KS
Killer-Skills

rs — how to use rs how to use rs, rs setup guide, rs alternative, rs vs other CTI platforms, what is rs, rs install, rs CTI studio, AI-assisted Sigma rule generation, OSINT ingestion tools

v1.0.0
GitHub

About this Skill

Perfect for Threat Analysis Agents needing automated Sigma rule generation and OSINT ingestion rs is an open-source CTI platform that enables OSINT ingestion, observable extraction, and AI-assisted Sigma rule generation with validation

Features

Runs rescore commands for keyword/regex hunt scores and ML hunt scores
Utilizes `threat_hunting_score` in article metadata for scoring
Leverages `ml_hunt_score` from chunk-level model predictions for ML-based scoring
Executes `./run_cli.sh rescore --force` and `./run_cli.sh rescore-ml --force` commands
Supports validation of AI-assisted Sigma rules

# Core Topics

dfirtnt dfirtnt
[2]
[0]
Updated: 2/26/2026

Quality Score

Top 5%
48
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add dfirtnt/Huntable-CTI-Studio/rs

Agent Capability Analysis

The rs MCP Server by dfirtnt is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for how to use rs, rs setup guide, rs alternative.

Ideal Agent Persona

Perfect for Threat Analysis Agents needing automated Sigma rule generation and OSINT ingestion

Core Value

Empowers agents to rescore articles using keyword/regex hunt scores and ML hunt scores from chunk-level model predictions, leveraging threat_hunting_score and ml_hunt_score metadata, and utilizing CLI commands like ./run_cli.sh rescore and ./run_cli.sh rescore-ml

Capabilities Granted for rs MCP Server

Automating Sigma rule generation for threat hunting
Validating scoring rules after retraining machine learning models
Rescoring articles after updating keyword/regex rules

! Prerequisites & Limits

  • Requires CLI access
  • Dependent on OSINT ingestion and observable extraction
  • Limited to Sigma rule generation and validation
Project
SKILL.md
882 B
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

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SKILL.md
Readonly

RS — Rescore All Articles

When the user says rs, run both rescore commands. Do not commit or push; this is data-only.

Commands (in order)

  1. Keyword/regex hunt scoresthreat_hunting_score in article metadata:

    bash
    1./run_cli.sh rescore --force
  2. ML hunt scoresml_hunt_score from chunk-level model predictions:

    bash
    1./run_cli.sh rescore-ml --force

When to use

  • After changing scoring rules (keyword rescore).
  • After retraining the ML model or changing aggregation (rescore-ml).
  • To backfill or refresh all article scores.

Optional scope

  • Single article: ./run_cli.sh rescore --article-id ID --force and ./run_cli.sh rescore-ml --article-id ID --force.
  • Dry run: add --dry-run to either command to preview without writing.

Out of scope

  • No git add / commit / push (use lg for that).

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