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Use when evaluating Joel Greenblatt-style special situations: spinoffs, restructurings, merger securities, forced selling, magic formula quality/value screens, and event-driven mispricing.

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Use when evaluating Nicolas Darvas-style box breakouts, new-high momentum, volume confirmation, and mechanical stop placement for liquid growth stocks.

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Use when evaluating Hindenburg-style investigative short reports: evidence chains, promoter networks, stock promotion, related parties, disclosure failures, and catalyst-driven short narratives.

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Use when evaluating Peter Lynch-style growth stocks: invest in what you can understand, classify the company type, check growth runway, PEG, balance sheet, and whether the story is still improving.

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Use when evaluating markets through Howard Marks-style second-level thinking, credit cycles, risk premiums, market psychology, and defensive versus aggressive posture.

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Use when evaluating crypto networks and companies through an a16z crypto lens: web3 adoption, infrastructure vs apps, open networks, and long-duration crypto ecosystem investing.

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Use when evaluating a business through Buffett-style ownership, owner earnings, durable moat, management quality, capital allocation, and margin-of-safety judgment.

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Use when evaluating stocks through Dan Bin's long-term compounder lens: buy great companies, hold through volatility, and let time do the work. Focus on consumer, pharma, and tech compounders.

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Use when evaluating Cathie Wood-style disruptive innovation themes: S-curves, converging technologies, long-duration growth, platform adoption, and multiyear innovation portfolios.

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Use when evaluating top-down macro trades, liquidity regimes, currency/rates/equity themes, concentrated risk-taking, and quick loss-cutting in a Stanley Druckenmiller style.

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 evaluating crypto through a Grayscale-style Crypto Sectors lens: sector taxonomy, FTSE/Grayscale index eligibility, fee/usage fundamentals, sector-share valuation, ETP/trust wrappers, and Zcash-style privacy-as-money theses.

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Use when evaluating Seth Klarman-style deep value and distressed opportunities: extreme margin of safety, forced sellers, liquidity discounts, complexity, downside protection, and patience.

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Use when evaluating startups or tech platforms through an a16z-style lens: technology adoption, market creation, platform power, AI/crypto/software narratives, and bold long-duration tech bets.

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Use when evaluating Bill Ackman-style concentrated long theses, activist campaigns, high-conviction multiyear bets, and public narrative plus fundamental catalysts.

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Use when evaluating Carl Icahn-style activist investments: undervalued assets, poor governance, capital allocation fixes, buybacks, asset sales, board pressure, and control catalysts.

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 evaluating startups through a Founders Fund-style lens: contrarian conviction, hard tech, bold product ambition, non-consensus markets, and high-upside company building.

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Use when evaluating growth stocks through O'Neil CAN SLIM: earnings acceleration, relative strength, institutional demand, market direction, base quality, and breakout discipline.

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Use when evaluating Michael Burry-style contrarian, asymmetric, unpopular trades: deep mispricing, short theses, complex assets, forced consensus errors, and catalyst-driven downside/upside.

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 evaluating crypto charts through an Hsaka-style technical lens: market structure, trend, invalidation, leverage discipline, and narrative-aware timing.

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Use when evaluating Ed Seykota-style systematic trend following: mechanical rules, long-term trend capture, small losses, position sizing, whipsaw tolerance, and emotional discipline.

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 for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.

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Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.

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 for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Explore an existing PostgreSQL database to understand its objects and relationships - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates - Plan or execute safe schema migrations with zero downtime **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series, migration

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 to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential 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。