489 skills (repositories by github) · sorted by popularity
Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.
Add Arize AX tracing to an LLM application, recording how model calls and tool executions connect. Inspect the existing code, select suitable integrations, add instrumentation, and verify that the application runs and traces reach Arize.
Manage the credentials Arize uses to call LLM providers such as OpenAI and Anthropic. Create, inspect, update, or delete connections, configure allowed models and authentication methods, and retrieve connection IDs for use in evaluations.
Compare C# Web API source code with its TypeScript or JavaScript clients to find mismatched data fields, required inputs, and response handling. Receive an audit report showing client impact, severity, and confidence in each match.
Set up telemetry, data about application operation, for web apps hosted in Azure. Choose automatic or manual instrumentation, create an Azure App Insights resource, and configure application code to send monitoring data.
Review an iOS app’s code, settings, and submission materials for App Store rejection risks. Receive prioritized findings with supporting evidence, concrete fixes, verification steps, and draft notes to help reviewers test the app.
Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.
Create, review, migrate, or troubleshoot Azure Developer CLI projects. Align application settings, infrastructure definitions, deployment environments, secrets, and automated delivery workflows while checking that configuration files and deployment requirements agree.
Set up a Python project with AWS CDK, a toolkit for defining cloud infrastructure in code. Follow guidance on prerequisites, AWS credentials, virtual environments, dependencies, template generation, deployment preparation, and reviewing changes before deployment.
Ask plain-language questions about AWS resources, settings, and costs, and receive readable tables using read-only AWS CLI commands. Inspect services and filter results by region, state, or tags without creating, changing, or deleting resources.
Check Bicep infrastructure definitions before deploying to Azure. Validate syntax, preview resource changes, check deployment permissions, and produce a Markdown report of issues and recommendations using Azure CLI or Azure Developer CLI workflows.
Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
Review AI prompts for harmful output risks, bias, privacy concerns, security weaknesses, and clarity. Receive an assessment, a revised prompt, and suggested tests with explanations of the principles behind each improvement.
Assess an AWS resource using CloudWatch logs and metrics, the measurements that describe its behavior. Investigate failures and performance issues, classify their severity, and produce a remediation report with staged actions, CLI commands, and monitoring recommendations.
Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.
Coordinate a small AI development team with planning, implementation, and optional independent testing roles. Adapt the process to the work’s complexity, prepare pull requests, and preserve decisions and next steps so later sessions can continue.
Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.
Design, refine, or review web and iOS interfaces using the product’s existing components and visual style. Consult relevant screen examples when useful, then inspect the result for layout problems, inaccessible controls, and broken interactions.
Analyze a codebase to document its technologies, architecture, dependencies, and implementation patterns. Produce a Markdown reference with optional diagrams, code examples, and guidance for adding features consistently with the existing design.
Edit patch release notes for GitHub Enterprise Server, correcting typos and style while preserving approved technical facts. Keep repeated notes consistent across versions and update known issue source comments so future releases inherit the same edits.
Edit and validate Bazel build settings using conventions specific to CodeQL development. Follow established helpers for packaging and operating system differences, add dependencies with checksums, and check changed targets while respecting the standalone build setup.
Maintain reference test data and expected results for model-routing audits, which analyze how AI models are selected. Investigate mismatches, update outputs after intentional behavior changes, and add captured workflow runs as new test cases.
Guide visual changes to an existing documentation site and landing page. Apply defined limits for text width, spacing, navigation, and color so layout updates preserve readable content and a consistent visual style.
Turns collected repository statistics into an eight-panel PNG infographic. It shows pull request outcomes, timing, code changes, and human versus agent activity, with scripts to recalculate figures and check them against available source records.