Automate incident response with anomaly detection, root cause analysis, and code fixes.
npx skills add https://github.com/hariprasath-dlh/rootmind --skill rootmind
Please help me install this Agent Skill. Skill: rootmind Source: https://github.com/hariprasath-dlh/rootmind/tree/main Command: npx skills add https://github.com/hariprasath-dlh/rootmind --skill rootmind
This skill automates incident response and observability in modern software systems, reducing MTTR and minimizing downtime costs by detecting anomalies, performing root cause analysis, generating code fixes, and documenting incident post-mortems.
Autonomous 5-Agent Pipeline: Coordinating detectors, RCA analysts, fix suggesters, technical writers, and alerting utilities.
ML-Based Anomaly Detection: Identifies CPU, memory, and latency spikes using an Isolation Forest classifier.
RAG-Powered Root Cause Analysis: Locates the root cause using Qdrant Cloud Vector Database and Groq (Llama 3) LLM.
AI Code Patch Generation: Analyzes git diffs and drafts a precise context-aware patch.
Automated Post-Mortem Reports: Compiles incident logs, timelines, and diffs into Markdown format.
Slack Integration: Posts alerts to operational channels with color-coded severity cards and links.
GitHub Integration: Reads repository structures, commits, and diff files using the GitHub REST API.
SQLite Database Persistence: Tracks full historical metrics and remediation reports.
Memory Engine: Remembers past incident patterns to speed up diagnostics over time.
Beautiful Cinematic UI: Sleek dark-mode dashboard with real-time incident feeds, interactive node charts, and animations.
Use Cases: Production outage detection, root cause tracing, automated fix generation, incident documentation, team notification, pattern recognition, compliance audit trails, engineer onboarding.
Trigger the RootMind AIOps platform to detect and resolve an incident.
High-intent search queries and answers about installing and using this skill.
Automate incident response and root cause analysis using a multi-agent AI platform that detects anomalies with Isolation Forest, queries Qdrant Cloud for context, and leverages Groq Llama 3 to identify the root cause. The pipeline autonomously traces production outages down to the specific git diff.
AI code patch generation and automated post-mortem reports are triggered immediately after anomaly detection. The multi-agent pipeline analyzes git diffs via the GitHub API to draft precise context-aware patches, while a technical writer agent compiles incident logs, timelines, and diffs into Markdown format.
Machine learning anomaly detection for CPU, memory, and latency spikes works by training an Isolation Forest classifier on system metrics. The detector agent continuously monitors data streams to identify abnormal patterns, triggering the root cause analysis pipeline when an outlier is found.
You need FastAPI, LangGraph, Scikit-learn, Groq, Qdrant Cloud, Slack API, GitHub API, and SQLite to run this AIOps multi-agent pipeline. FastAPI serves the application, LangGraph orchestrates the agents, Qdrant provides RAG vector storage, and SQLite handles historical incident persistence.
This incident response platform integrates directly with Slack and GitHub for team notifications. The alerting utility posts color-coded severity cards and links to operational Slack channels, while the GitHub REST API reads repository structures, commits, and diff files to support root cause analysis.
The best way to retain historical incident patterns for faster diagnostics is using a memory engine backed by SQLite. The platform tracks full historical metrics and remediation reports, allowing the multi-agent system to remember past incidents and significantly speed up future root cause tracing.
jusjinukConfigure Slack tokens, environment files, and repository hooks for internbot deployment.
TheBushidoCollectiveGenerate incident response runbooks with severity definitions and communication templates.
mkurmanAnalyze censored survival data and build time-to-event models with scikit-survival.
saeed-vayghanDiagnose production incidents and coordinate remediation to reduce MTTR.
