146 boosters for "analysis" — open source, verified from GitHub, ready to install
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.
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"
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.
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.
Analyzes ANY input to find, improve, or create the right skill. Start with least privilege (, , , , ). Only add higher-risk tools when explicitly required:
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.
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
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.
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):"
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".
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).
Use this skill as the end-to-end coordinator for GA4 + GTM tracking delivery. Do not assume the user wants the full workflow. <!-- event-tracking auto-update bootstrap:start -->
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.
This booster enables comprehensive SMILES molecular analysis for cheminformatics tasks, including validation, descriptor computation, and ADMET prediction. It's useful for chemists, drug developers, and AI assistants working with molecular structures.
"name": "x64dbg-skills", "description": "Claude Code skills for x64dbg debugger automation — state snapshots, memory analysis, and more", "name": "dariushoule"
"description": "MalChela malware analysis toolkit — exposes file analysis, string extraction, hash lookup, NSRL queries, and directory scanning to Claude via MCP. Built for DFIR analysts and malware researchers.", "name": "Doug Metz", "email": "dwmetz@gmail.com"
31 specialized agents covering every department from solo founder Day 0 to IPO. 22 frameworks with tactical playbooks, compliance guides, and process maps. Before loading any agent files, consult . It contains:
A development guidelines prompt for Python projects using Windsurf, establishing coding standards for event-driven systems with clear conventions for testing, type hints, and tooling. Useful for teams standardizing their Windsurf workflows and maintaining consistent code practices across event-sourcing architectures.
