1.5K skills · sorted by relevance
Detect unauthorized cryptocurrency mining in cloud workloads by combining billing changes, CPU usage, mining pool connections, and process alerts. Follow workflows to preserve evidence, isolate affected resources, and trace how attackers gained access.
Parse Windows Prefetch files, which store traces of program execution, to extract run counts, timestamps, and referenced files. Build execution timelines and compare findings with other forensic evidence when investigating suspicious software or tool usage.
Reconstruct USB device usage on Windows by comparing registry records, Event Logs, and device installation logs. Identify devices, correlate connection times and drive letters, and build a timeline for removable media investigations.
Design focused products built around AI APIs, with guidance on prompts, model selection, output validation, and user experience. Track usage and set cost limits to connect product behavior with operating costs and pricing decisions.
Use LangSmith to record LLM application inputs, outputs, and timing, investigate failures, evaluate answers against test datasets, and monitor production usage. It also guides automated quality checks and analysis of token usage and costs.
Integrate the Gemini API on Agent Platform using the Google Gen AI SDK. Guidance covers authentication, text and multimedia tasks, structured JSON responses, live voice interactions, context caching, and batch prediction across five programming languages.
Gives AI agents persistent project memory for architecture, implementation patterns, and decisions. Supports saving, searching, and retrieving knowledge, automatically syncing it with project documentation, and viewing memory usage through a dashboard.
Create evaluation drafts for existing skills using actual conversations and user corrections. Produce criteria and repeatable test cases, distinguish evidence from specification assumptions, and prepare an anonymized draft for human review.
Get answers about Qwen Code usage, features, configuration, and troubleshooting from its official documentation. The skill also helps inspect and edit user or project settings files when configuration changes are requested.
Gather usage, performance, and error data from configured sources for a chosen time window. Produce a compact, saved product report with followup actions, and show its headlines in chat to support regular reviews of product activity.
Manage the AI models offered through OpenWork, including model additions, removals, aliases, and pricing discounts. Validate configuration consistency and trigger the GitHub workflow that refreshes models from models.dev, then report its merge or failure result.
Test CodexBar against real provider services, check application settings, and verify menu contents. Use command-line checks and visual evidence to distinguish authentication problems, API mismatches, and display issues before a release.
Check Screenpipe's process, API, screen capture, and audio transcription status. Inspect logs, storage, resource usage, and processing metrics to identify problems, then report the findings with relevant errors and suggested fixes.
Build Gemini API integrations on Google Cloud’s Agent Platform using the Google Gen AI SDK. Get guidance on authentication, text and media processing, structured JSON responses, function calls, caching, batch processing, and real-time voice or video interactions.
Answers questions about GKE spending using BigQuery billing exports and live cluster metrics. It breaks down costs by project, namespace, or workload and compares requested computing resources with actual usage to investigate cost increases.
Configure GKE ComputeClasses, which define preferred computing resources for workloads. Set fallback options between Spot and on-demand VMs, target GPUs or machine families, control class usage, and investigate pods waiting for suitable nodes.
Adjust application capacity on GKE through manual scaling, HPA for changing the number of running pods, and VPA for adjusting CPU and memory allocations. Includes configuration templates and a workflow for reviewing resource requests against actual usage.
Reduce Google Kubernetes Engine costs by adjusting CPU and memory requests, setting resource quotas, and choosing Spot VMs or committed use discounts. Match spending decisions to steady usage, variable demand, and workloads that tolerate interruptions.
Generate Terraform alerts for Cloud Run services, jobs, and worker pools using PromQL, a monitoring query language. Monitor errors, latency, resource usage, job failures, and queue delays, with configurable thresholds and explanations of when each alert triggers.
Create Terraform files that define alerts for AI agent latency, errors, token use, safety, security, and response quality. Check telemetry and existing policies first, then validate the generated configuration. Quality alerts require Vertex AI.
Find information you previously saw on screen and reconstruct past work from locally recorded screenshots, recognized text, and app usage. Summarize research, measure recorded screen time, and provide references that reopen the relevant moments.
Investigate software errors, version conflicts, and API questions across official documentation, GitHub, technical Q&A, and Chinese developer communities. Results distinguish matching errors, source authority, and reported environments, giving you documented leads and next checks for debugging.
Analyze ARIS usage logs to propose evidence-based changes to skill instructions, reviewer prompts, and workflow defaults. Receive a report and focused patch proposals, with selected patches staged for a separate application process.
Build, publish, and manage apps on 妙搭（Spark/Miaoda） through local development or cloud AI sessions. Create page designs, manage data and permissions, inspect logs and usage, and configure automated triggers.