Results for “greenbone”

Configure and execute authenticated vulnerability scans using OpenVAS/Greenbone Vulnerability Management with SSH and SMB credentials for comprehensive host-level assessment.

Infer gene regulatory networks from gene expression data using scalable algorithms (GRNBoost2, GENIE3) with support for distributed computation.

Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.

Plans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters, producing upgrade plans, checklists, and runbooks with gcloud commands.

Hardens Google Kubernetes Engine (GKE) clusters with Workload Identity, Secret Manager, RBAC, Binary Authorization, Network Policies, and Pod Security Standards.

Generates a per-target knowledge-base markdown file by walking the BSP root and source tree, documenting image layout, source tree structure, and document references.

Plan and apply safe, reversible headless-mode changes on Jetson devices to reclaim memory from the GUI and non-essential daemons.

Routes to specialized GKE sub-skills for cluster management, networking, security, scaling, and more on Google Kubernetes Engine.

Configures GKE workload reliability with PodDisruptionBudgets, health probes, topology spread constraints, and graceful shutdown patterns.

Plans, configures, and manages GKE networking including private clusters, VPC-native configurations, Gateway API, DNS, ingress/egress, Dataplane V2, and IP planning.

Infer gene regulatory networks (GRNs) from expression matrices using arboreto's scalable GRNBoost2 and GENIE3 tree-ensemble algorithms with Dask-distributed computation. Use when analyzing bulk or single-cell RNA-seq transcriptomics to map transcription-factor-to-target-gene regulatory interactions, build adjacency networks, or run the GRN-inference step of a SCENIC pipeline on large datasets. Part of the AlterLab Academic Skills suite.

Operate and validate a GoHome service via gRPC discovery, read-only RPC calls, and Prometheus metrics exposed over HTTP.

Operates GoHome services via gRPC discovery, metrics validation, and read-only RPC calls, with stateful actions gated on user approval.

Designs and implements blue-green deployment architectures with environment switching, traffic routing, automated rollback, and health monitoring for zero-downtime releases.

Manages vector embeddings with Pinecone for semantic search, recommendation, and RAG pipelines.

Operate GoHome services via gRPC discovery, metrics validation, and read-only RPC calls, with stateful actions gated on user approval.

Synchronize Team-Wiki markdown with GBrain database (cron-friendly or daemon mode)

GSAP Skill

Provides code examples and best practices for using Pinecone, a managed vector database for production RAG, recommendation, and semantic search applications.

Greenfield development path for products, tools, and systems that do not yet exist. G Stack validates the idea before anything is planned or built. Use when starting from zero: no existing codebase, no existing schema, no existing API that this extends. AUTO-FIRE immediately when the user says any of: - "new development", "/new-development [description]" - "I have an idea for [X]", "I want to build [X] from scratch" - "new product", "new tool", "new platform", "new application" - "build [X] — it doesn't exist yet", "start a new project" Do NOT use when an existing codebase already exists for the work — use /full-development for large brownfield work. If unsure: ask "Does any part of this already exist in code?" If yes → /full-development. This is the most token-intensive command. Use it when the investment is justified by the scope of what is being built.

Build and maintain a model book for Stockbee-style Momentum Burst setups by ingesting screener candidates, updating 3-day and 5-day forward outcomes with MFE/MAE and stop-hit status, and summarizing cohort statistics to improve setup recognition.

Audit, write, or revise README.md. Use when creating a README, editing one, or noticing smells (missing badges, no install steps, unclear purpose, no engagement guidance). Applies ddbeck's checklist and a shields.io badge principle.

Scaffolds a new ClawBio skill from a JSON/YAML spec or interactively, generating SKILL.md, Python skeleton, tests, and updating the catalog.

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `media-gen`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Generate images and videos with AIsa. Supports Gemini, Wan, and Seedream image generation plus Wan text-to-video and image-to-video models. One API key; the bundled client routes each model to the correct endpoint automatically. Use when: you need a neutral AIsa media-generation skill that spans multiple model families without changing credentials or request flow.

Unreal Engine 5 development using the VibeUE Python API. Use when working in Unreal Engine — blueprints, state trees, materials, actors, landscapes, animation, niagara, widgets, sound, foliage, gameplay tags, enhanced input, skeletons, PCG (procedural content generation), and more. VibeUE is an extension of Unreal's native MCP endpoint.

Build streaming data pipelines with Redpanda Connect using declarative YAML configs, Bloblang mappings, and component discovery. Covers running, linting, and dry-running pipelines.

Streams change data capture from Oracle Database into Redpanda or Kafka using the oracledb_cdc input in Redpanda Connect, which reads redo logs via LogMiner. Covers configuration, Oracle setup, checkpointing, and enterprise features.

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.

Imported skill daytona from langchain

Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.

Diagnoses and validates Redpanda Connect pipelines using linting, dry-run connection tests, logging, metrics, tracing, and health endpoints, including enterprise feature troubleshooting.

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

Diagnoses Redpanda broker and cluster health using debug bundles, metrics endpoints, logs, CPU profiling, and partition/raft checks, with triage playbooks for common failure modes.

Patterns for debugging and extending the AegisNex GitHub Actions CI pipeline. Covers common failure modes, encoding issues, dependency problems, and how to add new CI steps.

Predicts chromatin state across 919 tracks (histone marks, DNase, TF binding) for DNA sequences via the hosted Genomic Intelligence API, producing a report and JSON results.