent, selects framework, and produces structured, ready-to-use prompts.

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。

Generate or refresh docs/ARCHITECTURE.md from current codebase state with evidence-backed component and data-flow mapping

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。

Provision database infrastructure, initialize migration framework, configure environments, and verify connectivity/migration gates

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。

Analyzes codebase structure, data flow, module relationships, and key patterns to generate and maintain a living architecture document (ARCHITECTURE.md).

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 migrations and schema versioning. Use when planning migrations, version control, rollback strategies, or data transformations for any database paradigm.

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。

Define system architecture, error architecture, and data strategy with map guards and ideation context reload

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。

Systematic methodology for verifying that IA, BE, and FE specification layers are mutually consistent. Covers coverage matrices, field mapping checks, error code propagation, access control consistency, and data contract drift detection.

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。

InnoLive 팀 LLM 위키에 확인된 세션 사실을 반영하고 검증한 뒤 요청 범위에 맞춰 커밋과 PR까지 진행한다. 위키 문서화·업데이트 요청에 사용한다. 위키 검색·질문이나 코드 레포 수정에는 사용하지 않는다.

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。

Bootstrap a new Gradle module in any Kotlin Android project. Walks the user through choosing one of three module types — library module (`com.android.library`), feature module (regular Android library that hosts a feature/UI, install-time only), or dynamic feature module (`com.android.dynamic-feature`, on-demand/conditional/instant delivery). Use whenever the user wants to add a module, modularize code, extract a util/data/ui layer, create a new feature, set up Play Feature Delivery, or asks "should this be a library or feature module?". Discovers the project's existing conventions (plugin aliases, namespaces, sibling modules, version catalog) instead of hardcoding any. Pushes back on bad combos (e.g. utility code in a dynamic feature). Stops at scaffolding; app-level wiring is out of scope.

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 this skill when working on camera, gallery, file pick/upload/download, file preview, sharing, or filesystem-backed media. For Preferences keys, use mobile-local-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。

Use this skill when changing persisted local data, storage keys, caches, or any offline/sync 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。

Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling

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。

Zod 4 — TypeScript-first schema validation with static type inference. Use when writing Zod schemas, validating data, defining types with Zod, parsing input, creating form validation schemas, defining API request/response schemas, working with z.object, z.string, z.number, z.enum, z.array, z.union, z.discriminatedUnion, z.file, z.jwt, z.email, z.uuid, z.url, z.codec, z.toJSONSchema, z.fromJSONSchema, z.int, z.stringbool, z.templateLiteral, z.record, z.partialRecord, or any other Zod API. Also use when migrating from Zod 3 to Zod 4, or when the user's package.json shows zod@^4. CRITICAL: Always use Zod 4 APIs. Never use deprecated Zod 3 patterns unless user explicitly requests Zod 3 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。

TanStack Query (React Query) v5 best practices for data fetching, caching, mutations, and server state management. Use when building data-driven React applications, setting up query configurations, implementing mutations/optimistic updates, configuring caching strategies, integrating with SSR, or fixing v4→v5 migration errors.

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 Personal Assistant network skill for multi-agent PA coordination. Use when: contacting another PA, coordinating with peer agents, scheduling meetings between owners, broadcasting messages to PA groups, or looking up contacts from the local PA directory. Reads contact data from data/pa-directory.json in the workspace.

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 Personal Assistant network skill. Use when: contacting another PA, coordinating with peer agents, scheduling meetings between owners, broadcasting messages, looking up contacts from the local PA directory, or performing any cross-PA task. Reads contact data from data/pa-directory.json in the workspace. Does NOT contain hardcoded contact data — each PA maintains their own directory file.

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。

Single-skill wrapper for the 4-layer recall (durable memory → semantic DB → PostgreSQL → daily notes). Use BEFORE saying "I don't have context". Triggers: "search memory", "what do we know about X", "deep recall", "I don't remember".

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。

Turns data/commitments.jsonl (flat list) into a who-owes-what-to-whom graph. Surfaces overdue chains and cross-PA dependencies that the flat list hides. Triggers: "what's overdue", "what did X promise", "commitment graph", "who's blocking whom".

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 a monday.com account for an OpenClaw agent and work with monday.com boards, items, and updates via the GraphQL API or MCP server. Use when: creating a monday.com workspace for a PA, connecting the PA to monday.com, querying boards and items, creating or updating items, troubleshooting monday.com API access, self-registering an agent on monday.com via HATCHA agent verification, or integrating with monday.com workflows. Covers GraphQL cookbook, column types, MCP configuration, and HATCHA self-registration. Works with any LLM model.

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。