「long-term」の検索結果

[omh] Explaining OMH itself: current-source-first documentation for OMH itself: product identity, public capability catalog, model routing, local state, and long-term memory. Use when the user says: product-docs, OMH documentation, oh-my-hermes documentation, what is OMH, what is oh-my-hermes, how does OMH work, OMH capability catalog, OMH skill catalog.

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Your long-term knowledge pages. Read them at session start. Create new pages when you learn something worth remembering across sessions. Pages auto-update from your conversations via Hindsight.

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Skill that analyzes 18-month scenarios from a news headline. Runs the primary analysis with the scenario-analyst agent and obtains a second opinion with the strategy-reviewer agent. Generates a comprehensive English report covering 1st/2nd/3rd-order impacts, recommended stocks, and a critical review. Example: /scenario-analyzer "Fed raises rates by 50bp" Triggers: news analysis, scenario analysis, 18-month outlook, medium-to-long-term investment strategy

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Detect structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis. Analyze RSP/SPY concentration, yield curve, credit conditions, size factor, equity-bond relationship, and sector rotation to identify regime shifts between Concentration, Broadening, Contraction, Inflationary, and Transitional states. Run when user asks about macro regime, market regime change, structural rotation, or long-term market positioning.

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Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).

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Build lean, opinionated products using the 37signals philosophy from "Getting Real", "Rework", and "Shape Up". Use when the user mentions "Getting Real", "Rework", "Shape Up", "37signals", "Basecamp method", "six-week cycles", "fixed time variable scope", "appetite vs estimates", "betting table", "breadboarding", "fat marker sketch", "build less", "underdo the competition", "opinionated software", "we have too many meetings", "how do we ship faster", or "stop overbuilding". Also trigger when cutting scope to ship sooner, running a small team, or avoiding long-term roadmaps. Covers shaping, betting, building, and the art of saying no. For MVP validation, see lean-startup. For design sprints, see design-sprint.

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Record important information to long-term memory for learning user preferences, successful patterns, and error lessons. When you need to remember user preferences, save successful patterns, or record lessons from errors.

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Gregor Mendel — genetics mentor, patient experimenter, and gardener-monk. Trigger this skill when users ask about genetics, heredity, inheritance patterns, Mendelian laws, dominant/recessive traits, gene segregation, independent assortment, Punnett squares, classical genetics, or evolution-genetics connections. Also trigger when discussing the philosophy of science, how great ideas get ignored, the relationship between faith and science, or the virtue of patient long-term observation. Even if the user doesn't say "Mendel" explicitly, trigger this skill for questions touching genetics, breeding, trait inheritance, or genomics history. Part of the AIPOCH Science Mentor Skill Hub.

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Use when evaluating John Templeton-style global contrarian value: buy at points of maximum pessimism, search worldwide, and demand a large discount to long-term value.

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Use when evaluating investments through Zhang Lei / Hillhouse Capital's long-term research-driven value lens: deep research, do time's friend, invest in great companies, and actively help them grow.

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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.

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Use when evaluating startups or growth companies through a Sequoia-style lens: founder quality, market size, product-market fit, distribution, and long-term company building.

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Use when evaluating Li Lu-style value investments: circle of competence, long-term compounding, deep research, conservative downside, China/global quality businesses, and concentrated ownership.

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Use when evaluating Nick Sleep-style long-term compounders: scale economies shared, customer obsession, low turnover, long runway, owner culture, and value creation through lower prices.

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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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Detect long-term trends in time series data using parametric and non-parametric methods. Use when determining if a variable shows statistically significant increase or decrease over time.

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Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.

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Zep memory server integration for long-term conversation memory and user profiling

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Decode a care-home contract before a parent moves in — the fee-escalation clause with no cap, the needs-can-no-longer-be-met eviction trigger, the guarantor line that quietly makes you personally liable, and the after-death charges nobody mentions on the tour. Use when asked to review a care-home or nursing-home contract, what does this admission agreement mean, can they raise the fees, or can they make my parent leave. Produces a clause-by-clause decode ranked by financial and eviction risk, the questions to put in writing before signing, and the negotiate-or-verify list. Pairs with long-term-care-options for choosing the home; this is for the contract. Not legal advice.

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Help users identify whether supply or demand is their primary growth bottleneck and apply tactical levers to achieve marketplace liquidity and long-term health.

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Help users define a compelling, qualitative description of a future state that aligns decentralized teams and motivates long-term innovation.

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Help users identify the drivers of long-term retention and implement behavioral mechanics that keep customers coming back. This guide focuses on measuring the survival curve of users and optimizing the product environment for high frequency usage.

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Help users navigate complex product decisions by weighing competing priorities and calculating long-term value. This skill covers build-vs-buy logic, technical debt evaluation, and communicating tradeoffs to senior leadership.

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Help users create memorable, suggestive, and distinctive names and brand identities that facilitate growth and drive long-term defensibility.

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。