「long-term」の検索結果

Optional power-up — detects oversized codebases/docs and suggests tobi/qmd for local semantic search. Bridges cm-continuity (working memory) with long-term document retrieval. Zero-config detection, non-intrusive suggestion.

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。

Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

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。

OS post-exploitation after a stable host foothold: quiet host recon, host-native persistence (Windows Run/tasks/services/COM/WMI and Linux SSH/cron/systemd/SUID), long-term C2 (Sliver primary, Mythic/Havoc optional), targeted collection and exfil, and engagement cleanup. Does not own AD/cloud/K8s native persistence, Ligolo pivoting, LSASS dumping, or EDR bypass. Operator selects objectives; success is a live callback, verified persistence, delivered loot, or restored host — not generating an unused implant.

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 Codex must make a high-quality judgment on any complex problem, not only technical problems. Trigger when a question has high complexity, uncertainty, cost of being wrong, long-term impact, value tradeoffs, possible structural causes, or when the user asks for 全面思考, 深度理解, 系统性分析, 帮我想清楚, 从根上看, 这件事到底怎么看, 方法论, 复杂问题拆解, 系统方案, 架构判断, 策略判断, or 根因分析. Also trigger for project and engineering high-risk scenarios such as multi-module, multi-environment, multi-data-source, multi-state, financial truth, live trading, ledger, revenue, customer assets, order governance, broker interfaces, online availability, major solution selection, and system closure.

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。

Builds economic profit (EVA) models with capital charge calculation, value spread analysis, and long-term value creation measurement. Use when calculating economic profit, analyzing EVA trends, or measuring value creation.

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。

Structures investor day content with long-term strategy presentation, financial targets, and capital allocation framework communication. Use when preparing investor days, designing strategy presentations, or building financial target frameworks.

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。

Structures 100-day and long-term value creation plans with revenue growth, margin improvement, and capital efficiency initiatives. Use when building value creation plans, tracking improvement initiatives, or preparing operating partner reviews.

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。

Evaluates competitive advantage sustainability with switching costs, network effects, data assets, and brand strength analysis. Use when assessing competitive moats, analyzing defensibility, or evaluating long-term positioning.

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。

Identify and safely gather edible and useful wild plants. Covers safety rules and deadly plant recognition, habitat reading, multi-feature identification methodology, the universal edibility test, sustainable harvesting practices, preparation methods, reaction monitoring, and building knowledge with beginner-friendly universal species. Use when supplementing food supply in a wilderness or survival setting, needing medicinal or utility plants, identifying plants around camp for safety, or in long-term scenarios where foraging extends available rations.

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。

Cultivate bonsai trees from species selection through long-term seasonal care. Covers species suitability, structural and maintenance pruning, wiring technique, repotting protocol, soil mix preparation, seasonal care schedules, and contemplative sitting practice. Use when selecting a species for a first or next bonsai, performing structural or maintenance pruning, repotting when roots are circling or growth has stalled, wiring branches for shaping, developing a seasonal care calendar, or building a contemplative practice with a living tree.

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 implementing or managing persistent, hierarchical memory systems for AI agents. Covers cross-session state, fact supersession, and self-managed memory tools to enable long-term recall and adaptive agent behavior.

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

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。

You are an expert in user onboarding and activation. Your goal is to help users reach their "aha moment" as quickly as possible and establish habits that lead to long-term retention.

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。

Agent-agnostic long-term project memory through `.memory-bank/` + RULES (TDD/SOLID/Clean Architecture/FSD/Mobile) + dev-toolkit commands. Use when working in a project with a `.memory-bank/` directory or when the user explicitly asks for memory-bank workflow, code rules, or dev-toolkit commands (`/mb`, plan, spec, verify, done; «память проекта», «контекст проекта», «план», «продолжай по плану»).

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。

"Apply cultivation theory (Gerbner) to analyze how long-term media exposure shapes worldviews. Use this skill when the user needs to study cumulative media effects on audience beliefs, evaluate mainstreaming and resonance phenomena, or assess how media consumption patterns correlate with perceptions of social reality — even if they say 'does watching news make people more fearful', 'how does media shape worldview', or 'mean world syndrome'.".

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。

"Apply organizational ambidexterity theory to balance exploration and exploitation activities. Use this skill when the user needs to diagnose whether an organization is over-exploiting or over-exploring, design structures that support both innovation and efficiency, or evaluate the tension between short-term performance and long-term renewal.".

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 session-based recommendation from short-term user behavior sequences without long-term profiles. Use this skill when the user needs to recommend in anonymous sessions, predict next click from browsing sequence, or build recommendations for non-logged-in users — even if they say 'what should they click next', 'anonymous user recommendations', or 'browsing sequence prediction'.".

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。

Forge highly privileged Kerberos Ticket Granting Tickets (TGTs) to gain persistent, undetectable, and long-term administrative access across an entire Active Directory domain. Use this skill during the final stages of a Red Team operation after Domain Admin access has been achieved, simulating an Advanced Persistent Threat (APT) establishing deep persistence that survives password resets.

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。

MANDATORY deploy after every code change. Typecheck → deploy → purge CDN → E2E on production → visual verify → fix-forward loop. Workers Builds native CI/CD, D1 Time Travel PIT recovery, D1→R2 long-term backups, wrangler rollback, wrangler secrets management, structured observability, cross-browser smoke tests, rollback procedures, and GitHub auto-configuration.

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 a task needs the judgment of a Farmer, Rancher, or other Agricultural Manager — deciding a planting/breeding mix or acreage shift under price and weather uncertainty, sizing a hedge or crop-insurance coverage level, running break-even and diversification scenarios before a capital or input commitment, or evaluating land/herd management practices against long-term productive capacity.

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。

Detect content decay by identifying pages that are losing both clicks and impressions over a 90-day comparison window in Google Search Console. Surfaces pages with true long-term decline (not seasonal variation) and assigns decay severity ratings. Use when user says "content decay", "declining pages", "losing rankings", "pages losing traffic over time", or "90 day decline".

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。

Design memory for AI agents — short-term context, long-term stores, retrieval strategies, and memory-augmented reasoning. Use when an agent must remember facts, learn preferences, or stay coherent across sessions.

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。

Persistent memory patterns for moflo agents — session memory, long-term knowledge, pattern learning, and cross-session context via moflo's node:sqlite + HNSW vector store. Use when building stateful agents or assistants that need to remember across runs.

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。

Deliberate session retrospective — look back over what you just did, distill the durable, reusable lessons (not session trivia), and write them to the learnings memory namespace, deduped against what is already stored. Use at the END of a meaningful chunk of work to capture high-signal lessons worth keeping long-term. The curated counterpart to moflo's passive session-continuity capture.

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。