Results for “long-term”

Set up the OpenViking long-term memory plugin for OpenClaw through conversation. The skill handles installation, configuration, and verification so chat facts can be saved and relevant memories recalled across sessions, using an existing OpenViking server.

• Add long-term memory to OpenClaw
• Recall saved facts across chat sessions
• Separate memories by assistant or sender

Evaluate partners and opportunities by asking whether trust, knowledge, and reputation can build over ten years. This skill provides questions for weighing quick returns against lasting cooperation in which everyone benefits.

• Assessing a potential long-term business partner
• Weighing quick returns against reputation
• Planning actions that build trust over time

Give an agent rules for consulting long-term memory before work and saving decisions, corrections, and failures afterwards. It helps carry preferences and project conventions across sessions while preserving dated history and making conflicting memories visible.

• Carry preferences and project conventions across sessions
• Record user corrections for future tasks
• Preserve the history of changed decisions

Design memory systems that help AI assistants retain conversation information and retrieve it when relevant. Explore short-term, long-term, and entity memory, along with memory consolidation, prompt integration, storage lifecycle management, and separation between users.

• Designing an assistant that remembers across conversations
• Storing and updating facts about people or other entities
• Adding relevant memories to prompts

口腔种植 (含 单牙/多牙缺失种植 / 全口 All-on-X / 即刻种植 即刻负重 / 上颌窦提升 / 牙槽嵴骨增量 GBR / 软组织移植 / 数字化种植 (CBCT 设计 + 口扫 + 导板 + CAD-CAM 上部) / 种植体周炎防治 / 种植修复 (基台 + 冠 + 杆卡 + 覆盖义齿) — 不含 普通牙体牙髓 / 正畸 / 儿牙 / 口腔颌面外科肿瘤 / 隐形矫治商业) (implant dentistry — surgical placement and prosthetic restoration of osseointegrated dental implants; covers diagnosis & treatment planning, site preparation (bone augmentation, sinus elevation, soft-tissue grafting), implant placement (delayed / immediate / immediate-loading), provisionalization and final restoration (cement / screw retained, custom abutments, full-arch), digital workflow (CBCT planning, intra-oral scanning, guided surgery, CAD-CAM milled prosthetics), and long-term maintenance including peri-implantitis prevention and treatment; NOT general restorative dentistry, NOT orthodontics, NOT pediatric dentistry, NOT maxillofacial oncology / trauma reconstruction, NOT clear-aligner business.) Master OS — automated mastery of implant dentistry — surgical placement and prosthetic restoration of osseointegrated dental implants; covers diagnosis & treatment planning, site preparation (bone augmentation, sinus elevation, soft-tissue grafting), implant placement (delayed / immediate / immediate-loading), provisionalization and final restoration (cement / screw retained, custom abutments, full-arch), digital workflow (CBCT planning, intra-oral scanning, guided surgery, CAD-CAM milled prosthetics), and long-term maintenance including peri-implantitis prevention and treatment; NOT general restorative dentistry, NOT orthodontics, NOT pediatric dentistry, NOT maxillofacial oncology / trauma reconstruction, NOT clear-aligner business.: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on implant dentistry — surgical placement and prosthetic restoration of osseointegrated dental implants; covers diagnosis & treatment planning, site preparation (bone augmentation, sinus elevation, soft-tissue grafting), implant placement (delayed / immediate / immediate-loading), provisionalization and final restoration (cement / screw retained, custom abutments, full-arch), digital workflow (CBCT planning, intra-oral scanning, guided surgery, CAD-CAM milled prosthetics), and long-term maintenance including peri-implantitis prevention and treatment; NOT general restorative dentistry, NOT orthodontics, NOT pediatric dentistry, NOT maxillofacial oncology / trauma reconstruction, NOT clear-aligner business. problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「种植牙」「种植」「口腔种植」「牙种植」「implant」

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Save facts, decisions, preferences, and task outcomes to Hindsight long-term memory. The skill turns what was learned into short notes with context and reasons, choosing either a Bot’s own memory store or a shared store.

• Saving a completed task’s outcome and reasoning
• Remembering a user’s stated preferences
• Recording decisions for future work

Find context from past coding sessions in Hindsight long-term memory. Reuse memories already loaded for the session, search for missing details, and reason over accumulated records when answering questions about decisions, project context, or preferences.

• Revisit the reasons behind past implementation decisions
• Recover project context discussed in earlier sessions
• Check previously discussed preferences

Create a specialized Claude Code subagent with Hindsight long-term memory. Start with an empty agent or import prepared documents, then equip it with knowledge pages and memory search to reuse information across sessions.

• Create a specialized agent that remembers across sessions
• Build an agent from prepared reference documents
• Start an empty agent and add knowledge over time

Use OpenViking through OpenClaw to manage long-term memories, search archived conversations, and find imported resources or Agent Skills. Inspect recall traces to understand search results and recover full tool outputs stored outside the conversation.

• Recall past preferences and decisions
• Find exact commands in archived conversations
• Import and search documents or Agent Skills

Analyze how assets move together and test cointegration, a long-term equilibrium relationship between prices. Find related assets, group similar holdings, examine changing correlations, and estimate hedge ratios and reversion times for pairs-trading research.

• Find assets that move with a target holding
• Group similar assets to review diversification
• Test long-term price relationships for pairs trading

Analyze product, organization, international expansion, and hiring decisions through frameworks drawn from 张一鸣’s public statements. Examine information flow, user needs, and long-term tradeoffs to identify underlying problems and opportunities for small-scale validation.

• Reviewing product ideas against underlying user needs
• Examining information flow and reporting layers
• Planning small tests before international expansion

Design memory systems that help AI agents retrieve relevant past information. Choose short-term and long-term memory structures, compare vector storage options, and review document splitting and retrieval strategies for different kinds of knowledge and experience.

• Design memory for information used across conversations
• Choose storage for experiences, facts, and procedures
• Review retrieval when an agent misses saved information

Store conversations, agent activity, and feedback in cognee sessions, then carry them into long-term memory. This skill explains improve(), its processing stages, result statuses, and settings for diagnosing skipped work or controlling automatic runs.

• Saving conversations and agent activity by session
• Moving session content into long-term memory
• Investigating skipped or blocked processing stages

Guide an agent through searching past decisions before work, saving new decisions with their reasons, and treating corrections as lessons. The workflow helps distinguish useful long-term knowledge from temporary details, code facts, and routine activity already captured by hooks.

• Checking earlier decisions before starting a task
• Saving a resolved issue with its underlying reason
• Preserving lessons from corrections

Test an idea, strategy, or investment against 11 future time horizons, from six months to 50 years. Compare scenarios with probability ranges, identify structural risks, and review or update the underlying world models.

• Testing an investment thesis across time horizons
• Reviewing long-term risks to a business model
• Updating future scenarios after major events

Turn OpenClaw conversations, actions, and tool history into structured long-term memory. Agents can retrieve past facts, decisions, and summaries when needed, helping them continue work across sessions and recover context after it is shortened.

• Retrieve decisions made in previous sessions
• Review summaries of earlier work
• Recover context after conversation compaction

Search an agent's long-term memory for people, projects, and other facts. Save important corrections immediately, update outdated entries, and remove errors or duplicates while respecting which notebooks the current conversation can read or change.

• Recall a person's role or current work
• Save facts that should be remembered later
• Correct outdated or inaccurate information

Install or remove memU long-term memory for the agent in use. The skill selects the host-specific guide, configures scheduled memory recording and retrieval before answers, and verifies the setup with local or cloud storage.

• Adding long-term memory to an AI agent
• Setting up memory stored on the current device
• Connecting an agent to MemU Cloud

Examine important choices and assess claims by rebuilding their reasoning from basic knowledge. Use mental models, reusable ways to understand problems, to compare conclusions, identify unknowns, and consider long-term consequences.

• Choosing a direction with long-term consequences
• Assessing the reasoning behind an expert's claims
• Building judgment in an unfamiliar field

Build and update a study plan for bar exams or law school courses. Allocate time around weak subjects, available hours, and rest days, then adjust upcoming sessions using recorded practice results and progress.

• Building a long-term bar exam study plan
• Planning for law school finals
• Rebalancing study time using practice results

Test whether instructions planted in an agent's long-term memory or searchable knowledge base influence later behavior. Verify both a stored test marker and a resulting change in answers, permissions, or treatment of retrieved documents.

• Checking whether stored instructions affect later conversations
• Testing whether planted documents become trusted answers
• Reviewing permission changes caused by saved conversation summaries

Test whether an AI agent stores hostile instructions in persistent memory and follows them later. The skill uses marked test instructions and follow-up dialogue to document storage confirmations or changes in subsequent responses.

• Checking instruction handling in long-term memory
• Testing agents with profiles or persistent notes
• Verifying whether stored instructions affect later replies

Offer 对比决策器。用户同时拿到两份（或多份）offer 纠结怎么选时，扮演 Senior Career Decision Advisor：结构化对比 comp / 成长 / AI 敞口 / 公司强度 / 团队风险 / 晋升速度 / 生活方式，识别 front-loaded vs long-term upside、resume value、switch-out 难度、hidden risks，最后给一条**明确、有立场、不中性**的推荐（并附「你是 X 类人选 A，你是 Y 类人选 B」的分叉判定）。三步：贴两份 offer → 补 priorities & 现状 → 自动生成并打开一份 HTML Offer Decision Report。是 offer-toolkit 里 JD Skill / Resume Skill / BQ Skill 拿到结果之后的下一环。关键词：offer compare, 选 offer, 两个 offer, offer decision, TC compare, 该去哪家, which offer, 4-year TC, RSU, sign-on, front-loaded, long-term upside, career decision。

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Rules for when an agent should recall from long-term memory before acting and when it should save decisions, corrections and failures afterwards. Works with any memory backend.

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