'graphs' 검색 결과

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 to reason like Martin Heidegger, 20th-century German philosopher and author of Being and Time. Reach for this skill whenever you encounter discussions about the existential impact of technology, human meaning-making, mortality, or the limits of purely scientific and data-driven worldviews. Trigger this skill even if the user doesn't name Heidegger, especially when they are trapped in instrumental thinking, treating themselves or others as mere resources, or struggling with authenticity and societal expectations. Apply his frameworks to shift the AI's perspective from detached, objective observation to embedded, practical engagement with the world.

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Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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Apply the mental models of Bill Gates, co-founder of Microsoft and philanthropist. Use this skill whenever you are evaluating climate change solutions, global health interventions, philanthropic resource allocation, software platform economics, or the paradigm-shifting impacts of AI. Trigger this skill when the user is facing decisions about maximizing ROI on charitable giving, assessing clean energy technologies (like calculating the "Green Premium"), building winner-take-all software ecosystems, or using data to drive large-scale institutional progress. Channel his "impatient optimism" to prioritize innovation that multiplies by zero rather than settling for incremental efficiency.

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Applies the computational biology and AI-driven reasoning of Aviv Regev (computational biologist, Genentech, single-cell genomics). Use this skill whenever the user is dealing with experimental design, high-dimensional data analysis, integrating AI into scientific workflows, scaling biological research, or navigating noisy, complex systems. Trigger this for topics like single-cell genomics, drug discovery, biological atlases, interdisciplinary research strategy, or when deciding between depth vs. breadth in data collection.

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Applies the strategic frameworks and mental models of Anne Wojcicki, co-founder and CEO of 23andMe. Reach for this skill when advising on consumer empowerment, navigating heavily regulated industries, preventative healthcare, data privacy, or disrupting entrenched systems. Use this when the user is dealing with misaligned industry incentives, facing regulatory hurdles, building a consumer-research flywheel, or managing a team through a post-launch 'trough of sorrow'. Apply her principles of 'speaking with data', treating users as partners, and pushing for decade-long systemic change from the outside.

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Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.

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Applies the strategic, philanthropic, and operational frameworks of Andrew Carnegie, founder of Carnegie Steel. Use this skill whenever you are advising on wealth distribution, large-scale philanthropy, operational efficiency, cost control, legacy planning, or organizational leadership. Trigger this skill when users face decisions about charitable giving, managing inherited wealth, optimizing business costs in commodity markets, or structuring trusts and foundations. Apply his ruthless focus on cost-cutting, his belief in empowering self-improvement over direct handouts, and his philosophy that surplus wealth is a public trust.

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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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Applies the epidemiological reasoning and population-health frameworks of Albert Hofman (Harvard epidemiologist, Rotterdam Study). Trigger this skill whenever you are analyzing public health strategies, preventive medicine, cohort study design, cardiovascular or neurodegenerative disease risks, or healthy aging. Use it when evaluating whether to use population-wide interventions versus individual screening, assessing risk factors in elderly populations, or tracing adult chronic diseases back to early-life or fetal origins.

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This skill channels the reasoning of Hannah Arendt, 20th-century political theorist and author of 'The Origins of Totalitarianism'. Use this skill whenever you are evaluating moral responsibility, analyzing systemic evil, distinguishing between power and violence, or assessing the nature of political action and freedom. Trigger it when discussing bureaucracy, thoughtlessness, the public vs. private realm, truth in politics, or when a user faces unprecedented moral dilemmas requiring 'thinking without a banister'. Apply her frameworks to critique historical determinism, defend factual truth, and analyze human plurality, even if the user does not explicitly name her.

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A library for building, validating, visualizing, and serializing dialogue graphs. Use this when parsing scripts or creating branching narrative structures.

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Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.

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Run Neo4j Graph Analytics algorithms (PageRank, Louvain, WCC, Dijkstra, KNN, Node2Vec, FastRP, GraphSAGE) directly inside Snowflake without moving data. Use when running graph algorithms against Snowflake tables via the Neo4j Snowflake Native App ("GDS Snowflake", "graph algorithms in Snowflake", "Neo4j Graph Analytics"). Covers the explore → prepare projection views → project-compute-write flow, the strict view/column type rules the graph engine requires, exact SQL CALL syntax, and privilege setup in both modes — app-identity grants and execute-as-user / per-user PAT auth (programmatic access token, set_enable_custom_credentials, register_user_role, caller grants). Does NOT cover Cypher or Neo4j DBMS queries — use neo4j-cypher-skill. Does NOT cover Aura Graph Analytics — use neo4j-aura-graph-analytics-skill. Does NOT cover self-managed GDS — use neo4j-gds-skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plan, write, art-direct, generate, and package a coherent 2-10 image minimal-zine social series from notes, articles, photographs, travel stories, products, reviews, cultural topics, data, PDFs, or mixed subjects. Use for Xiaohongshu/小红书图片, Instagram carousels, social visual essays, recommendation posts, photo diaries, evidence-backed explainers, or any multi-image editorial sequence. Produces analysis, card roles, subject-aware composition, saved prompts, real raster images, natural caption copy, evidence provenance when needed, and series-level QA.

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Plan, art-direct, generate, edit, and quality-check tactile minimal-zine bitmap imagery from almost any subject: landscapes, portraits, objects, products, architecture, interiors, existing photographs, moods, article ideas, cultural essays, data, reviews, and social-media card series. Use for 小红书图片, Instagram carousels, poetic posters, editorial covers, photo restyling, visual essays, evidence-aware explainers, or any request for aged paper, photocopy, risograph, letterpress, sparse type, and restrained single-ink character. Routes the subject before choosing composition, preserves identity and factual content when editing, saves reproducible prompts, invokes the runtime-native image generator, and audits the real raster output. Do not use for glossy commercial ads, deterministic UI/vector assets, or exact diagram rendering.

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Expert guide for Knowledge Graphs, GraphRAG, Microsoft GraphRAG, Neo4j Text2Cypher, multi-hop relational retrieval, and hybrid vector-graph search / Panduan ahli Knowledge Graph, GraphRAG, dan pencarian relasional multi-hop.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert guide for in-memory Abstract Syntax Tree (AST) code knowledge graphs, real-time symbol dependency mapping, refactoring blast radius calculation, and dead code eradication / Panduan ahli graf pengetahuan AST kode in-memory, pemetaan dependensi simbol real-time, kalkulasi radius dampak refaktor, dan pembersihan kode mati.

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Expert guide for AI-assisted coding workflows — agentic code generation, multi-agent code swarms, self-healing CI/CD, automated PR review, spec-to-code pipelines, codebase knowledge graphs, and human-in-the-loop approval gates / Panduan ahli untuk workflow pengkodean berbasis AI — generasi kode agentic, code swarm multi-agen, CI/CD self-healing, review PR otomatis, pipeline spec-to-code, knowledge graph codebase, dan gate persetujuan human-in-the-loop.

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Designs and troubleshoots NUKE-based CI/CD pipelines for .NET services. Use when refactoring target graphs, splitting test flows, publishing reports, or diagnosing slow pipelines.

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Use when an agent must stand up the OpenClaw↔Cognee integration with FalkorDB as the vector + graph store. This skill is SELF-CONTAINED — it tells you to generate every file (Dockerfile, sitecustomize.py, docker-compose.yaml) from the exact contents below, build a custom Cognee image that loads the FalkorDB adapter, run Cognee + FalkorDB together, self-verify the adapter registered, and point the OpenClaw cognee plugin at it (including per-agent graphs). No other files in this directory are required.

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Build, query, and inspect the project knowledge graph in .planning/graphs/

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Persistent state tracking and Architecture Decision Records across sessions. Use when recording a decision, ADR, or progress that must survive session boundaries. Manages progress state, module health, dependency graphs, and ADRs for any workflow.

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