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Cyber Defense Log Analysis Pipeline

cyber-defense-team

FlorianBruniaux/claude-code-ultimate-guide

Orchestrates a robust, multi-stage cyber defense pipeline using specialized AI agents to analyze raw log files (e.g., nginx, auth, syslog). The system ingests logs, detects anomalies, classifies overall risk, and generates a comprehensive, actionable incident report, making it ideal for security investigations and breach analysis.

De-identifying Multilingual Clinical Text

deidentifying-multilingual-text

maziyarpanahi/openmed

This skill de-identifies sensitive PII from clinical notes across numerous languages, including Spanish, German, French, and Arabic. It automatically selects language-specific PII models, national ID regex patterns, and generates locale-aware fake surrogates. Designed for global healthcare datasets, it runs entirely on-device, ensuring maximum privacy and linguistic accuracy for diverse international clinical records.

Deploying OpenMed MCP Server Tools

deploying-openmed-mcp

maziyarpanahi/openmed

This skill deploys the OpenMed Model Context Protocol (MCP) server, exposing critical clinical NLP tools such as NER, PII extraction, and de-identification. It enables AI coding agents and chat clients to interact with OpenMed's capabilities via simple tool calls, running locally on-device to ensure user data privacy and security. Supports both stdio and Streamable HTTP deployment modes.

Agent Workflow Audit and Scoring

FlorianBruniaux/claude-code-ultimate-guide

This tool systematically audits and scores defined AI agents to ensure they are correctly scoped, highly specific, and safe for programmatic orchestration. It checks for description ambiguity, over-permissive tool grants, and anti-patterns that break headless workflows. Use it when onboarding new agents, integrating agent fleets, or debugging inconsistent multi-agent selection.

Evaluating De-identification with Leakage Gates

evaluating-with-leakage-gates

maziyarpanahi/openmed

This skill evaluates de-identification and clinical NER models against rigorous leakage-first release gates (G1a-G8). Instead of relying on aggregate F1 scores, it focuses on detecting residual Protected Health Information (PHI) leakage and enforcing per-label recall floors, which is crucial for HIPAA compliance. Determines if a model is RELEASABLE or QUARANTINED.

Extract Social Determinants And Map Codes

extracting-sdoh

maziyarpanahi/openmed

This skill extracts crucial Social Determinants of Health (SDOH) — such as housing instability, food insecurity, and transportation barriers — from unstructured clinical notes. It maps the identified text spans to standardized ICD-10-CM Z-codes (Z55–Z65). It is essential for health-equity analytics, building risk stratification tools, and identifying coding gaps by converting narrative text into queryable, discrete data points.

De-identification Leakage Detection Gate

gating-deid-leakage

maziyarpanahi/openmed

This skill implements a critical CI gate to ensure the privacy and performance integrity of de-identification models. It automatically fails the build if the model's recall on held-out PHI data drops below the defined threshold, or if any critical Personally Identifiable Information (PHI) is leaked (leakage must be zero). It enforces strict privacy standards in continuous integration pipelines, blocking unsafe merges.

Clinical Document Ingestion and Preprocessing

ingesting-clinical-documents

maziyarpanahi/openmed

This module converts diverse clinical inputs—including scanned images, faxes, CSV/TSV exports, and C-CDA XML—into a normalized, clean text format (`ExtractedDocument`). It supports on-device OCR (Tesseract/PaddleOCR) and structured redaction for tables. It is the crucial first stage for feeding de-identification and Named Entity Recognition (NER) pipelines.

Load And Cache OpenMed Biomedical Models

loading-openmed-models

maziyarpanahi/openmed

This skill manages the loading and efficient caching of OpenMed clinical/biomedical Named Entity Recognition (NER) models. Models are downloaded once from the Hugging Face Hub, enabling full offline operation afterward. It supports model reuse across multiple calls, configuring cache directories, specifying devices (CPU/GPU), and handling various model identifiers (registry keys, full HF IDs, or local paths).

OpenClaw Memory Plugin Setup and Configuration

openclaw-memory-tencentdb-setup

TencentCloud/TencentDB-Agent-Memory

A comprehensive guide for installing, configuring, and validating the local long-term memory plugin (`memory-tencentdb`) within the OpenClaw environment. This guide covers everything from environment pre-checks to advanced configuration for data capture, information extraction, context recall, and vector embedding, ensuring robust, self-contained memory capabilities for RAG applications.

OpenRouter Video Asset Generator

openrouter-video-generator

opensquilla/opensquilla

An adapter for generating short, browser-playable video assets using OpenRouter's asynchronous video generation API. This skill is designed for meta-skills to integrate dynamic multimedia content into web pages. It handles the full job lifecycle—submitting the request, polling for completion, and downloading the final MP4 asset—and adheres strictly to the required manifest reporting format.

Parsing Lab Values and Abnormal Flags

parsing-lab-values

maziyarpanahi/openmed

This skill processes clinical text to interpret laboratory results. It parses reference ranges (e.g., "135-145", "<5") and derives structured abnormal flags (low, normal, high, critical) for a given numeric value. It is unit-agnostic and respects explicit flags provided by the originating lab, making it crucial for structured clinical data extraction and interpretation workflows.

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