InfraNodus

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---
name: infranodus
description: >-
  Text network analysis and knowledge graphs via the InfraNodus MCP server:
  text structure, content gaps, research questions and ideas, text comparison,
  SEO/GEO and Google search analysis, GraphRAG retrieval, structured memory.
  Accepts text, URLs (incl. YouTube), and saved graphs. Analyzes and optimizes
  an AI assistant's memories (memory files and external memory stores):
  optimizes their structure with optimize_knowledge_base and finds gaps to
  bridge them and enhance the memories. Also graphs code repos and Obsidian
  vaults: in a project folder ("graph this repo", "analyze this vault") it
  mines docs, PDF text, docstrings, WHY/NOTE comments, and commit/PR/issue
  history into saved graphs with a report, and keeps them current as files
  change. 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.
metadata:
  homepage: https://infranodus.com
  openclaw:
    emoji: "🕸️"
    requires:
      env: ["INFRANODUS_API_KEY"]
    primaryEnv: INFRANODUS_API_KEY
---

# InfraNodus

Text network analysis and knowledge graph tools via the InfraNodus MCP server.

## How this skill talks to InfraNodus

- **Queries and analysis** — use the native MCP tools in the session
  (any InfraNodus prefix: `mcp__infranodus__<tool>`, a plugin's
  `mcp__plugin_<plugin>_infranodus__<tool>`, the claude.ai connector's
  `mcp__claude_ai_InfraNodus__<tool>`, or the plain tool name in ChatGPT
  and Codex).
  The server's own instructions and tool schemas are the authoritative
  reference for every tool's parameters and workflow patterns — do not
  duplicate or second-guess them here.
- **Not signed in** — if the InfraNodus server is installed but not signed
  in (its tools are missing or return an authentication error), tell the
  user to sign in: in Claude Code, run `/mcp`, select InfraNodus and sign
  in; in ChatGPT or Codex, reconnect the InfraNodus plugin in its
  settings. Then continue.
- **Bulk uploads (repo/vault graphs) — session server FIRST.** Prefer the
  InfraNodus MCP server already connected to this session (e.g. the
  claude.ai InfraNodus connector): the agent performs the upload itself
  with that server's `create_knowledge_graph` tool, chunk by chunk,
  following the upload contract in
  [references/repo-graph.md](references/repo-graph.md) Step 3 (one
  `graphName` per scope, heading-aware chunks, `maxNodes: 500`, the
  scope's declared `wikilinksMode`, paced calls, 429 backoff, 413
  bisection, and the same manifest + report bookkeeping the script does).
- **Fallback — the bundled script.** Only when the session has NO
  InfraNodus MCP tools connected, use `scripts/upload_scopes.py`, which
  uploads through the MCP server configured in this agent's own config —
  `<project>/.mcp.json`, then `~/.claude.json` (this project's section,
  then global), then `~/.claude/settings.json` — with project scope
  winning. An `http` entry is reached at its own `url`; a `stdio` entry
  (a local or self-hosted server) is launched as its own subprocess.
  Preflight: `upload_scopes.py [project_dir] --check-auth`. The script
  can never reach a cloud OAuth connector — that token lives remotely —
  which is exactly why the connector path above is agent-driven.
- **Credentials (script path only) are never read from config files.**
  For an `http` server the key comes from `INFRANODUS_API_KEY` in the
  environment and nowhere else, and is only sent to `infranodus.com` or
  a loopback address (any other host is refused); for a `stdio` server
  the entry's own `env` block is passed to the subprocess untouched.
  There is no endpoint default and no credential fallback: one server,
  one attempt, and a failed connection falls back to the session
  connector if one exists —
  otherwise it is a hard stop. Do not work around this by exporting a key
  you found in some other application's config

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