timescale/rsigma
by Various
A complete Sigma detection engineering toolkit: parser, linter, evaluator, correlation engine, conversion framework, streaming daemon, MCP and LSP servers :crab:
MCP
timescale/rsigma
Added 3 Sept 2026
Overview
rsigma is a Sigma detection engineering toolkit written in Rust. It includes a parser, linter, evaluator, correlation engine, conversion framework, streaming daemon, MCP and LSP servers. The project is maintained by the timescale organization.
Best for
Best for
Security engineers building and automating Sigma detection pipelines
Use cases
- Parse and lint Sigma detection rules
- Evaluate Sigma rules against event data
- Convert Sigma rules into other detection formats
How to use
Tested with
Claude Code, Cursor, VS Code
Notes
rsigma is a Sigma detection engineering toolkit written in Rust. It includes a parser, linter, evaluator, correlation engine, conversion framework, streaming daemon, MCP and LSP servers. The project is maintained by the timescale organization.
136 stars on GitHub. Last updated 2026-08-31. Licensed MIT.
Use cases
- Parse and lint Sigma detection rules
- Evaluate Sigma rules against event data
- Convert Sigma rules into other detection formats
Pros
- Covers the full detection workflow from parsing to correlation
- Rust-based implementation offers performance and memory safety
- Includes MCP and LSP servers for developer tooling integration
Cons
- Early-stage project with limited community traction at 136 stars
- The many components can require a significant learning curve
- Documentation and examples may still be sparse
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Covers the full detection workflow from parsing to correlation
- Rust-based implementation offers performance and memory safety
- Includes MCP and LSP servers for developer tooling integration
Cons
- Early-stage project with limited community traction at 136 stars
- The many components can require a significant learning curve
- Documentation and examples may still be sparse
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