NFT Log Analyser

What it is

A server that reads a log file on your disk, filters it down to its errors, groups the repeats, has a locally-run model write up each distinct failure, and files those write-ups as issues in a repository you nominate.

What you get

• A large log file cut down to its error lines before any model sees it
• Repeated events fingerprinted and deduplicated, so one recurring failure becomes one issue rather than hundreds
• Analysis against a model running on your own machine — raw log content stays on it
• Issues written with a root cause and a suggested fix, filed into your repository, with ones that already exist skipped
• A preview mode that composes the issues and shows them to you without filing anything
• Long runs handled as background jobs: the call returns a job id straight away and you poll it
• Classification rules kept as plain-English files you can edit, so the analysis learns your own stack's error patterns

Requirements

A GitHub personal access token with repository scope for the account whose repo receives the issues, a local model runtime with the model already pulled, and ripgrep. It documents macOS on Apple Silicon and wants a machine with plenty of memory, since the model runs beside your work. Setup is clone-and-build: make a Python virtual environment, install the dependencies, then point your client at the server script.

Setup effort

Follow the published steps — brew install ollama ripgrep

Tools you get

3 tools named in the project's documentation. The skills are markdown files, not tools, and dry_run is a parameter of analyze_log_file. v1 runs over stdio only, which Claude Desktop, Claude Code and Cursor all use; the HTTP+SSE transport the README lists for LangChain and n8n is planned for v2.

Requires

GITHUB_PAT in your environment

Where it runs

All 3 documented clients run the server on your machine.