“knowledge graph”的搜索结果

Explore gene, drug, disease, and phenotype associations in a downloaded PrimeKG CSV. Retrieve direct relationships, disease summaries, and paths through one intermediate node while preserving relationship types and data provenance for reproducible research and hypothesis generation.

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• Checking associations between genes, drugs, and diseases
• Summarizing genes, drugs, and phenotypes linked to a disease
• Exploring short paths between two entities

Check stored knowledge before answering substantive questions about a person, company, project, or other named subject. The skill retrieves the relevant page when needed and follows related links only when relationship details matter to the response.

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• Checking saved notes before discussing a person
• Verifying company or project details before a decision
• Resolving an unfamiliar name in a conversation

Looks up a person's identity, role, and relationship before asking the user for clarification. Searches stored knowledge and connected records, then the web, updates incomplete pages, and asks a specific question with a hypothesis when evidence remains inconclusive.

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• Identifying contacts from existing records
• Filling role and relationship fields after imports
• Adding context to names in meeting preparation

Deliver knowledge-page updates with links derived from Git paths and checked after pushing. Use defined alternatives for local repositories, rewrite paths relayed by other agents, and keep links between pages relative to preserve their connections.

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• Include verified page links in completion messages
• Investigate page links that return a 404 error
• Rewrite local paths returned by other agents for delivery

Develop C++ bots that join existing Zoom meetings from Linux servers without a graphical interface. Follow guidance for recording raw audio and video, feeding transcription or AI workflows, and handling authentication, recording permissions, and Docker audio setup.

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• Build a bot that automatically joins and records Zoom meetings
• Feed meeting audio into a transcription service
• Connect transcripts to AI summaries and action item extraction

Find prospective customers resembling your best existing customers, identify relevant decision makers, and rank them by fit, buying signals, and ease of contact. Results include reasons to reach out, source information, and a full XLSX list.

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• Finding companies similar to your best customers
• Building a prioritized prospect list
• Identifying decision makers and contact routes

Process vendor bills from email, PDFs, or photos into proposed accounting entries and a payment plan. Checks duplicates, assigns accounts and jobs, matches purchase and receiving records, and requires separate approval for ledger entries and payments.

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• Organize accumulated email, PDF, and photographed bills
• Check duplicate invoices and purchase discrepancies
• Assign expenses to accounts and jobs

Review a Figma design, screenshot, or written description for usability, visual hierarchy, consistency, and accessibility. Receive specific findings, explanations, and prioritized improvements, with feedback tailored to the design stage and intended users.

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• Reviewing an early screen design
• Checking navigation and onboarding usability
• Finding inconsistent colors, spacing, and typography

Retrieve past work, decisions, and lessons from agentmemory using keyword, meaning, and relationship searches. It expands selected records, groups findings by session, and prioritizes important observations so earlier context can inform the current task.

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• Recalling an earlier decision and its context
• Finding how a similar issue was handled
• Reviewing one project's or agent's history

Choose a technique for adding maps to Remotion videos, from satellite images with animated overlays to globe views, geographic annotations, and terrain flythroughs. Compare approaches based on the intended shot and service requirements.

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• Animate overlays on a satellite image
• Show a globe or 3D buildings with Mapbox
• Use MapLibre without an API key

Repair navigation between existing Obsidian project notes by improving meaningful links among papers, experiments, results, and writing. Keep references connected to the main note for each subject while avoiding unnecessary extra notes and excessive linking.

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• Repairing links between existing project notes
• Connecting paper notes with experiments and results
• Finding the best existing note for a new reference

Use whenever the agent creates, writes, moves, or renames a file in a team/delegate (shared) workspace, OR when the user asks to organize, clean up, restructure, audit, or find files in any workspace or the Vault, OR when starting a multi-file task or named project. Enforces a purpose-based folder convention (flat mode: notes/, data/, outputs/, scripts/, archive/; project mode: projects/<slug>/{docs,assets,source,reports,research}/), per-agent namespacing under shared/<agent_key>/, and pre-write discovery via memory_search, vault_search, knowledge_graph_search to surface related files and avoid duplicates. Trigger before any write_file or exec at workspace root, when starting a project, generating reports/assets/exports, delegating, or when the user says "messy", "where did I save", "tổ chức lại", "dọn workspace", "tạo report", "find related", "search vault". Do NOT trigger for read-only ops, edits inside an existing project tree (cloned repo), or short-lived files deleted in the same turn.

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Show vault statistics and knowledge graph metrics. Provides a shareable snapshot of vault health, growth, and progress. Triggers on "/stats", "vault stats", "show metrics", "how big is my vault".

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Apply plugin knowledge base updates to an existing generated system. Consults the Ars Contexta research graph for methodology improvements, proposes skill upgrades with research justification. Never auto-implements. Triggers on "/upgrade", "upgrade skills", "check for improvements", "update methodology".

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Research a topic and grow your knowledge graph. Uses Exa deep researcher, web search, or basic search to investigate topics, files results with full provenance, and chains to processing pipeline. Triggers on "/learn", "/learn [topic]", "research this", "find out about".

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Create text-driven visuals in Markdown: charts, diagrams, cards, architecture and page layouts. Charts and analytical views (bar, line, pie, heatmap, correlation, regression); reliability and operations (latency, incident, throughput, cycle time, OKR, standup, on-call); product and finance (funnel, retention, revenue, budget); process and workflow (approval, BPMN); software design and behaviour (class, state machine, sequence, C4); dependencies and impact (dependency graph, ER, causality); system architecture as HTML (layer stack, wings, zones, topology, service catalogue, request paths); infrastructure (cloud, Kubernetes, ETL, network, security, IAM, compliance); governance and people (ArchiMate, org chart, hiring); knowledge and planning (mind map, roadmap, Gantt, migration); documents (comparison, SWOT, memo, policy, catalogue, case study). Use when a document needs a diagram, chart or card. Figures render live. Not for slide decks or math notation. Not recommended: mermaid / canvas / drawio / dot.

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Use when the user asks to "optimize entity presence", reconcile an entity identity, or update canonical Knowledge Graph facts; audits and maintains machine-facing identity, sameAs, schema, disambiguation, and AI-recognition evidence through the entities registry. Not for page-level AI-citation readiness - use geo-content-optimizer; not for human-facing brand canon - use narrative-registry. 实体注册/知识图谱

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Render a self-contained HTML viewer for a pro-workflow wiki. Pages, sources, claims, seed queue, page-link graph and full-text search all in one file. No external dependencies, no JS framework, S3-uploadable. Use when the user wants to browse a wiki visually, share its current state with someone, audit research progress, or hand off a knowledge base. Inspired by Thariq Shihipar's "Unreasonable Effectiveness of HTML" — favors information density and shareability over markdown-only outputs.

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Routes any task involving AWS databases — choosing, comparing, recommending, getting started with, or operating a database — to the correct service-specific skill. Supersedes general training-data knowledge with post-training service updates, corrected limitations, and decision procedures for relational (Aurora, DSQL, RDS), key-value (DynamoDB), wide-column (Keyspaces), document (DocumentDB), graph (Neptune), time-series (Timestream), and in-memory/caching (ElastiCache, MemoryDB) workloads. Activates when a user describes building an application on AWS that will store, retrieve, or manage data, even if they do not mention 'database' explicitly.

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Acknowledges limitations in sample, design, measurement, and validation in a professional way that improves credibility without undermining the whole paper. Use when writing the limitations paragraph of a Discussion section, preparing a grant risk assessment, responding to reviewers about study weaknesses, or framing scope boundaries for a paper. Also triggers on "write my limitations", "how should I address the limitation of", "reviewer said my sample is too small", or "help me word this limitation".

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HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.

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Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)

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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.

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Use when someone asks to build a 3D brain, visualize their AIOS or second brain, turn their knowledge into an interactive graph, or run /3d-brain or /3D brain.

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