Agent Skills

Define and implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targets, implementing SRE practices, or measuring service performance.

Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.

This repository is no longer maintained. The core functionality has been integrated into all IntelliJ-based IDEs since version 2025.2. The built-in functionality works with SSE and JVM-based proxy (for STDIO) so this NPM package is no longer required. Migration: Please refer to the official documentation for details on using the built-in functionality. Issues & Support: For bugs or feature request

The semantic engine for MCP clients. Define metrics once, query from anywhere. Docs · Getting Started · Changelog · Discord · Bonnard is an agent-native semantic layer CLI. Deploy an MCP server and governed analytics API in minutes -for AI agents, BI tools, and data teams. Define metrics and dimensions in YAML, validate locally, and ship to production. Works with Snowfl

Implement the minimal code needed to make failing tests pass in the TDD green phase.

Plan a user interview topic in PostHog — pick who to target (cohort, emails, or PostHog distinct IDs), draft what to ask about, and prepare the voice-agent context plus a question list. Use when the user asks to "talk to users", "check how users feel about X", "interview some customers", "set up a user interview", "run a user-research call", "find users to ask about Y", or otherwise wants qualitative feedback through a conversation.

This project provides a set of tools for retrieving information about SonarQube projects using the FastMCP (Fast Model Context Protocol) framework. It serves as an interface to SonarQube, allowing users to programmatically access metrics, historical data, and component tree metrics for specified projects. This automated access enables reporting, analysis, and integration of SonarQube data with oth

Deterministically resolve which Codex and Claude Code instruction sources apply to a repository path, including scope, shadowing, exclusions, and diagnostics.

Azure Identity Java SDK for authentication with Azure services. Use when implementing DefaultAzureCredential, managed identity, service principal, or any Azure authentication pattern in Java applications.

An MCP server implementation that provides job search functionality. Get available jobs with filtering options sinceWhen (string): Since when to get available jobs. e.g., '1d' or '1w' (only days and weeks are supported) keywords (string[], optional): Keywords to filter jobs by excludeKeywords (string[], optional): Keywords to exclude from the jobs isRemote (boolean, optional): Whether to filter jo

Audit PostHog experiments and feature flags for configuration issues, staleness, and best-practice violations. Read when the user asks to audit, health-check, or review experiments or feature flags, check flag hygiene, or verify experiment setup.

gomcp is an MCP server written in It exposes tools to interact with Lightpanda Browser via CDP protocol. gomcp requires you to have already installed Lightpanda You need to install Go to build from source. Once you have cloned the repository, build the binary with go build. By default, gocmp starts a local instance of Lightpanda browser. On the first run, you need to download the binary with the c

Decompose any text into classified semantic units — authority, risk, attention, entities. No LLM. Deterministic.

Analyze git repositories to build a security ownership topology (people-to-file), compute bus factor and sensitive-code ownership, and export CSV/JSON for graph databases and visualization. Trigger only when the user explicitly wants a security-oriented ownership or bus-factor analysis grounded in git history (for example: orphaned sensitive code, security maintainers, CODEOWNERS reality checks for risk, sensitive hotspots, or ownership clusters).

An AI coding assistant built in Rust that provides both command-line and graphical interfaces for autonomous code analysis and modification. Multi-Modal Tool Execution: Adapts to different LLM capabilities with pluggable tool invocation modes - native function calling, XML-style tags, and triple-caret blocks - ensuring compatibility across various AI providers. Real-Time Streaming Interface: Advan

Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.

Persistent memory and knowledge management. Use at session start to load context, when the user asks about past work or stored knowledge, and when saving important information for future sessions.

This skill should be used when the user asks to "perform cloud penetration testing", "assess Azure or AWS or GCP security", "enumerate cloud resources", "exploit cloud misconfigurations", "test O365 security", "extract secrets from cloud environments", or "audit cloud infrastructure". It provides comprehensive techniques for security assessment across major cloud platforms.

LLMs rely on outdated or generic information about the libraries you use. You get: - ❌ Code examples are outdated and based on year-old training data - ❌ Hallucinated APIs that don't even exist - ❌ Generic answers for old package versions Context7 MCP pulls up-to-date, version-specific documentation and code examples straight from the source — and places them directly into your prompt. Add use con

A Model Context Protocol (MCP) server for comprehensive OPNsense firewall management. This server enables AI assistants like Claude to directly manage firewall configurations, diagnose network issues, and automate complex networking tasks. - Complete CRUD operations for firewall rules - Proper handling of API-created "automation rules" - Inter-VLAN routing configuration - Batch rule creation and m

English | 中文 Zero-dependency MCP memory server for AI agents — persistent, searchable, local-first, single binary. - No infra to babysit. Single Go binary. No Docker, no Node.js runtime, no cloud account, no API keys. brew install in 30 seconds. - Memory stays with the project. Stored in .aimemo/ next to your code — commit it to git or add it to .gitignore. Switch branches; memory follows the dire