359 boosters for "sis" — open source, verified from GitHub, ready to install
AGENTS.md provides a Python framework for building AI agents that execute complex, multi-step workflows with consistency using natural language definitions. Developers working with Claude can use this to create reusable agent SOPs that work across multiple platforms and convert to Anthropic Skills format.
You are a trading strategy hypothesis ideation engine. Produce 1-5 falsifiable hypothesis cards from structured evidence.
"name": "iothackbot", "description": "IoT security testing toolkit with skills for firmware analysis, network reconnaissance, UEFI security, and device exploitation", "name": "BrownFineSecurity"
Heuristic scoring (no AI key configured).
Use this agent when documentation in the `architecture/` directory needs to be updated or created for a specific file after implementing a feature, fix, refactor, or behavior change. Launch one instance of this agent per file that needs updating. This agent maintains the *contents* of architecture documentation files — it does not decide which files exist or how the directory is organized.\n\nExamples:\n\n- Example 1:\n Context: A developer just finished implementing OPA policy evaluation in the sandbox system.\n user: "I just finished implementing the OPA engine in crates/openshell-sandbox/src/opa.rs. Update architecture/sandbox.md to reflect the new policy evaluation flow."\n assistant: "I'll launch the arch-doc-writer agent to update the sandbox architecture documentation with the new OPA policy evaluation details."\n <uses Task tool to launch arch-doc-writer with instructions to update architecture/sandbox.md>\n\n- Example 2:\n Context: A refactor changed how the HTTP CONNECT proxy handles allowlists.\n user: "The proxy allowlist logic was refactored. Please update architecture/proxy.md."\n assistant: "Let me use the arch-doc-writer agent to synchronize the proxy documentation with the refactored allowlist logic."\n <uses Task tool to launch arch-doc-writer with instructions to update architecture/proxy.md>\n\n- Example 3:\n Context: After implementing a new CLI command, the assistant proactively updates docs.\n user: "Add a --rego-policy flag to the CLI."\n assistant: "Here is the implementation of the --rego-policy flag."\n <implementation complete>\n assistant: "Now let me launch the arch-doc-writer agent to update the CLI architecture documentation with the new flag."\n <uses Task tool to launch arch-doc-writer with instructions to update architecture/cli.md>\n\n- Example 4:\n Context: A user wants high-level overview documentation for a non-engineering audience.\n user: "Update architecture/overview.md with a non-engineer-friendly explanation of the sandbox system."\n assistant: "I'll launch the arch-doc-writer agent to create an accessible overview of the sandbox system for non-technical readers."\n <uses Task tool to launch arch-doc-writer with audience=non-engineer directive>\n\n- Example 5:\n Context: Multiple files need updating after a large feature lands.\n user: "I just landed the network namespace isolation feature. Update architecture/sandbox.md and architecture/networking.md."\n assistant: "I'll launch two arch-doc-writer agents — one for each file — to update the documentation in parallel."\n <uses Task tool to launch arch-doc-writer for architecture/sandbox.md>\n <uses Task tool to launch arch-doc-writer for architecture/networking.md>
Analyzes ANY input to find, improve, or create the right skill. Start with least privilege (, , , , ). Only add higher-risk tools when explicitly required:
Switch between Docker and Kubernetes backends for Kurtosis. After switching, restart the engine: When using Kubernetes:
This skill provides a philosophical framework and analytical methods for evaluating whether end users can "know" what value they can achieve through a product. It guides analysis from a value discovery perspective, rather than providing checklists. End users adopt products when they know what value
An MCP server that enables AI assistants to programmatically control and interact with Unreal Engine 5, allowing developers to automate game development tasks and integrate AI-powered workflows into their UE5 projects.
Matchms enables mass spectrometry data processing and spectral analysis for metabolomics research, supporting multiple file formats and similarity calculations. Researchers and bioinformaticians working with MS data benefit from standardized workflows and compound identification.
"name": "ensue-memory", "description": "Persistent memory layer for AI agents via Ensue Memory Network", "email": "founders@ensue.dev"
An X-ray Diffraction analysis skill that enables automated crystal structure identification, phase analysis, and crystallite size determination for nanomaterials research. Ideal for materials scientists, chemists, and nanotechnology researchers working with XRD data.
"name": "claude-equity-research-marketplace", "name": "quant-sentiment-ai", "email": "quant.sentiment.ai@gmail.com"
An MCP server that integrates Xcode with AI assistants, allowing Claude to interact directly with iOS/macOS projects and understand their structure. Ideal for iOS developers seeking AI-powered code analysis, refactoring, and project navigation.
LLM-first SEO analysis skill with 16 specialized sub-skills, 10 specialist agents, and 33 scripts for website, blog, and GitHub repository optimization. For prompt reliability in Codex/agent IDEs, map common user wording to a fixed workflow: When the user requests SEO analysis, follow this routing:
"name": "finlab-plugin", "description": "FinLab quantitative trading skills for Taiwan stock market (台股) - includes strategy development, backtesting, data analysis, and factor research", "name": "FinLab Community"
A specialized diagnostic tool for data engineers to systematically investigate Airflow DAG failures, identify root causes, and implement prevention strategies. Ideal for complex pipeline debugging scenarios requiring deep analysis beyond basic log inspection.
pycse is a Python library that assists with scientific computing tasks including nonlinear regression, uncertainty quantification, design of experiments, and neural network-based modeling. It's useful for researchers, engineers, and data scientists working on numerical optimization, experimental design, and uncertainty analysis.
XerahS is a cross platform reimagining of the ShareX user interface built with Avalonia. It targets modern UX modular architecture and long term maintainability while preserving core workflows speed and reliability. It provides a foundation for future desktop experiences on Windows, macOS, and Linux
Quick Codebase Analysis is a fast alias for Gemini codebase analysis that lets developers analyze directories with optional scope filters (e.g., `/c ./src security`). It's useful for developers who need rapid codebase insights without full context overhead.
Matchms is a Python library for mass spectrometry data processing, enabling researchers to import spectra from multiple formats, standardize metadata, calculate spectral similarities, and perform metabolomics analysis within Claude Code environments.
A Windsurf-specific rule set that enforces Haiven team's backend (Python) and frontend (React/Next.js) development standards, including architecture patterns, testing practices, and code style conventions. Developers working on this codebase benefit from clear, actionable guidelines integrated directly into their IDE.
A fully autonomous AI research agent that ingests sources into Google NotebookLM, runs deep web research, synthesizes knowledge through cited Q&A and 9 downloadable artifact types, creates polished content drafts, and optionally publishes to social platforms.
