gesh75

Netlog AI — AI skill for Claude Code

AI community

v0.6.0 causal console.

How to install Netlog AI

This entry records only its repository, not the path inside it, so there is no exact command to give. Open gesh75/netlog-ai and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Netlog AI does

v0.6.0 causal console. AI-powered network log analyzer with LLM-assisted RCA (Junos / EOS / FRR / SONiC / Cumulus). Local, Claude, or Grok. Sanitize-before-LLM. 423 tests. MIT.

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README

netlog-ai — architecture

netlog-ai

📖 Live documentation

[![netlog-ai — live documentation](docs/assets/preview.png)](https://gesh75.github.io/netlog-ai/)

🌐 **Live:** — an animated single-page guide: architecture diagrams, data flow, tech stack, and quickstart.

🗂️ Part of the **[gesh75 documentation hub](https://gesh75.github.io/)** — all my network & AI engineering project docs in one place.

**Network logs in. Ranked actions out.** A local, dark-themed dashboard that classifies syslog events from any vendor (Junos, Arista EOS, FRR, IOS-XE, NX-OS, SR Linux, SONiC, Cumulus), builds a prioritized action list, and lets an LLM write the root-cause analysis with copy-pastable CLI fixes.

[![CI](https://github.com/gesh75/netlog-ai/actions/workflows/ci.yml/badge.svg)](https://github.com/gesh75/netlog-ai/actions/workflows/ci.yml) ![Tests](https://img.shields.io/badge/tests-423%20passing-brightgreen) ![License](https://img.shields.io/badge/license-MIT-blue) ![Python](https://img.shields.io/badge/python-3.10%2B-blue) ![Stack](https://img.shields.io/badge/stack-Flask%20%2B%20vanilla%20JS-1f6feb)

📓 Recent changes — **0.6.0 causal console** (Grok, timeline, blast radius, SONiC/Cumulus) — are in [`CHANGELOG.md`](CHANGELOG.md).

Why this exists

Most "AI for ops" tools either ship your data to a SaaS or hide what the model actually saw. **netlog-ai** runs entirely on your laptop:

Configs and logs never leave the host — the LLM only sees pre-sanitized text (passwords, public IPs, SSH keys redacted before any outbound call).
Pluggable LLM backend — local Docker Model Runner (Qwen, Llama), Anthropic Claude, or xAI Grok. No telemetry, no API keys required for the local path. Every provider is sanitize-first.
Every finding ships with executable CLI: Junos set lines, EOS running-config patches, FRR vtysh com