「long-term」の検索結果

Use this skill whenever you are reasoning about large-scale scientific projects, genomics, bioethics, data infrastructure, or long-term medical translation. Eric S. Lander (geneticist, Broad Institute, Human Genome Project) provides a framework for hypothesis-free discovery, structuring "big science," and treating biology as an information science. Reach for this when the user is discussing open science, collaborative ecosystems, CRISPR/gene editing ethics, managing massive datasets, or setting realistic timelines for technological breakthroughs. Apply his principles to avoid the "lone genius" myth, prevent overpromising, and build foundational infrastructure that serves the broader community.

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This skill channels the strategic thinking of Mark Zuckerberg, co-founder and CEO of Meta. Reach for this skill whenever the user is navigating rapid product iteration, scaling social platforms, open-source AI strategy, content moderation trade-offs, or long-term organizational management. It is highly effective for scenarios involving "The Hacker Way" (rapid prototyping), building community, handling intense public criticism, or deciding between open vs. closed ecosystems. Use this skill to apply his frameworks on decentralized testing, small group context sharing, and progressive AI integration, even if the user doesn't explicitly mention his name.

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Applies the frameworks of Aristotle (ancient Greek philosopher, logic, ethics, metaphysics, 384-322 BCE) to decision-making, ethics, and analysis. Use this skill whenever the user is grappling with moral dilemmas, character development, habit formation, finding balance (the Golden Mean), persuasive communication (rhetoric), or understanding the root causes and ultimate purpose (teleology) of a project or system. Reach for this skill even if the user doesn't name Aristotle, especially when discussing career fulfillment, long-term happiness (eudaimonia), balancing extremes, or structuring persuasive arguments.

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Applies the reasoning, principles, and frameworks of Jürgen Schmidhuber (LSTM co-inventor and deep learning pioneer). Reach for this skill whenever tackling problems involving sequence learning, artificial curiosity, intrinsic motivation, reinforcement learning architectures, or predicting long-term technological and cosmic evolution. Use this when discussing AGI timelines, the history and attribution of AI breakthroughs, data compression as learning, or when designing autonomous agents that must set their own goals. Trigger this skill for topics like recurrent neural networks, algorithmic information theory, open-source AI democratization, and evaluating true existential risks versus media hype.

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Applies the philosophical frameworks of Confucius (ancient Chinese philosopher, 551-479 BCE) to modern problems. Reach for this skill whenever the user is dealing with leadership, governance, team harmony, organizational culture, moral dilemmas, mentorship, or personal self-cultivation. It triggers on topics like building trust without micromanaging, resolving hierarchical conflicts, aligning actions with values, and creating systems based on virtue rather than strict punitive rules. Use this skill to evaluate character, design educational approaches, and foster long-term social harmony.

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Applies the reasoning style of Judy Faulkner, founder and CEO of Epic Systems, to decisions around corporate governance, healthcare technology, and unconventional leadership. Use this skill whenever Claude is asked for advice on going public vs. staying private, M&A strategy (acquisitions), budgeting, reducing software-driven burnout, data privacy, or interoperability. Faulkner's thinking is highly independent, fiercely protective of long-term mission over short-term profits, and deeply skeptical of traditional corporate playbooks (like MBAs, strict budgets, and venture capital). Trigger this skill to channel her focus on organic growth, customer empathy, and treating software as 'mental clay'.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Manage persistent memory across sessions. Use when asked to remember, recall, forget, or review what was stored. Covers long-term facts (MEMORY.md), daily session logs, search, and memory housekeeping.

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用户在做长期投资/储蓄/增长决策，需要理解时间和复利的力量，或被短期波动困扰时。 语言信号:"持有多久合适"/"年化X%十年后会怎样"/"为什么长期持有比频繁交易好"/"小收益怎么变成大财富" 不适用于:短期交易决策、不需要时间维度的分析。

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用户在考虑是否卖出持仓、评估持有期限，或纠结于"何时卖出"时。 语言信号:"什么时候该卖"/"持有多久合适"/"已经涨了要不要卖"/"什么时候止盈" 不适用于:短期交易决策或投机性持仓的卖出判断。

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Simulating business strategy via short/mid/long-term scenario planning from financial, market, and competitive data. Applies SWOT/PESTLE/Porter, KPI forecasting, roadmaps. Does not write code.

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Advising founders YC-style — socratic probing to surface the #1 bottleneck, anti-pattern detection, and 1-2 week action items. Not for brainstorming (Riff) or long-term scenarios (Helm).

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Store memories in tier-aware hierarchical memory — working / short-term / long-term — and recall with tier filters. Use for working-set context that should fade, vs facts that should persist, vs patterns that should be searchable forever.

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Maintain the harness memory files (USER.md, MEMORY.md, AGENTS.md) so that long-term knowledge persists across conversations.

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Expert guide for long-term episodic memory integration (Mem0 v2, Letta/MemGPT, Zep v2), memory tier architecture, pgvector HNSW storage, and unified context management for autonomous AI agents / Panduan ahli untuk integrasi memori episodik jangka panjang (Mem0 v2, Letta/MemGPT, Zep v2), arsitektur tier memori, penyimpanan pgvector HNSW, dan manajemen konteks terpadu untuk agen AI otonom.

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The discipline of keeping a field notebook — words, pictures, numbers, metadata, and questions captured during sustained outdoor observation. Covers the Laws notebook method, the sit-spot practice, phenology recording, sketch-first-name-later discipline, and the long-term research value of accumulated journals. Use when the task is to teach or structure ongoing field observation rather than answer a one-shot identification.

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Analyzing patterns of continuity and change over time in historical contexts. Covers periodization (how and why we divide history into eras), identifying turning points, measuring rates of change, and recognizing long-term patterns and trends. Use when analyzing temporal structure in history, debating period boundaries, or assessing whether a moment represents genuine transformation or surface-level disruption.

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Run this appropriateness check the moment you are about to integrate a retrieved long-term memory — a Grove content-addressed hit, a chroma/pgvector neighbour, a memory-consolidation digest, or a MEMORY.md line — into a response, especially anything touching private origins, Fox Companies IP, credentials, or Center Camp / consent-governed content. It answers a question intent-router never asks: not WHAT to fetch or HOW, but WHETHER a correctly-retrieved item should reach output. Relevance is not appropriateness — a perfect similarity match can still be a boundary violation. Default is FAIL-CLOSED: if in-context authorization is uncertain, the memory may inform behaviour but must not be surfaced. Backed by RBI-Eval (arxiv 2606.06055v1). Triggers on surfacing recalled sensitive memory into a response.

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Build and navigate DSP (Data Structure Protocol) — graph-based long-term structural memory of codebases for LLM agents. Stores entities (modules, functions), their dependencies (imports), public API (shared/exports), and reasons for every connection. Use when: (1) project has a .dsp/ directory, (2) user asks to set up DSP or bootstrap project structure, (3) creating/modifying/deleting code files in a DSP-tracked project, (4) navigating project structure, understanding dependencies, or finding modules, (5) user mentions DSP, dsp-cli, .dsp, or structure mapping.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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。

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

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Evaluate whether an OSS repo is worth long-term investment before committing time. Assesses project health, governance, community, bus factor, and trajectory. Use when deciding whether to contribute to a specific repo, choosing between multiple repos, or evaluating a project's sustainability. Not for checking if a repo accepts contributions — use oss-find-issue for that.

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。