infranodus
De la autor
Text network analysis and knowledge graphs via the InfraNodus MCP server: analyze text structure, find content gaps, generate research questions and ideas, compare texts, optimize content for SEO/GEO, analyze Google search results, GraphRAG retrieval, structured memory. Accepts text, URLs (YouTube transcription included), and saved graphs. Also builds knowledge graphs of code repos and Obsidian vaults: invoked in a project folder ("graph this repo", "analyze this vault", "/infranodus") it mines docs, PDF text, docstrings, WHY/NOTE comments, and commit/PR/issue history into saved graphs with a report, and keeps them current by replacing the statements of changed files in place. When infranodus/manifest.json exists in the project root, answer questions about themes, decisions, rationale, or gaps by querying the existing graphs FIRST, before reading files. For an LLM-authored knowledge base built from sources (wiki pages, curated ontologies), prefer the llm-wiki skill.
Instalare
npx skills add infranodus/skills --skill infranodus -a claude-code
Se instalează în .claude/skills/infranodus/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
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Verificări de siguranță pe SKILL.md
- Include scripturi pe care nu le-am verificatScăzut
De ce 70
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- ontology-generatorinfranodus/skillsGenerate comprehensive ontological knowledge graphs in [[wikilinks]] syntax for InfraNodus visualization. Use when the user requests to create an ontology, extract entities and relationships from text, or generate knowledge graph structures. Handles both topic-based ontology generation and entity extraction from existing text. Output is formatted for direct paste into InfraNodus.com for network visualization and AI-powered gap analysis.Altele★ 122
- llm-wikiinfranodus/skillsGuide users through setting up a personal LLM-maintained wiki — a persistent, compounding knowledge base where the LLM incrementally builds and maintains interlinked markdown pages from raw sources. Use this skill when the user wants to: set up a personal knowledge base, create a research wiki, organize notes with LLM help, build a "second brain", set up an Obsidian + LLM workflow, create a persistent knowledge graph from documents, plan research priorities and next steps, or mentions "LLM wiki". This skill asks questions to understand the user's domain and goals, then scaffolds the entire wiki structure, schema, and workflows tailored to their needs. It also helps plan ongoing research by analyzing knowledge gaps and creating actionable todo lists. For a one-shot structural map of an existing repo or vault without authoring new content, prefer the infranodus skill.Știință★ 122
- context-routerllopresto87/CypressResolve the minimum set of knowledge-graph nodes needed for a task before reading any source file. Use at the start of every non-trivial task once a project has a knowledge graph — when changing a subsystem, tracing a bug, planning work, answering a question about how something works, or onboarding. This is the mechanism that keeps a large or multi-repo codebase inside a context window: it decides what to load, what to deliberately skip, and forces the agent to declare both before working. Pairs with the knowledge-graph skill, which builds and lints the graph this one traverses.Productivitate★ 116
- knowledge-graphvibeeval/vibecosystemKnowledge graph integration for token-efficient codebase understanding. Uses codebase-memory MCP for AST indexing, dependency graphs, and smart context selection. 6-71x token savings vs raw file reading.Productivitate★ 540
- geo-knowledgeyaojingang/GEOHubBuild and query an evidence-lined GEO knowledge graph from approved source bundles. Use for entity normalization, relation lineage, conflicting fact preservation, incremental source-hash updates, local/global knowledge queries, 知识图谱, 知识库, and 知识治理. Exclude unsourced facts, hidden conflict resolution, autonomous crawling, and external database mutation.Date★ 165
- whymichael-denyer/pstack-claudeUse for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.DevOps★ 3.4K
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