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概要と使いどころ
Use when a user requests the reasoning behind one matched session by query or handle. Not for peer transfer: use history-sync. Not for store registration: use history-source-registry.
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。
Resolve `.mighty/loro/*.loro` merge conflicts safely using `mt merge`, then verify store health and sync.
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 the local Odylith projection store after concrete anchors exist.
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 concrete anchors already exist and the local Odylith projection store needs 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。
Create knowledge references from URLs, documents, or files for use by AI agents. Fetch, process, and store structured references in the configured references directory (default: .ai-factory/references/).
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 guide + benchmarked pitfalls for building Monte-Carlo / Simulated Annealing refinement layers on top of greedy hex-grid layouts for code visualization (packages/gui/src/store/hexLayout.ts, sandbox/hex-sandbox, future Rust ports). Use when: (1) planning to add MC/SA on top of an existing hex-grid pack, (2) deciding whether a simpler SA would match a hierarchical MC with force-directed + chain drag, (3) budgeting wall-clock for million-node cold-start, (4) debugging "blob turns Swiss-cheese" or "folders stop nucleating" during MC refinement, (5) choosing between folder cohesion and raw Σ-link-length as cost function. Contains four hard-won, quantified lessons and anti-patterns that would take a full day of experiments to rediscover.
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。
Decide what to PERSIST in your data structures (graph nodes, DB tables, cache layouts) based on the QUERIES you must answer, not the DEFINITIONS that justify the data. Definitions tell you logical identity; storage strategy is independent. Materializing every definitional component leads to O(N×M) blow-ups that masquerade as memory leaks. Use when: (1) designing a new graph-node type, edge type, DB table, or cache layout; (2) about to name a `subgraph` / `comprises` / `members` / `contains` collection on a parent entity; (3) memory growth proportional to (entities × per-entity-elements); (4) "memory leak" debugging that survives multiple targeted patches — the leak may be storage shape, not retention; (5) pre-implementation sketch includes "for each X, store all reached nodes"; (6) research/spec doc says "X = ⟨A, B, C⟩" and you're tempted to make it three storage edges per 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。
Diagnose and fix RFDB data disappearing after compaction, where rfdb-server reports a tiny node count (e.g. "15 nodes") on startup despite manifest_index.json showing hundreds of thousands of nodes and segment files existing on disk. Use when: (1) rfdb-server logs "Default database: N nodes" with N orders of magnitude smaller than recent analysis output, (2) /api/stats returns only the most recent commit's nodes, (3) the issue appears AFTER a compaction event (manifest with l1_node_segments populated and node_segments: []), (4) subsequent commits dropped L1 references. Root cause: ManifestStore::create_manifest() initializes l1_node_segments/l1_edge_segments to Vec::new() instead of cloning from current manifest, so any commit AFTER compaction silently orphans all L1 data even though segment .seg files still exist on disk. Compaction injects L1 fields explicitly after create_manifest, but regular commits do not.
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。
Handles NoSQL document operations and collection hierarchy exploration. Use for CRUD tasks and data retrieval. Provides flexible document manipulation and structured querying.
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 wants to run SQL - especially analytical SQL - on local files (parquet/csv/json), URLs, S3 paths, or remote databases (Postgres, MySQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake) without setting up a server. Provides chDB - embedded ClickHouse SQL in Python with 1000+ functions, Session for stateful multi-step pipelines, parametrized queries, and cross-source joins via `s3()`, `mysql()`, `postgresql()`, `iceberg()`, `deltaLake()`, `remoteSecure()` table functions. TRIGGER when: user wants SQL on parquet/csv/files or across remote analytical sources; uses ClickHouse SQL features (window functions, windowFunnel, geoToH3, JSON path ops, Session, parametrized queries); imports `chdb` or calls `chdb.query()`. SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.
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 knowledge references from URLs, documents, or files for use by AI agents. Fetch, process, and store structured references in the configured references directory (default: .ai-factory/references/).
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。
Store, find and curate lasting facts in the cognitive memory map.
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。
Reads, searches and counts Claude Code session transcripts (the .jsonl files under ~/.claude/projects, subagents too) with the csift command-line tool, and ships hooks that re-inject compacted turns or record pending questions. Use when asked what a past or running session said, did, edited, spent or is doing now; when about to grep, jq or script over a transcript jsonl (a type=="user" filter overcounts the person's messages), count how many messages the person typed, tool calls or tokens, or claim something was never done or decided; to rebuild a lost file or plan from what a session wrote, extract pasted images, restore turns a compaction summary lost, find a subagent's own id when CLAUDE_CODE_SESSION_ID names the parent, wait on or message another session or subagent; or when csift prints "unknown label selector", "show needs an address", "looks like a session/agent id, not a project path" or "0 matches — a DEFINITIVE absence". Not for semantic or embedding search: csift matches regular expressions only.
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。
Connect every external account NowStack Mobile needs (Convex, Expo/EAS, Apple Developer, Vercel, Stripe, Google Play) by acquiring PROGRAMMATIC API ACCESS for each, then doing everything automatically through that access. Use for "setup accounts", "connect my accounts", first-run onboarding, or when check-setup reports CLI/auth gaps. The skill obtains a token/key per service, stores it in the right place, and only asks the user for the irreducible manual steps (browser signup, 2FA, one-time key download). Every account is skippable.
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 a lean, mobile-aware Product Requirements Document for a NowStack Mobile app through interactive discovery, then save it to .agents/plan/PRD.md. Use for "write a PRD", "ns prd", "define the product", "plan my app", or as the first planning step before ns-architecture and ns-tasks. Mobile-flavored — personas, onboarding/paywall flow, platforms (iOS/Android/web), IAP vs subscription monetization, store scope. Feeds ns-init-project (site-config) and the rest of the planning pipeline.
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。
Perform a read-only NowStack Mobile launch audit across product, web, iOS, Android, backend, auth, payments, email, storage, analytics, stores, and operations. Returns an evidence-backed GO or NO-GO verdict.
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。
Prepare and submit NowStack Mobile iOS App Store releases. Use for real 6.5-inch app screenshots, metadata, IAP, review details, validation, and submit.
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。
One-shot NowStack Mobile Android deployment with GPC Google Play Console CLI. Use when preparing Play Store releases, uploading AABs, syncing listings/screenshots, monitoring rollout health, or submitting Android without EAS Submit.
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。
Programmatic canvas toolkit for creating, editing, and refining Excalidraw diagrams via MCP tools with real-time canvas sync. Use when an agent needs to (1) draw or lay out diagrams on a live canvas, (2) iteratively refine diagrams using describe_scene and get_canvas_screenshot to see its own work, (3) export/import .excalidraw files or PNG/SVG images, (4) save/restore canvas snapshots, (5) convert Mermaid to Excalidraw, or (6) perform element-level CRUD, alignment, distribution, grouping, duplication, and locking. Requires a running canvas server (EXPRESS_SERVER_URL, default http://127.0.0.1:3000).
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。
Make a short product film (website hero loop, landing section, launch film, feature walkthrough, store-listing promo, social cut, illustrated story film) in which the product appears as its own real UI, drawn frame by frame from an HTML timeline, rendered with Playwright and cut with ffmpeg. Four routes: licensed footage cut against cinematic product beats, product beats only, a pointer-driven walkthrough with captions or text cards (plain or kinetic), or a cut-paper collage in which an illustrated cast tells a story over real UI captured from the code with fictional data. Use when asked for a product film, hero video, promo or demo video, launch film, explainer, walkthrough video, paper or collage film, or to recut one. Not for plain screen recordings or voice-over walkthroughs; laying a supplied music track under a finished cut is in 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。
Work with the @upstash/blob TypeScript/JavaScript SDK for S3-compatible object storage with direct browser uploads, presigned URLs, multipart, and signed reads. Use when storing files or blobs, uploading avatars, images, videos, attachments or user documents, letting a browser upload straight to storage without proxying bytes through a server, generating public or time-limited signed URLs, serving private files, streaming large files with pause and resume, setting cache headers on stored objects, or reaching an S3-compatible bucket from the AWS SDK.
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。
Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model, choosing dense, sparse, or hybrid indexes, filtering by metadata, organizing data with namespaces, running resumable queries, or connecting Upstash Vector to an AI or LLM application. Also use when the user asks for a vector store, vector search, nearest-neighbor or kNN search, embeddings storage, semantic cache, recommendations or similarity features, or a hosted vector index that needs no infrastructure.
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。
GreptimeDB provider for obz. Covers PromQL metric query, labels, label-values, and series commands via GreptimeDB's Prometheus-compatible API. Use this skill when the user mentions "GreptimeDB", "Greptime", "obz metric -p greptimedb", or needs to query metrics stored in a GreptimeDB instance.
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。
Makers 开发专家团 AI Agent 工程师 Skill — Claude Agent SDK、 OpenAI Agents SDK、LangGraph、DeepAgents、CrewAI 框架集成， SSE 流式响应、conversation store、EdgeOne Makers Agent Runtime。 触发词：AI Agent、聊天机器人、LLM 应用、多 Agent 系统、 Claude SDK、OpenAI Agents、LangGraph、CrewAI、SSE 流式、 智能助手、AI 推理端点。
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。