Your agent can do more. Find the skill that makes it happen.
Find your next superpower If you are an agent, refer to our llms.txt for full access.Evaluates whether product work is AI-first or AI-shaped. Helps teams assess their AI maturity and choose the next capability to build.
This stock analysis system combines multiple data sources and real-time news with LLM-powered analysis and AI decision dashboards. It uses GitHub Actions to support multi-channel push notifications.
Supports Blender MCP tools and Python scripts for inspecting and manipulating scenes, extracting materials and animation, and exporting GLTF/GLB for the web. Covers export failures, material preservation, texture optimization, headless workflows, and asset integrations.
Supports repository-level discovery to map codebases, document architecture, and help users onboard. Focuses on explicit codebase exploration and documentation requests.
Restates the previous message in straightforward human language. Removes jargon to make the message easier to understand.
Handles legacy references, package upgrades, DataRow mismatches, timeout changes, unsupported target frameworks, and conversion to .runsettings. Repairs lingering migration issues while preserving the existing VSTest or MTP platform.
Adds capabilities from pydantic-ai-harness, including filesystem and shell tools, sub-agents, planning, and context compaction. Code Mode combines multiple tool calls in one sandboxed Python execution and supports running agent-written Python.
Creates a tracked execution plan and fresh worktree branch, implements work phase by phase, and runs configured validation gates. Opens a pull request against the configured base branch, applies pipeline labels, and supports handoff of long plans and later resumption.
Handles package and central management updates, executable test projects, target framework compatibility, async tests, and custom attributes. Preserves the existing runner and addresses extensions and consolidated xUnit packages.
Coordinates eight project-management sub-skills covering sprint analytics, portfolio health, Jira, Confluence, administration, templates, meetings, and team communication. Selects a specialist and returns a digest, or runs a goal-to-close delivery loop using an agent harness and Jira MCP data.
Addresses changed assertion APIs, custom test attributes, cleanup behavior, timeout handling, framework compatibility, and test metadata. Also covers MSTest.Sdk, MTP, and vstest.console discovery changes after upgrading.
Assesses an AI product idea across intended outcomes, hypotheses, risks, and positioning. Supports decisions about whether the solution merits investment or recommendation.
Skills give your agent reusable instructions for a specific job. Pick one, read what it does, and bring it into your workflow.
A name only tells half the story. Search the full description and instructions to find the right fit.
Read the skill, visit its source, and see exactly what you’re adding to your agent.
Copy the install command from a skill page and run it in your project.
npx skillycli add owner/repo --skill name