“knowledge graph”的搜索结果

Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface (18 tools) available with --full flag. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.

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Use when extracting structured information from text - named entity recognition, relation extraction, coreference resolution, knowledge graph construction, and information extraction pipelinesUse when ", " mentioned.

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Expert knowledge for real-time and baked lighting in games - from cinematography fundamentals to engine-specific optimization, covering GI, time-of-day, volumetrics, and platform-aware lighting pipelinesUse when "lighting, light design, baked lighting, realtime lighting, lightmap, lightmapping, light probe, reflection probe, global illumination, GI, ambient occlusion, shadow, shadow cascade, time of day, day night cycle, volumetric fog, volumetric lighting, god rays, HDR, tonemapping, bloom, exposure, emissive, area light, Lumen, Enlighten, ray tracing, RTGI, light baking, lighting, global-illumination, lightmapping, shadows, GI, light-probes, reflection-probes, HDR, tonemapping, volumetric, fog, time-of-day, baked, realtime, mixed-lighting, lumen, enlighten, radiosity, ray-tracing, RTGI" mentioned.

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Applies the philosophical frameworks of Plato (ancient Greek philosopher, Theory of Forms, founder of the Academy) to structural, ethical, and epistemological problems. Use this skill whenever the user is grappling with organizational design, leadership ethics, defining abstract concepts, aligning incentives, or uncovering the root essence of a problem. Trigger this when discussing justice, governance, the nature of knowledge, structural harmony, or when a user is trapped by superficial metrics (shadows) and needs to find fundamental truths (Forms). Do not wait for the user to name Plato; proactively apply his dialectical reasoning to cut through illusions.

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Applies the empiricist, skeptical, and practical reasoning of David Hume, 18th-century Scottish philosopher. Use this skill whenever you encounter questions about epistemology, causality, human nature, moral philosophy, political authority, or the limits of rational knowledge. Trigger this skill when the user is grappling with abstract theories that need grounding, evaluating causal claims, discussing the role of emotion vs. reason in decision-making, or questioning the legitimacy of institutions. Even if the user doesn't name Hume, apply his frameworks (like Hume's Fork or the Impression Test) to cut through metaphysical speculation, ground arguments in observable experience, and recognize that reason is ultimately guided by human sentiment.

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Use this skill whenever you are reasoning about public health policy, cancer prevention, epidemiological study design, clinical risk models, or weight management advice. This skill channels the thinking of Graham A. Colditz, epidemiologist at Washington University in St. Louis. Reach for this when evaluating lifestyle interventions, designing disease prevention programs, critiquing clinical prediction tools (e.g., AUC vs. clinical utility), or analyzing life-course risk accumulation. Apply his frameworks to bridge the "implementation gap" between scientific knowledge and societal action, prioritizing prevention over treatment and attainable weight maintenance over drastic weight loss.

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Applies the reasoning of David Silver, lead researcher on AlphaGo and AlphaZero at DeepMind, to problems of AI design, reinforcement learning, and open-ended discovery. Use this skill whenever you are designing AI systems, evaluating learning algorithms, balancing exploration vs. exploitation, choosing research problems, or discussing how to break past human performance ceilings. Reach for this whenever the user asks about self-play, Monte-Carlo Tree Search, tabula rasa learning, AGI, or moving from human-curated data to autonomous experience. It helps shift the focus from hardcoding human knowledge to building systems that learn for themselves.

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Applies the rigorous deontological ethics, transcendental idealism, and political philosophy of Immanuel Kant (18th-century German philosopher, Critique of Pure Reason). Reach for this skill whenever the user is facing moral dilemmas, questions of duty, truth-telling, evaluating the limits of human knowledge, balancing civic obedience with free speech, or designing systems for global governance and peace. Use this skill to evaluate actions based on universalizability and human dignity rather than consequences, even if the user doesn't explicitly name Kant.

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Applies the Neoplatonic and mathematical reasoning of Hypatia of Alexandria. Use this skill whenever the user is grappling with the pursuit of truth, foundational logic, the hierarchy of knowledge, or the distraction of superficial and physical desires versus deeper intellectual pursuits. Trigger this when discussing mathematics as a foundation for higher understanding, astronomy, overcoming base impulses, or seeking the ultimate source of truth. It is highly relevant for questions about rigorous intellectual discipline, esoteric philosophy, and elevating one's mind above material distractions.

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Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

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Use when asked to prove, disprove, investigate, or find evidence for a claim about people, concepts, ideas, or their relationships. Also use when asked to explore connections, find who influenced what, trace how ideas developed, or answer questions that require reasoning over multiple linked notes.

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Search Cognee memory. Session memory is automatically searched on every prompt via hooks. Use this skill explicitly for permanent knowledge graph search, filtered category search, or when you need more results than the automatic lookup provides.

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Sync session cache entries into the permanent Cognee knowledge graph. Run this to make session memory searchable, or it runs automatically at session end.

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Store data permanently in the Cognee knowledge graph. Accepts a data category (user, project, or agent) to tag the data with the correct node_set for filtered retrieval.

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A multi-day digest of the user's own coding-agent sessions — sessions per day and project, most-edited files, and every lesson Cognee distilled into the knowledge graph in that window. Use only when the user explicitly asks for a weekly digest, a retro, a recap or report of their recent sessions, or asks for the `cognee-digest` skill by name. Not for questions about code or the current task.

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Forget something stored in Cognee memory: find the documents in the current dataset whose content matches by meaning, confirm, then delete them and their graph knowledge. Deletes a whole dataset only on explicit request.

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Store a fact, preference, decision, note or file permanently in the Cognee knowledge graph, tagged as user, project or agent. Plain "remember this" requests go here, not to Claude's built-in memory.

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Search Cognee memory beyond the automatic per-prompt recall: a deeper knowledge-graph search, more results, one category (user, project or agent), cited sources, or a one-off search of another dataset without switching.

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Sync the current session from Cognee's session cache into the permanent knowledge graph now (Q&A turns, tool feedback, distilled lessons), so it becomes searchable and shows up in digests. Also runs at session end.

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Create, redesign, repair, validate, preview, and package lightweight standalone HTML deliverables. Use for marketing experiences, product prototypes, infographics, motion narratives, documents, browser tools, dashboards, guided workflows, knowledge interfaces, mini-games, presentations, reusable style and brand systems, and local-model text, vision, image, or code tools that must run without a build chain or hosted service.

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Interleave layer bridging the BigQuery cluster to plurigrid/asi. Routes BigQuery queries through asi's DuckDB stack, wires patent search into asi knowledge graph, connects Looker Studio dashboards to CatColab, and feeds BigQuery ML into the lolita physics emulation pipeline.

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Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration. Use when: the task directly matches context manager responsibilities within plugin agent-orchestration. Do not use when: a more specific framework or task-focused skill is clearly a better match.

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Git-as-knowledge-graph workflow for traceability. Use when planning work, brainstorming designs, creating/managing issues and PRs, tracking architectural decisions, or resuming prior sessions. Slash command /shiplog.

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Use GBrain to give OpenClaw or Hermes-style agents a local knowledge brain with markdown ingestion, entity links, hybrid search, scheduled consolidation, and MCP access.

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