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概要と使いどころ
Query Chinese A-share, ETF, index, financial statement, valuation, capital-flow, and technical-indicator data through the AShareHub Python SDK and hosted API. Use this skill when an agent needs structured China market data as pandas DataFrames.
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 embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.
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Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.
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。
Database design, query optimization, migrations, and indexing. Use when designing schemas, writing queries, or managing 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。
Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (LCP, INP, CLS) and supplementary metrics (FCP, TBT, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge.
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Comprehensive SEO analysis for any website or business type. Performs full site audits, single-page deep analysis, technical SEO checks (crawlability, indexability, Core Web Vitals with INP), schema markup detection/validation/generation, content quality assessment (E-E-A-T framework per Dec 2025 update extending to all competitive queries), image optimization, sitemap analysis, and Generative Engine Optimization (GEO) for AI Overviews, ChatGPT, and Perplexity citations. Analyzes AI crawler accessibility (GPTBot, ClaudeBot, PerplexityBot), llms.txt compliance, brand mention signals, and passage-level citability. Industry detection for SaaS, e-commerce, local business, publishers, agencies. Triggers on: "SEO", "audit", "schema", "Core Web Vitals", "sitemap", "E-E-A-T", "AI Overviews", "GEO", "technical SEO", "content quality", "page speed", "structured 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。
Operate Milvus vector database with pymilvus Python SDK. Use when the user wants to connect to Milvus, create collections, insert vectors, perform similarity search, hybrid search, full-text search, manage indexes, partitions, databases, or RBAC via Python code.
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 ManagedCode.MarkItDown when a .NET application needs deterministic document-to-Markdown conversion for ingestion, indexing, summarization, or content-processing workflows.
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Analyzes inflation protection mechanisms in infrastructure with CPI-linked revenues, index-based contracts, and real return modeling. Use when modeling inflation linkage, analyzing CPI adjustment, or evaluating real return profiles.
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Organizes virtual data room structure with document indexing, access permissions, activity tracking, and Q&A management. Use when setting up VDRs, managing document production, or tracking buyer diligence activity.
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Evaluates CDS, CDX, and bespoke credit derivative structures with spread analysis and credit event mechanics. Use when analyzing credit derivatives, pricing CDS, or evaluating index tranche products.
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Use when you need to auto-log all tool interactions into a self-organizing vector DB with instant recall. Intercepts interactions, extracts key information, generates embeddings, stores in LanceDB, auto-categorizes, and builds a retrieval index. Inspired by OpenClaw SQLite LCM + summary DAG pattern.
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Start and manage the full heartbeat ecosystem for agent-studio. Registers all 7 heartbeat loops plus auto-reschedule task via CronCreate to keep the agent ecosystem healthy, indexed, informed, and connected.
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Applies general rules for Convex development, emphasizing schema design, validator usage, index-first query patterns, function registration, and correct handling of system fields.
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Database design and optimization specialist. Schema design, query optimization, indexing strategies, data modeling, and migration planning for relational and NoSQL databases.
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Manage spec registry and generate snapshots.
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 SEO planning and analysis for pages generated at scale from data sources. Covers template engines, URL patterns, internal linking automation, thin content safeguards, and index bloat prevention. Use when user says "programmatic SEO", "pages at scale", "dynamic pages", "template pages", "generated pages", or "data-driven SEO".
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。
Comprehensive SEO analysis for any website. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, E-E-A-T content quality, image optimization, sitemap analysis, GEO for AI search, local SEO, and strategic planning. Uses Wove browser tools for real-page execution. Triggers on: "SEO", "audit", "schema", "Core Web Vitals", "sitemap", "E-E-A-T", "AI Overviews", "GEO", "technical SEO", "content quality", "structured data", "local SEO", "page speed".
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 near the end of EZ_math_model intake when external/user-corpus contains user-provided papers, notes, PDFs, datasets, or examples that should become a local AGENTS.md reference index.
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Pure-Python knowledge base statistics dashboard. Reads shelf-index and log.md; emits Inventory, Layer distribution, Domain distribution, Recent Activity, and Staleness sections. No agent dispatch. Read-only.
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Register an external knowledge base library in the user-scope global registry at ~/.sdlc/global-libraries.json. Validates that the target path exists, contains a _shelf-index.md, and parses its format_version. Optional helper — hand-editing the JSON is also supported.
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Check whether the knowledge base shelf-index is stale relative to the library files. Reports drifted entries without rebuilding. Designed to be invoked from environment validation or pre-push hooks as an opt-in check. Default mode is warning; strict mode fails with non-zero exit when any drift is detected.
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Parallel map-reduce bulk ingest for large source sets. Map phase dispatches read-only knowledge-extractor agents (≤N-wide, default Haiku) emitting per-source JSON extracts; Python routes extracts by target library file (fuzzy-merging new topics, pre-allocating new files, flagging oversized topics); reduce phase dispatches one agent-knowledge-updater per file (parallel, one writer per file); single shelf-index rebuild + one log.md entry. Resumable. Supersedes kb-ingest-batch.
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Integrate a new source into the project knowledge base. Wraps the agent-knowledge-updater agent. Source can be a local file path, a URL, or pasted content. The updater reads the source, classifies it, identifies which existing library files it touches, makes surgical updates or creates new files, rebuilds the shelf-index, and appends to log.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。