Direktori skill
Setiap rekomendasi tetap terhubung dengan repositori, audit, dan jalur pemasangannya.
Kata Containers is an open source project and community working to build a standard implementation of lightweight Virtual Machines (VMs) that feel and perform like containers, but provide the workload isolation and security advantages of VMs. https://katacontainers.io/
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.
Runtime Security Enforcement System. Workload hardening/sandboxing and implementing least-permissive policies made easy leveraging LSMs (LSM-BPF, AppArmor).
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
10 Fable 5-native agent skills — Works with Claude Code, Cursor, Copilot
SPD V1 Batch. Process large, user-supplied sets of legitimate product, software, startup, app, and AI-tool directory URLs across Windows, macOS, and Linux-capable environments through normalization, deduplication, execution sharding, verification-first queues, authorization-controlled form work, idempotent submission, recovery, and truthful throughput reporting. Use when coverage and operational throughput matter more than deep per-site quality analysis. Do not use for ranking manipulation, bulk link spam, invented data, CAPTCHA bypass, paid-link acquisition, forced reciprocal links, or routes prohibited by a site's terms.
Manage federated learning workload using cloud native technologies.
Scale down or delete unneeded workload after work hours based on conditions
Design and build AI agents with persistent memory, tool use, and multi-turn conversation. Covers architecture selection, memory design, model selection, tool configuration, and implementation patterns across agent frameworks. Use when creating, debugging, or improving AI agents.
Run the full end-to-end observability setup for a service after its telemetry is already flowing into SigNoz — sequence SLI/SLO capture, data exploration (RED/USE), focused dashboards, saved Explorer views, burn-rate and absent-data alerts, and a tuning loop into one opinionated, SLO-aware workflow. Make sure to use this skill whenever the user says "set up observability after ingestion", "now that data is flowing, give me dashboards and alerts", "onboard this service to SigNoz end-to-end", "I want the full monitoring setup for X", or asks to go from raw telemetry to a complete dashboard + alerts + views package — even if they don't say "observability" explicitly. This is the orchestration layer: for a single artifact (just a dashboard, just one alert, just a saved view, or one static threshold alert, or a one-off query) use signoz-creating-dashboards, signoz-creating-alerts, signoz-managing-views, or signoz-generating-queries directly.
Direktori skill
Setiap rekomendasi tetap terhubung dengan repositori, audit, dan jalur pemasangannya.
Kata Containers is an open source project and community working to build a standard implementation of lightweight Virtual Machines (VMs) that feel and perform like containers, but provide the workload isolation and security advantages of VMs. https://katacontainers.io/
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.
Runtime Security Enforcement System. Workload hardening/sandboxing and implementing least-permissive policies made easy leveraging LSMs (LSM-BPF, AppArmor).
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
10 Fable 5-native agent skills — Works with Claude Code, Cursor, Copilot
SPD V1 Batch. Process large, user-supplied sets of legitimate product, software, startup, app, and AI-tool directory URLs across Windows, macOS, and Linux-capable environments through normalization, deduplication, execution sharding, verification-first queues, authorization-controlled form work, idempotent submission, recovery, and truthful throughput reporting. Use when coverage and operational throughput matter more than deep per-site quality analysis. Do not use for ranking manipulation, bulk link spam, invented data, CAPTCHA bypass, paid-link acquisition, forced reciprocal links, or routes prohibited by a site's terms.
Manage federated learning workload using cloud native technologies.
Scale down or delete unneeded workload after work hours based on conditions
Design and build AI agents with persistent memory, tool use, and multi-turn conversation. Covers architecture selection, memory design, model selection, tool configuration, and implementation patterns across agent frameworks. Use when creating, debugging, or improving AI agents.
Run the full end-to-end observability setup for a service after its telemetry is already flowing into SigNoz — sequence SLI/SLO capture, data exploration (RED/USE), focused dashboards, saved Explorer views, burn-rate and absent-data alerts, and a tuning loop into one opinionated, SLO-aware workflow. Make sure to use this skill whenever the user says "set up observability after ingestion", "now that data is flowing, give me dashboards and alerts", "onboard this service to SigNoz end-to-end", "I want the full monitoring setup for X", or asks to go from raw telemetry to a complete dashboard + alerts + views package — even if they don't say "observability" explicitly. This is the orchestration layer: for a single artifact (just a dashboard, just one alert, just a saved view, or one static threshold alert, or a one-off query) use signoz-creating-dashboards, signoz-creating-alerts, signoz-managing-views, or signoz-generating-queries directly.
Direktori skill
Setiap rekomendasi tetap terhubung dengan repositori, audit, dan jalur pemasangannya.
Kata Containers is an open source project and community working to build a standard implementation of lightweight Virtual Machines (VMs) that feel and perform like containers, but provide the workload isolation and security advantages of VMs. https://katacontainers.io/
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.
Runtime Security Enforcement System. Workload hardening/sandboxing and implementing least-permissive policies made easy leveraging LSMs (LSM-BPF, AppArmor).
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
10 Fable 5-native agent skills — Works with Claude Code, Cursor, Copilot
SPD V1 Batch. Process large, user-supplied sets of legitimate product, software, startup, app, and AI-tool directory URLs across Windows, macOS, and Linux-capable environments through normalization, deduplication, execution sharding, verification-first queues, authorization-controlled form work, idempotent submission, recovery, and truthful throughput reporting. Use when coverage and operational throughput matter more than deep per-site quality analysis. Do not use for ranking manipulation, bulk link spam, invented data, CAPTCHA bypass, paid-link acquisition, forced reciprocal links, or routes prohibited by a site's terms.
Manage federated learning workload using cloud native technologies.
Scale down or delete unneeded workload after work hours based on conditions
Design and build AI agents with persistent memory, tool use, and multi-turn conversation. Covers architecture selection, memory design, model selection, tool configuration, and implementation patterns across agent frameworks. Use when creating, debugging, or improving AI agents.
Run the full end-to-end observability setup for a service after its telemetry is already flowing into SigNoz — sequence SLI/SLO capture, data exploration (RED/USE), focused dashboards, saved Explorer views, burn-rate and absent-data alerts, and a tuning loop into one opinionated, SLO-aware workflow. Make sure to use this skill whenever the user says "set up observability after ingestion", "now that data is flowing, give me dashboards and alerts", "onboard this service to SigNoz end-to-end", "I want the full monitoring setup for X", or asks to go from raw telemetry to a complete dashboard + alerts + views package — even if they don't say "observability" explicitly. This is the orchestration layer: for a single artifact (just a dashboard, just one alert, just a saved view, or one static threshold alert, or a one-off query) use signoz-creating-dashboards, signoz-creating-alerts, signoz-managing-views, or signoz-generating-queries directly.
Direktori skill
Setiap rekomendasi tetap terhubung dengan repositori, audit, dan jalur pemasangannya.
Kata Containers is an open source project and community working to build a standard implementation of lightweight Virtual Machines (VMs) that feel and perform like containers, but provide the workload isolation and security advantages of VMs. https://katacontainers.io/
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.
Runtime Security Enforcement System. Workload hardening/sandboxing and implementing least-permissive policies made easy leveraging LSMs (LSM-BPF, AppArmor).
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
10 Fable 5-native agent skills — Works with Claude Code, Cursor, Copilot
SPD V1 Batch. Process large, user-supplied sets of legitimate product, software, startup, app, and AI-tool directory URLs across Windows, macOS, and Linux-capable environments through normalization, deduplication, execution sharding, verification-first queues, authorization-controlled form work, idempotent submission, recovery, and truthful throughput reporting. Use when coverage and operational throughput matter more than deep per-site quality analysis. Do not use for ranking manipulation, bulk link spam, invented data, CAPTCHA bypass, paid-link acquisition, forced reciprocal links, or routes prohibited by a site's terms.
Manage federated learning workload using cloud native technologies.
Scale down or delete unneeded workload after work hours based on conditions
Design and build AI agents with persistent memory, tool use, and multi-turn conversation. Covers architecture selection, memory design, model selection, tool configuration, and implementation patterns across agent frameworks. Use when creating, debugging, or improving AI agents.
Run the full end-to-end observability setup for a service after its telemetry is already flowing into SigNoz — sequence SLI/SLO capture, data exploration (RED/USE), focused dashboards, saved Explorer views, burn-rate and absent-data alerts, and a tuning loop into one opinionated, SLO-aware workflow. Make sure to use this skill whenever the user says "set up observability after ingestion", "now that data is flowing, give me dashboards and alerts", "onboard this service to SigNoz end-to-end", "I want the full monitoring setup for X", or asks to go from raw telemetry to a complete dashboard + alerts + views package — even if they don't say "observability" explicitly. This is the orchestration layer: for a single artifact (just a dashboard, just one alert, just a saved view, or one static threshold alert, or a one-off query) use signoz-creating-dashboards, signoz-creating-alerts, signoz-managing-views, or signoz-generating-queries directly.