Helps plan and implement database structure changes and data transformations using Sequelize, TypeORM, or Prisma. Includes staged changes intended to avoid downtime, plus transaction and backup examples for rolling back failed migrations.
中文简介正在准备中。当前显示英语简介。
- Renaming database columns in stages
- Splitting stored addresses into separate fields
- Preparing rollback steps for failed migrations
wshobson/agents☆ 4.1万2026年10月20日 更新
Configure AWS Verified Access for VPN-free access to internal applications. Combine user identity and device security checks, write Cedar access policies, and use Terraform and AWS RAM to deploy and share access controls across AWS accounts.
中文简介正在准备中。当前显示英语简介。
- Replacing VPN access to internal applications
- Writing policies based on users and device security
- Deploying Verified Access with Terraform
mukul975/Anthropic-Cybersecurity-Skills☆ 3.5万2026年8月31日 更新
Configure Google Cloud IAP to check identity before granting access to internal applications and virtual machines. Set permissions by user group, device, and network, configure reauthentication, enable service-account access, and record access decisions for monitoring.
中文简介正在准备中。当前显示英语简介。
- Replacing VPN access to internal Google Cloud applications
- Restricting sensitive tools to managed devices
- Configuring SSH and RDP access through IAP tunnels
mukul975/Anthropic-Cybersecurity-Skills☆ 3.5万2026年8月31日 更新
Configure Zscaler Private Access to replace VPN access with access to specific internal applications. The skill covers connectors, application groups, identity and device checks, browser access, and logging for deployment and migration reviews.
中文简介正在准备中。当前显示英语简介。
- Migrating remote access from VPN to ZPA
- Limiting application access by user group and device condition
- Providing contractors with browser access to approved applications
mukul975/Anthropic-Cybersecurity-Skills☆ 3.5万2026年8月31日 更新
Plan and configure large language model training with OpenRLHF to better match human preferences. Covers training method selection, distributed GPU setup, inference acceleration with vLLM, and troubleshooting memory or training stability issues.
中文简介正在准备中。当前显示英语简介。
- Choosing a method for training models on human preferences
- Setting up PPO training with a reward model
- Configuring GRPO training without a critic model
davila7/claude-code-templates☆ 3.3万2026年10月20日 更新
Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill explains model selection, batch processing, and memory-saving settings, with Python examples for connecting image understanding to language models.
中文简介正在准备中。当前显示英语简介。
- Generating captions for multiple images
- Building a question-answering system for images
- Searching an image collection with text
davila7/claude-code-templates☆ 3.3万2026年10月20日 更新
Use SAM to identify object regions in images from points, boxes, or existing masks. The skill provides implementation examples for transparent cutouts, automatic mask generation, annotation tools, and model selection based on available resources.
中文简介正在准备中。当前显示英语简介。
- Extracting objects with transparent backgrounds
- Building annotation tools with point or box selection
- Generating object masks automatically
davila7/claude-code-templates☆ 3.3万2026年10月20日 更新
Diagnose failures in GKE HorizontalPodAutoscaler (HPA), which adjusts Pod counts automatically. Investigate missing metrics, unexpected scaling behavior, and slow reactions, then receive proposed configuration fixes or commands for review before deployment.
中文简介正在准备中。当前显示英语简介。
- Investigating unknown metrics and HPA errors
- Diagnosing workloads that will not scale up or down
- Troubleshooting scaling to and from zero
google/skills☆ 2.1万2026年10月20日 更新
Enable PUA always-on mode from Codex CLI using a short command alias. It preserves existing configuration fields, restores feedback frequency when it is zero, and requires command output as evidence before reporting completion.
中文简介正在准备中。当前显示英语简介。
- Enable PUA always-on mode in Codex CLI
- Restore feedback frequency from zero to five
- Update PUA settings while preserving other fields
tanweai/pua☆ 2万2026年9月9日 更新
Switch PUA to offline mode from Codex CLI. This alias updates the offline setting and sets feedback frequency to zero while preserving other configuration fields, then reports the mode change.
中文简介正在准备中。当前显示英语简介。
- Switching PUA to offline mode
- Setting feedback frequency to zero
- Calling the offline command from Codex CLI
tanweai/pua☆ 2万2026年9月9日 更新
Disables PUA's always-on mode from Codex CLI and sets feedback frequency to zero. The alias updates the local configuration while preserving unknown fields, then reports completion with evidence from the executed command.
中文简介正在准备中。当前显示英语简介。
- Disabling PUA's always-on mode in Codex CLI
- Setting PUA feedback frequency to zero
- Using a Codex equivalent of Claude Code's /pua:off
tanweai/pua☆ 2万2026年9月9日 更新
Automate web page tasks such as clicking, filling forms, uploading and downloading files, and taking screenshots. Work across tabs using a browser on the connected host machine or inside a Docker container.
中文简介正在准备中。当前显示英语简介。
- Filling web forms and selecting options
- Uploading or downloading files through websites
- Capturing screenshots to inspect page appearance
agent0ai/agent-zero☆ 2万2026年10月15日 更新
Guide shell, Python, and Node.js execution on the computer running a connected A0 CLI. It explains session reuse, interactive input, output retrieval, and recovery steps when execution permissions, timeouts, or disconnections interrupt the work.
中文简介正在准备中。当前显示英语简介。
- Run shell commands on the connected A0 CLI host
- Execute Python or Node.js on the host
- Retrieve output or answer an interactive program
agent0ai/agent-zero☆ 2万2026年10月15日 更新
Run model training, fine-tuning, inference, and batch jobs on Modal’s cloud GPUs. Estimate hardware needs and cost, generate Python launch scripts, run or deploy workloads, monitor logs, and retrieve results without managing remote servers.
中文简介正在准备中。当前显示英语简介。
- Test CUDA code without a local GPU
- Run model training or LoRA fine-tuning on cloud GPUs
- Deploy a model inference API or vLLM service
wanshuiyin/Auto-claude-code-research-in-sleep☆ 1.8万2026年10月19日 更新
Create masks that identify object regions in images using points, boxes, or existing masks. The skill guides automatic mask generation, transparent-background extraction, annotation workflows, and deployment, without requiring training for each specific task.
中文简介正在准备中。当前显示英语简介。
- Extracting objects with transparent backgrounds
- Building annotation tools with point and box selection
- Generating object masks automatically
Orchestra-Research/AI-Research-SKILLs☆ 1.4万2026年10月11日 更新
Build image captioning, visual question answering, and text-based image search with BLIP-2. The skill guides model selection, batch processing, and memory optimization, with examples for understanding images without training for each specific task.
中文简介正在准备中。当前显示英语简介。
- Generate captions for multiple images
- Build a question-answering system for images
- Search an image collection using text
Orchestra-Research/AI-Research-SKILLs☆ 1.4万2026年10月11日 更新
Guide large language model training with OpenRLHF using human preference feedback. Covers training method selection, distributed GPU setup, reward model training, and memory tuning, with examples for PPO, GRPO, and DPO.
中文简介正在准备中。当前显示英语简介。
- Choosing a training method for human preference alignment
- Setting up distributed training across multiple GPUs
- Training a reward model before PPO
Orchestra-Research/AI-Research-SKILLs☆ 1.4万2026年10月11日 更新
Write, review, and debug Rust applications, including ownership and borrowing rules that govern memory use. Covers trait-based APIs, explicit error handling, asynchronous work with tokio, and tests with Cargo validation.
中文简介正在准备中。当前显示英语简介。
- Resolve ownership, borrowing, and lifetime errors
- Design APIs with traits
- Implement asynchronous tasks with tokio
Jeffallan/claude-skills☆ 1.2万2026年10月17日 更新
Analyze agent runs that fail before any work begins. Check iteration counts, tool use, logs, and outputs to identify startup failures, then direct investigation toward environment setup, input formatting, permissions, or resource limits.
中文简介正在准备中。当前显示英语简介。
- Investigating failed runs with zero iterations
- Checking startup errors across multiple agents
- Recording failures that require system review
HKUDS/OpenSpace☆ 77612026年8月13日 更新
Check generated PDFs with Python and PyPDF2 before delivery. Verify expected page counts, detect unreadable or zero-page files, and collect results across multiple documents, with optional file size checks for unusually small outputs.
中文简介正在准备中。当前显示英语简介。
- Checking PDFs against required page counts
- Detecting unreadable or zero-page PDFs
- Verifying several generated PDFs together
HKUDS/OpenSpace☆ 77612026年8月13日 更新
Identify agent failures that happen before any work starts by checking iteration counts, tool use, artifacts, and logs. Classify these incidents as pre-execution failures and guide investigation toward environment setup, initialization, and resource limits.
中文简介正在准备中。当前显示英语简介。
- Investigating agent sessions with zero completed iterations
- Classifying failures before the first tool call
- Routing incidents to infrastructure or platform teams
HKUDS/OpenSpace☆ 77612026年8月13日 更新
Analyze agent runs that fail before any work begins. By checking execution counts, tool calls, outputs, and conversation logs, this skill classifies startup failures and directs investigation toward system setup, resources, or input processing.
中文简介正在准备中。当前显示英语简介。
- Investigating failed runs with zero execution steps
- Classifying failures with no tool calls or outputs
- Routing startup incidents to infrastructure teams
HKUDS/OpenSpace☆ 77612026年8月13日 更新
Assess LoRaWAN and sub-GHz wireless devices by capturing signals, examining packet formats, and checking key reuse and replay weaknesses, where recorded messages are resent. It covers IoT networks, fixed-code remotes, and proprietary radio protocols.
中文简介正在准备中。当前显示英语简介。
- Reviewing LoRaWAN keys and frame counters
- Testing fixed-code remotes for replay weaknesses
- Analyzing proprietary wireless packet formats
SnailSploit/Claude-Red☆ 76382026年10月13日 更新
Estimate GPU memory, training step time, checkpoint costs, and recovery overhead for training or reinforcement learning. Compare state partitioning, recomputation, offloading, and device counts to assess whether a job can finish within its deadline.
中文简介正在准备中。当前显示英语简介。
- Check whether training can finish by a deadline
- Compare memory-saving methods and their time costs
- Plan checkpoint intervals and recovery overhead
bojieli/ai-infra-book☆ 67952026年10月1日 更新