اپنے ایجنٹ کے لیے موزوں صلاحیت تلاش کریں۔
Setup and initialization guide for developing AWS CDK (Cloud Development Kit) applications in Python. This skill enables users to configure environment prerequisites, create new CDK projects, manage dependencies, and deploy to AWS.
Generate Python SDK setup + CRUD + bulk + paging snippets using official patterns.
Generate a complete MCP server project in Python with tools, resources, and proper configuration
Manage NuGet packages in .NET projects/solutions. Use this skill when adding, removing, or updating NuGet package versions. It enforces using `dotnet` CLI for package management and provides strict procedures for direct file edits only when updating versions.
How to design, structure, and validate custom Copilot agents for this workspace.
Prompt for creating Product Requirements Documents (PRDs) for new features, based on an Epic.
Instrument a webapp to send useful telemetry data to Azure App Insights
Step-by-step guide for capturing key application requirements for NoSQL use-case and produce Azure Cosmos DB Data NoSQL Model design using best practices and common patterns, artifacts_produced: "cosmosdb_requirements.md" file and "cosmosdb_data_model.md" file
Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.
Add instrumentation, build golden datasets, write eval-based tests, run them, root-cause failures, and iterate — Ensure your Python LLM application works correctly. Make sure to use this skill whenever a user is developing, testing, QA-ing, evaluating, or benchmarking a Python project that calls an LLM. Use for making sure an LLM application works correctly, catching regressions after prompt changes, fixing unexpected behavior, or validating output quality before shipping.
Comprehensive guide for BlazeMeter Integrations, including APM tools, CI/CD pipelines, and development tools. Use when working with integrations for (1) Integrating APM tools (AppDynamics, Datadog, New Relic, CloudWatch, DX APM, Dynatrace, Delphix), (2) Integrating CI/CD tools (Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps, AWS CodePipeline, Bamboo, TeamCity, CircleCI, Codeship), (3) Using development tools (Visual Studio Code Plugin, MCP Server), or any other integration tasks.
Manage and synchronize AI agent skills from local SKILL.md files and remote Git repositories, generating Cursor rules with Agent Skills specification XML. This skill should be used when users need to sync skills, add/remove skill repositories, or set up the skills infrastructure.
Migrate Express.js REST APIs to Fastify with automated testing, performance benchmarking, and schema generation. Use when migrating Express applications to Fastify, modernizing Node.js APIs, improving API performance, or when users mention Express to Fastify migration, Fastify conversion, API modernization, or performance optimization of Express apps.
Generate product changelog entries, X/Twitter posts, and Slack announcements for Leather wallet releases. Use when creating release communications including changelog entries for app.leather.io/changelog, announcement tweets (single or threads), and community Slack posts. Inputs may include verbal feature descriptions, Linear projects, or GitHub PRs.
Analyze git history for hotspots, coupling, and knowledge distribution. Use when user asks "who knows this code", "what files change most", "hotspots", "bus factor", "knowledge silos", or needs to understand code evolution.
Address GitHub PR review comments end-to-end, fetch unresolved review threads (via `gh` GraphQL), implement fixes, reply with what changed, and resolve threads using the bundled scripts. Use when asked to “address PR comments”, “resolve review threads”, or “clear requested changes”.
Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.
Automate GitHub pull request review resolution by processing ALL review comments and fixing ALL failing CI checks. Use when: (1) A PR has review comments that need to be addressed and resolved, (2) CI/CD checks are failing and need fixes, (3) You want to process all PR feedback and mark conversations as resolved, (4) You need to iterate on PR feedback quickly. IMPORTANT: This skill processes EVERY comment - no skipping allowed. Always fetches fresh data from GitHub. The only acceptable end state is zero unresolved threads. COMMIT CADENCE: Each thread gets its own commit (fix → commit → resolve → next). Never batch commits at the end.
