'agent memory' 검색 결과

Design a spaced retrieval schedule for any topic list and timeline. Use when planning units, term sequences, or revision programmes.

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Generate retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.

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Analyse a learning task for cognitive load problems and recommend specific design improvements. Use when tasks overwhelm students, instructions feel complex, or materials need simplifying.

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Explain and configure individual spacing algorithms using student performance data and forgetting curves. Use when personalising retention schedules in adaptive learning platforms.

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Use when an application or system — including one built quickly with AI coding agents — needs a security review with regulatory grounding: a STRIDE threat model, a LINDDUN privacy threat model, a dependency exposure screen against live CVE / CISA-KEV / EPSS data, or a selected, non-exhaustive screen of which EU security obligations (GDPR, NIS2, Cyber Resilience Act, AI Act) may apply and which need determination. Orchestrates the server-enforced threat-modeling workflows of the Ansvar Gateway MCP connector and grounds every regulatory statement in officially published text fetched at answer time — scope, role, and application-date limits stated, never a compliance verdict. Never simulates a workflow and never answers legal questions from model memory.

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Public-safe preliminary screen for challenges to the impartiality of an AI evaluator, LLM-as-a-judge, decision-support agent, or adjudicative system based on prior outputs, suspected bias, unstable reasoning, or self-reinforcing memory. Use when asked whether an AI should be recused, disqualified, replaced, audited, or escalated. Does not decide real cases or claim that human judicial-recusal doctrine automatically applies.

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When you want multi-source, multi-step research on a topic — competitor research before a sales call, market research for a new business idea, positioning angles, due diligence on a partnership or podcast guest, tech decision research (which DB, which auth), or any "I need to actually understand X." Combines web search, URL fetch, agent-browser, last30days (Reddit/X/YouTube/HN/web recency), memory, and Notion. Outputs a structured brief with citations, contradictions, gaps, and recommended next steps. Archives every research run to ~/.config/makerskills/deep-research/archive/ so past work is searchable. Triggers on "/deep-research," "research X," "investigate X," "do a deep dive on X," "look into X," "what's actually happening with X," "due diligence on X," "validate this market." Differs from a one-shot web search: this is multi-pass with verification.

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When you want to create, adapt, or update an Agent Skill (Claude Code, Codex, Cursor, and other hosts) in one of your repos (listed in ~/.config/makerskills/skillify/repos.yaml; defaults to makerskills). Modes — CREATE (from chat, video, dump, or scratch) turns a workflow into a new skill. ADAPT ports an external skill with keep/adapt/add classification, license check, and attribution. UPDATE improves existing skills from learnings with cross-skill propagation, memory-vs-skill triage, and semver. Checks new skills for host portability. Triggers on "/skillify," "create a skill," "make this a skill," "skill from this chat," "adapt this skill," "port this skill," "fork this skill," "update X skill," "apply this to the relevant skills," "propagate this learning," "improve [skill]," "fix [skill]." Part of the -ify trifecta (skillify / toolify / loopify).

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Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.

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Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.

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Use for authorized security assessment of LLM applications and AI agents, including prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.

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Use when the user says 'cognee recall', 'search memory', 'what do we know about', 'find related', or 'graph search'. Runs semantic, graph, and lexical search across the Cognee knowledge graph and injects results as agent context.

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Improve the agent itself, or audit its recent performance - better skills, prompts, workflows, and config, plus a quality/reliability/memory-hygiene review of what it did and what failed

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Write and read the between-session handoff file so a fresh agent with no memory can pick up where the last one stopped without re-deriving context. Structured prose, not JSON — the model writes prose better.

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Design or audit a repo-local markdown knowledge graph with wiki links, source-code backlinks, drift checks, and searchable sections. Use when AGENTS.md/CLAUDE.md is too flat for a large codebase or when a custom harness needs durable structured project memory.

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Containerize NestJS apps with multi-stage Docker builds, tune Node.js memory, and implement graceful shutdown hooks. Use when writing Dockerfiles, configuring K8s deployments, or adding shutdown hooks for NestJS.

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Write Pest feature tests with RefreshDatabase, mock external services, and create test data with Eloquent Factories in Laravel. Use when adding HTTP tests, configuring SQLite in-memory test database, or mocking payment services.

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Optimize Flutter widget rebuilds, memory usage, rendering, and scrolling performance. Use when diagnosing jank, reducing rebuilds, or improving list performance—not performance tests, monitoring setup, or API optimization.

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Apply SAP HANA database standards for SQL parameterization, in-memory engine optimization, dynamic IN query chunking, column aliasing on joins, and datatype casting. Use when writing SQL for SAP HANA, optimizing HANA queries, or diagnosing HANA driver errors.

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Enforce universal standards for high-performance development. Use when profiling bottlenecks, reducing latency, fixing memory leaks, improving throughput, or optimizing algorithm complexity in any language.

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System health check (MOT) for skills, agents, hooks, and memory

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Daily tech product and open-source discovery digest. Fetches today's Product Hunt leaderboard and GitHub Trending top 10, then curates a personalized briefing in the user's preferred language using their memory/profile to highlight items most relevant to them. Use this skill whenever the user says "daily hunt", "今日猎奇", "tech digest", "daily digest", "what's trending", "今天有什么新产品", "PH today", "show me today's launches", "GitHub trending", "今日趋势", "morning briefing", "tech briefing", "daily brief", or any combination asking about Product Hunt + GitHub Trending together. Also trigger when the user asks about new product launches, trending repos, or wants a curated tech news digest. Even casual phrasing like "what's new in tech today" or "有什么好玩的项目" should trigger this skill.

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Proactive intelligence pulse — scans user's memory files (USER.md, SOUL.md) to identify current interests, investments, projects, and concerns, then performs targeted web searches to find noteworthy updates. Returns a concise briefing if something deserves attention, or `[SKIPPED]` if nothing warrants interruption. Use this skill when the system needs to check if there's anything worth proactively notifying the user about — like a scheduled heartbeat check, a "what's new" sweep, or when the user asks "anything I should know?", "what did I miss?", or "any updates?". Also triggers on requests like "check my interests", "scan for news", "morning briefing", or "pulse check".

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Answer questions about an earlier agent run from its recording rather than from memory, and replay or fork that run. Use when the user asks why a previous run did something, which step changed a file or broke the build, whether yesterday's failure still reproduces, or whether a different model would have got it right.

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