Results for “elastic-siem”

Detect Kerberos Golden Ticket forgery by analyzing Windows Event ID 4769 for RC4 encryption downgrades, abnormal ticket lifetimes, and krbtgt account anomalies in Splunk and Elastic SIEM.

Detect LSASS credential dumping, SAM database extraction, and NTDS.dit theft using Sysmon Event ID 10, Windows Security logs, and SIEM correlation rules.

Designs Elasticsearch indexes and mappings, tunes queries, sizes clusters, and handles operations like shard/replica strategy, ILM, monitoring, troubleshooting, and safe reindexing or upgrades.

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

Generate, predict, and embed protein sequences and structures using ESM3, ESMC, and ESMFold2 with local or cloud inference.

Run ESM protein language models — ESM3 for generative multimodal protein design across sequence, structure, and function, and ESM C for efficient embeddings and representations — locally or via the cloud Forge API. Use when working with protein sequences, structures, or function prediction, designing novel proteins, generating protein embeddings, performing inverse folding, or doing protein-engineering tasks. Part of the AlterLab Academic Skills suite.

Conjunto abrangente de ferramentas para modelos de linguagem de proteínas, incluindo ESM3 (design multimodal generativo de proteínas em sequência, estrutura e função) e ESM C (embeddings e representações eficientes de proteínas). Use essa skill ao trabalhar com sequências de proteínas, estruturas ou predição de função; designing de proteínas inovadoras; geração de embeddings de proteínas; inverse folding; ou tarefas de engenharia de proteínas. Suporta tanto uso local de modelos quanto Forge API baseada em nuvem para inferência escalável.

Generates and analyzes proteins using ESM3 and ESM C language models, covering sequence generation, structure prediction, inverse folding, embeddings, and function conditioning with local or cloud-based Forge API inference.

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

Migrates legacy AEM (6.x, AMS, on-prem) to AEM as a Cloud Service using BPA CSV or CAM/MCP target discovery, with one-pattern-per-session workflow for scheduler, replication, event listener, HTL lint, dialog, and custom widget migration.

Implement custom AEM Workflow Java components on AEM 6.5 LTS, including WorkflowProcess and ParticipantStepChooser with OSGi registration, metadata handling, and error patterns.

Use when invoking the top-level `easel` dispatcher to discover or run Easel sequence-analysis subcommands from the HMMER toolchain.

Deploys and configures CrowdStrike Falcon EDR sensors across Windows, macOS, and Linux endpoints, sets prevention and response policies, validates deployment, and integrates with SIEM platforms.

Use when implementing elk functionality with production-grade patterns and safeguards.

Bidirectional conversion between Elm and Scala. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Elm↔Scala specific patterns. Use when migrating Elm frontend applications to Scala backends or full-stack Scala, translating The Elm Architecture to functional Scala patterns, or refactoring type-safe functional code from compile-time guarantees to more powerful type system features. Extends meta-convert-dev with Elm-to-Scala specific patterns.

Generate, inpaint, and outpaint music with ACE Step on RunComfy via the `runcomfy` CLI. ACE Step is StepFun-AI's open-weights music foundation model — tag-driven composition (genre, mood, instruments), multilingual lyrics with section markers, 5 s to 4 min stereo output, $0.0002–0.0003 per second (≈ 27× cheaper than ElevenLabs Music). Four endpoints: ACE Step text-to-audio (the default), ACE Step 1.5 text-to-audio (50+ language lyrics, refined structured-lyric handling), ACE Step audio-inpaint (regenerate a time range inside an existing track), ACE Step audio-outpaint (extend an existing track before or after). Triggers on "ace step", "ace-step", "acestep", "ACE music", "open music model", "cheap AI music", "inpaint audio", "audio inpaint", "extend music", "audio outpaint", "lengthen track", "music with tags", or any explicit ask to generate or edit music with ACE Step.

Builds and governs design systems with a three-layer token architecture, component specs, and design-to-code handoff, including slide generation.

Expert Australian Information Security Manual (ISM) advisor for government entities and their supply chains. Use for ISM control selection, gap analysis, system authorisation, IRAP assessment preparation, security documentation, and ASD compliance. Triggers on: ISM controls, ASD compliance, IRAP assessment, PROTECTED system scoping, Essential Eight vs ISM, system authorisation, NC/OS/ PROTECTED/SECRET/TOP SECRET classification markings, security objectives, ISM guidelines or chapters, control applicability markings, cybersecurity documentation for Australian government, and any question about the ASD Information Security Manual framework or Australian government cybersecurity obligations.

Idefics2: An 8B Parameters Multimodal Model

Implement Microsoft's Enhanced Security Admin Environment (ESAE) tiered administration model for Active Directory, covering Tier 0/1/2 separation, privileged access workstations, and credential theft mitigation.

Generates speech from text using ElevenLabs TTS with local playback, supporting voice selection, pronunciation rules, and audio tags.

Navigate the Ethereum L2 landscape: Arbitrum, Optimism, Base, zkSync, Unichain, and Celo. Covers how they work, deployment, bridging, and selection criteria, including 2025–2026 updates.

Model, simulate, and optimize Serializer/Deserializer (SerDes) systems — serial and parallel links — using MATLAB SerDes Toolbox. Design NRZ and PAM-N links (PAM3 through PAM16) — explore equalization architectures (FFE, CTLE, DFE), sweep or optimize parameters with genetic algorithms, and characterize channels from loss models, S-parameter files, or crosstalk scenarios. Process captured waveforms through equalization chains, build eye diagrams, and decompose jitter. Deliver IBIS-AMI models for Tx, Rx, Redriver, or Retimer by exporting to Simulink and compiling .ami/.ibs/.dll/.so files. Covers the full arc from initial design exploration and parameter optimization to compliance testing and compiled model validation, including custom datapath blocks for nonstandard equalization.

Train reinforcement learning agents using PPO, SAC, DQN, TD3, DDPG, and A2C algorithms with a scikit-learn-like API. Supports custom Gymnasium environments, vectorized environments, callbacks, and model persistence.

EVA-CLIP: Improved Training Techniques for CLIP at Scale

AWS infrastructure management — EKS, ECR, VPC, RDS, ElastiCache, S3, Route53, ACM, Secrets Manager, CloudWatch, IAM

Generate, inpaint, and outpaint music with ACE Step on RunComfy via the `runcomfy` CLI. ACE Step is StepFun-AI's open-weights music foundation model — tag-driven composition (genre, mood, instruments), multilingual lyrics with section markers, 5 s to 4 min stereo output, $0.0002–0.0003 per second (≈ 27× cheaper than ElevenLabs Music). Four endpoints: ACE Step text-to-audio (the default), ACE Step 1.5 text-to-audio (50+ language lyrics, refined structured-lyric handling), ACE Step audio-inpaint (regenerate a time range inside an existing track), ACE Step audio-outpaint (extend an existing track before or after). Triggers on "ace step", "ace-step", "acestep", "ACE music", "open music model", "cheap AI music", "inpaint audio", "audio inpaint", "extend music", "audio outpaint", "lengthen track", "music with tags", or any explicit ask to generate or edit music with ACE Step.