データ・AIのスキル

Analyze Ascend NPU computation-side profiling data for single-card runs or a selected rank from multi-card runs. Use this skill when the user asks to diagnose computation bottlenecks, AI Core / AI Vector / AICPU hotspots, dynamic shape overhead, block dim issues, redundant TransData/Transpose/Cast, cross-stream waits, frequency/runtime-state issues, or fusion opportunities.

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。

Analyze Ascend NPU collective communication profiling data with a DB-first workflow. Use when the user provides `cluster_analysis_output/cluster_analysis.db`, rank-level `analysis.db`, rank-level `ascend_pytorch_profiler_{rank_id}.db`, together with `profiler_info.json`, and asks about HCCL or hcom communication cost, collective communication TOP ops, wait time, slow rank/straggler, Notify Wait, bandwidth, retry, relay, SDMA/RDMA/HCCS links, communication matrix, or Ascend communication fault 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。

Analyze Ascend NPU schedule, operator dispatch, operator launch, and Host Bound profiling issues in Ascend profiling data. Use when need to diagnose device Free time, framework/operator dispatch latency, launch latency, PYTORCH_API/CANN_API launch gaps, aclrtSynchronizeStream stalls, task queue behavior, CPU scheduling interference, GC/lock pauses, CPU affinity, or schedule-side optimization actions.

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。

Compares two Prof datasets (GPU vs NPU / NPU vs NPU) by auto-installing and calling msprof-analyze compare, then parses the result xlsx into HTML report and Chinese xlsx. Invoke when user provides two prof dirs to compare, or has a compare result xlsx to analyze/translate.

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。

Integrate or audit privacy-safe PostHog analytics across NowStack Mobile Expo and TanStack web surfaces. Use for event design, consent, identity, dev/prod separation, funnels, feature flags, or analytics verification.

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。

Integrate PostHog analytics into Expo apps. Use for posthog-react-native setup, providers, screen tracking, env config, identify calls, and error tracking.

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。

Optimize NowStack Mobile across Convex, Expo, and TanStack Start. Use for performance, state, subscriptions, rendering, routing, forms, mobile UI, bundle, or data-fetching improvements.

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。

Summarize any block of text using the `summarize` CLI (chained-in from steipete/summarize — npm package `@steipete/summarize`). Use whenever the user pastes a long piece of content and asks for a TL;DR, a summary, a digest, a gist, a condensed version, or key points — even if they don't explicitly say "use summarize". Also chains into `mktg-transcribe --summarize` for audio/video TL;DRs and source-digestion workflows to condense long articles. Requires the `summarize` CLI to be installed (`mktg doctor` will tell you if it's missing). Do NOT trigger for structured data (JSON/CSV/code), for text already shorter than ~500 words, or when the user wants the full content preserved.

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。

Call Exa Contents (POST /contents) for LLM-ready extraction from known URLs: text, highlights, summaries, links, image links, subpages, freshness-controlled crawl. Use when the agent already has URLs. Prefer Exa MCP web_fetch_exa when available; otherwise raw HTTP with EXA_API_KEY. NOT for open-ended discovery (use exa-search) or auth-walled X/Twitter (use mktg-x). For full-site crawl of a known domain, firecrawl may be better.

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。

Map who is winning an SEO market with measured data — recurring SERP domains, organic footprints, winning content themes, backlink authority, and the gaps worth attacking. Use this skill for market-level questions ("who ranks for X", "how competitive is this space", "where are the SEO openings") before committing to keyword targets or content themes. For one named competitor, use openseo-competitor-analysis instead. Triggers: "competitive landscape seo", "who ranks for", "seo market map", "how competitive is", "serp competitors".

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。

Enhance an existing marketing plan with parallel research. Use when the agent has a draft plan (brand strategy, campaign brief, content calendar, launch plan) that needs strengthening with real data. Triggers on 'deepen this plan', 'strengthen this strategy', 'research gaps in my plan', 'make this plan better', 'this plan is too surface level', 'add research to this', 'validate this plan', 'back this up with data', or when a plan exists but lacks audience data, competitive positioning, or keyword strategy. Make sure to use this whenever someone has an EXISTING plan that feels thin or unresearched — even if they just say 'is this plan good enough?' or 'what's missing here?', they likely need deepening. This skill ENHANCES existing plans — it does not create new ones. If no plan exists, route to /brainstorm or /launch-strategy first.

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 professional brand-quality product images via Higgsfield's mode-specific prompt enhancement pipeline. Entry point for any product visual with a specific format or platform target. Use whenever: "product photoshoot", "lifestyle product shots", "Pinterest pin", "hero banner", "ad pack", "virtual try-on", "studio shot", "carousel images", "Meta ads creative", "model wearing product", "levitating product", "splash shot", "CGI style product", "restyle product image", Shopify image, brand campaign visual. 10 modes: product_shot, lifestyle_scene, closeup_product_with_person, moodboard_pin, hero_banner, social_carousel, ad_creative_pack, virtual_model_tryout, conceptual_product, restyle. Backend assembles the prompt — never call gpt_image_2 directly for product shots. NOT for: one-off images without a product (use image-gen), branded video with avatars (use higgsfield-generate), Soul training (use higgsfield-soul-id). Requires Higgsfield CLI and authed account.

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/search TypeScript/JavaScript SDK, a serverless full-text and semantic search database with built-in reranking. Use when adding search to an app or site, creating a search index, upserting documents with searchable content and filterable metadata, running keyword, semantic, or hybrid search queries, reranking results, filtering with SQL-like or structured filter syntax, paginating with range, fetching or deleting documents, resetting an index, or checking index info. Also use when the user asks for site search, product, document, or knowledge-base search, or a managed search service that needs no cluster to run.

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。

Translates natural language data intents into syntactically valid Perfetto SQL queries and executes them against a local trace file. Use this skill to extract slice, thread, or memory data from Android Perfetto traces using trace_processor.

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 complete Go development skill. REQUIRED for ALL Go work — writing, refactoring, reviewing, generating, scaffolding, or discussing Go code, including any change to .go files. Use whenever the user mentions Go, Golang, go.mod, a Go package, or asks to build a Go backend service, CLI, library, script, MCP server (Model Context Protocol), or AI/LLM agent with tool calling or ReAct-style reasoning. Routes by project type to the right architecture, conventions, and review checklist. Highly opinionated; overrides generic Go style and lint guidance on conflicts.

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。

Rules for building offline-first Progressive Web Apps that launch instantly from the home screen. Use when building, reviewing or debugging a PWA, or anything touching a service worker, a web app manifest, precaching, an in-app update prompt, iOS home screen behaviour, or safe-area/notch layout.

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。

最新AI技術動向のレポート（最新 / 週次 / 月次 / 特定トピック別）を Web 検索・WebFetch・GitHub/Hugging Face MCP で調査して生成し、Yagami360/ai-product-dev-tips リポジトリの GitHub Issue として投稿するスキル。ユーザーが「AI技術のキャッチアップ」「最新AIニュースまとめ」「週次/月次のAI動向レポート」「〇〇（例: AI Agent, RAG, VLM, Physical AI, Claude Code）についての技術調査レポート」「AI Tech Catchup レポートを作って」などを求めたとき、たとえ "skill" や "Issue" という単語が無くても必ずこのスキルを使うこと。直近のAI業界の最新情報を調べてまとめてほしい・GitHub Issue 化してほしいという依頼全般で発動する。AI とは無関係な一般的な技術調査や、このリポジトリへの Tip 追加（ai-product-dev-tips-create スキルの領分）には使わない。

TypeScript and JavaScript performance and code-quality rules, usable in any framework. Use when writing, reviewing, or refactoring hot paths, data-structure choices, async flows, or bundle-size-sensitive code. Triggers include "optimize this function", "too slow", "reduce allocations", "bundle size", "lazy load", "speed up imports", or general code review of utilities and services.

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 marketplaces for AI models and prompts. TRIGGERS - Use when user needs help with ai-marketplace-builder related tasks.

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 ML pipeline orchestration systems. TRIGGERS - Use when user needs help with ai-pipeline-orchestrator related tasks.

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。

Optimize embedding strategies for AI retrieval. TRIGGERS - Use when user needs help with ai-embedding-optimizer related tasks.

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 AI data deduplication systems. TRIGGERS - Use when user needs help with ai-deduplication related tasks.

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 data privacy systems for AI. TRIGGERS - Use when user needs help with ai-data-privacy-system related tasks.

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 data pipelines for AI model training. TRIGGERS - Use when user needs help with ai-data-pipeline related tasks.

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。