3,397 件 ・ 関連度順
概要と使いどころ
Screen and evaluate A-share investment opportunities. Adapts the original deal-screening skill for Chinese market data sources, A-share screening criteria, and domestic deal origination. Triggers on "A股标的筛选", "标的筛选", "screening China", "deal screening A-share", "筛选投资标的", or "screen for [criteria]".
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 stock screening and investment idea sourcing for A-share markets. Combines quantitative screens, thematic research, and pattern recognition to surface new long and short ideas. Adapted from the original idea-generation skill for Chinese market conventions and data sources. Triggers on "A股选股", "股票筛选", "寻找机会", "stock screen China", "A-share ideas", "find ideas for Chinese stocks", or "screen for A-share 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。
Discover Civitai models with the BUILT-IN download_model action:"search_civitai" and install/generate them locally. Find a checkpoint/LoRA/embedding on Civitai, download it into ComfyUI, and use its trigger words. Optionally pair the official Civitai MCP for community features (images browsing, posting, collections).
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。
Survey the codebase, pick ONE high-leverage deepening opportunity (filtering out anything already proposed), and publish it as a PRD-shaped GitHub issue via /to-prd-project. Designed to run unattended in the daily architecture-review workflow.
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。
Generates comprehensive test specification with unit tests, UI tests, accessibility testing, and beta testing plan. Creates TEST_SPEC.md from PRD and implementation specs. Use when creating QA strategy.
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。
Discover and validate product ideas, analyze markets, scope MVPs, and optimize app store presence for iOS/macOS apps. Use when user asks to discover, validate, assess, scope, or analyze product ideas, market opportunities, or when they mention "product agent", "app idea validation", "should I build this", "MVP", "market analysis", or "ASO".
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 test templates for unit tests, integration tests, and UI tests using Swift Testing and XCTest. Use when adding tests to iOS/macOS apps.
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。
Deep competitive analysis for iOS/macOS apps including feature comparison, pricing analysis, strengths/weaknesses, market positioning, and differentiation opportunities. Use when user asks for competitive analysis, competitor research, feature comparison, market positioning, or wants to understand competition in detail.
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。
Deep market analysis for iOS/macOS apps including market sizing (TAM/SAM/SOM), growth trends, market maturity, entry barriers, distribution channels, and revenue potential. Use when user asks for market research, market size, market opportunity, growth potential, TAM/SAM/SOM, or market trends.
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。
Growth skills for indie Apple developers — user acquisition, analytics interpretation, press/media outreach, community building, and indie business operations. Also covers the stage-by-stage store-growth audit and the post-launch store-signals loop. Use when user asks about growing their app, auditing growth levers, understanding metrics, getting press coverage, or running an indie dev business.
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。
Survey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for OTHER models/agents to execute. Strictly read-only on source code — never implements, fixes, or refactors anything itself. Use when asked to audit a codebase, find improvement opportunities (bugs, security, performance, test coverage, tech debt, migrations, DX), suggest features or where to take the project next (roadmap, product direction), or generate handoff plans for another agent to implement.
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。
Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when working with Unity Catalog, Lakehouse/Lakeflow, Lakebase, AI Runtime/model serving, or external connectors, and other Azure Databricks related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Machine Learning (use azure-machine-learning), Azure Data Factory (use azure-data-factory).
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。
Expert knowledge for Azure Digital Twins development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when modeling DTDL graphs, integrating IoT Hub/ADx, configuring endpoints/routes, securing access, or tuning query units, and other Azure Digital Twins related development tasks. Not for Azure IoT Hub (use azure-iot-hub), Azure IoT Central (use azure-iot-central), Azure IoT Operations (use azure-iot-operations).
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。
Review open PRs two ways - default is a per-PR deep review with severity-tagged findings, inline comments, and a verdict; --survey runs a risk-tiered triage digest of what's safe to merge 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。
Two-phase contributor rewards - plan builds a tier-priced payout from the repo's merged-PR ranking; send executes it on-chain via Bankr Wallet API with per-recipient idempotency and dry-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。
Three recap modes - default synthesizes agent health, community growth, and economic activity into a was-it-worth-it verdict; ops recaps what shipped and failed; push ranks push impact.
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。
Scan the ClawHunter agent bounty marketplace for opportunities that genuinely match this agent's real capabilities (code, security research, writing) and surface only real matches — never a raw unfiltered dump. When a match is real audit-shaped work with a linked GitHub repo, the notification carries a one-tap button to dispatch vuln-scanner at it directly.
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。
Install a community skill pack into this fork from a GitHub repo and ship it as an auto-merged PR
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。
Cross-skill signal detector - finds entities or themes surfaced independently by 3+ different skill categories within 48h and surfaces them as high-confidence write 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。
Package one of this agent's own skills as a standalone community pack and submit it to the aeon registry as a PR
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。
Amazon competitor monitoring and competitive intelligence for sellers. Track pricing changes, inventory levels, new product launches, review velocity, and advertising patterns for competitor ASINs. Set up monitoring alerts for price drops, stock-outs, and market changes. Works on 12 Amazon marketplaces. No API key required. Use when: (1) monitoring competitor pricing strategies, (2) tracking competitor inventory and stock patterns, (3) detecting new product launches in your category, (4) analyzing competitor review velocity and ratings, (5) identifying market opportunities from competitor gaps, (6) setting up competitive alerts and benchmarks.
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 a product-specific Amazon FBA prep, labeling, packing, and shipment-readiness plan. Use when a seller asks how to prepare units for FBA, choose barcode and labeling steps, plan poly-bagging, bubble wrap, sets, case packs, cartons, dangerous-goods review, supplier or prep-center instructions, or prevent receiving delays. Do not use as a substitute for current Seller Central requirements.
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。
Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic insights to optimize product strategy and advertising spend. Works with Brand Analytics data from all Amazon marketplaces. Requires Brand Registry access. Use when: (1) analyzing Search Frequency Rank data for keyword opportunities, (2) interpreting Market Basket data for cross-sell and bundling, (3) understanding Item Comparison competitive positioning, (4) extracting customer demographic insights, (5) optimizing product portfolio based on customer behavior, (6) building data-driven advertising 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。
Seller storefront analysis and competitive intelligence for Amazon. Analyzes seller revenue estimation, product portfolio strategy, growth trajectory, and market positioning. Reverse-engineer successful seller strategies and identify expansion opportunities. Use when the user asks about analyzing sellers, competitor seller analysis, seller revenue estimation, storefront analysis, seller strategy, or learning from successful Amazon sellers.
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。