376 boosters for "sis" — open source, verified from GitHub, ready to install
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.
Frontmatter fields above are primarily for Claude Code / OpenClaw. author: Agents365-ai category: Content Creation
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.
Zettelkasten memory for AI agents. Markdown cards in with . npm package . Distributed as CLI + MCP server + Claude Code plugin + VS Code extension + Pi extension. Build has a known TS error ( optional dep). Ignore it — it compiles fine for distribution. If you add a new MCP tool:
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.
Analyzes ANY input to find, improve, or create the right skill. Start with least privilege (, , , , ). Only add higher-risk tools when explicitly required:
Use this skill as a final completion ritual after a real piece of work is finished. This skill is for the last step of a substantial task, not for ordinary chat. It should feel like a Civilization technology or wonder completion line: brief, ceremonial, and anchored by a real quote with an author an
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.
In Memoria is a persistent intelligence infrastructure MCP server that enables AI agents and developers to build codebase-aware applications with semantic search and pattern learning capabilities. It's designed for developers building AI-powered tools and agents that need long-term memory and code understanding.
Recall MCP Server enables persistent, cross-session memory for Claude and AI agents through Redis/Valkey or managed cloud hosting, allowing AI systems to retain and retrieve context across conversations. Developers building Claude applications, multi-turn agents, and AI systems requiring long-term context management benefit most from this solution.
"name": "uuv-e2e-accessibility-test", "description": "Integrates the Unified UUV Testing Framework into Claude code to provide BDD (Behavior-Driven Development) assistance for your projects based on accessibility datas.", "name": "Louis Fredice NJAKO MOLOM (https://github.com/luifr10) & Stanley SERV
이 항목들은 Anthropic 내부 빌드에서 실행될 때만 포함됩니다. Carefully consider the reversibility and blast radius of actions. Generally you can freely take local, reversible actions like editing files or running tests. But for actions that are hard to reverse, affect shared systems beyond your local environment, or coul
"name": "codeguard-security", "description": "Security code review skill based on Project CodeGuard's comprehensive security rules. Helps AI coding agents write secure code and prevent common vulnerabilities.", "name": "Project CodeGuard",
Matchms is a Python library for mass spectrometry data processing, enabling researchers to load, standardize, and analyze spectral data from multiple formats (mzML, MGF, MSP) with similarity matching for metabolomics and compound identification workflows.
This MCP server bridges AI assistants with Linear project management via GraphQL, enabling Claude and Cursor users to query and manage Linear projects directly through natural language. It's essential for teams using Linear who want AI-assisted workflow automation and issue management.
Cursor rules for refactoring Strava Home Assistant integration to use consolidated sensors per activity type, remove external dependencies, and add device tracking. Useful for Home Assistant developers and Strava integration maintainers.
Run this bash block first, before any analysis: This is the first time clearshot is running — no config exists yet. Before doing any analysis, tell the user to run the onboarding setup. Say something brief like: "clearshot needs a quick first-run setup (two questions, arrow keys + enter):"
Memelord is a persistent memory system for AI coding agents that uses vector search and reinforcement learning to help agents learn from past interactions. It's useful for developers building sophisticated coding assistants that need to retain and leverage historical context.
Analyze, review, and provide recommendations for distributed system designs. Use when: (1) Reviewing existing system architectures for gaps or improvements, (2) Analyzing system designs for scalability, reliability, or performance issues, (3) Providing recommendations on load balancing, caching, databases, sharding, replication, messaging, rate limiting, authentication, resilience, or monitoring, (4) Assessing trade-offs in system design decisions, (5) Creating system design review documents with gaps and recommendations. Triggers: "review my system design", "analyze this architecture", "what are the gaps", "system design recommendations", "scalability review", "reliability analysis".
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
ALWAYS use when: creating/editing marimo notebooks, working with any .py file containing @app.cell decorators, building reactive Python notebooks, doing exploratory data analysis in notebook form, converting Jupyter (.ipynb) to marimo, or when user mentions "marimo", "reactive notebook", or asks for an interactive Python notebook. Covers marimo CLI (edit, run, convert, export), UI components (mo.ui.*), layout functions, SQL integration, caching, state management, and wigglystuff widgets. If a task involves notebooks and Python, invoke this skill first.
Heuristic scoring (no AI key configured).
"name": "prism-mcp-server", "mcpName": "io.github.dcostenco/prism-mcp", "description": "The Mind Palace for AI Agents — persistent memory (SQLite/Supabase), behavioral learning & IDE rules sync, multimodal VLM image captioning, pluggable LLM providers (OpenAI/Anthropic/Gemini/Ollama), OpenTelemetry
Use this skill when the work starts from and the goal is to produce approved . 1. Read the current and page inventory. 2. Group pages by business purpose, not just URL shape.
