recon-methodology
Bug bounty and pentest reconnaissance methodology
Bug bounty and pentest reconnaissance methodology
Set up and run an autonomous experiment loop for any optimization target. Use when asked to start autoresearch or run experiments.
Query the Sequence Read Archive (SRA), retrieve scientific publications, and analyze genomics metadata using the SRAgent toolkit. Supports accession conversion (GSE→SRX→SRR), BigQuery metadata queries, manuscript downloads from multiple sources, and scRNA-seq technology identification. Use when working with SRA/GEO datasets, finding publications, or analyzing single-cell sequencing experiments.
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Draft a long-form book review from your Reader highlights — synthesizing the book with your broader reading history to generate original arguments
Statistical analysis, outlier detection, and data transformation utilities.
Research subreddits and create posts — find the right subreddit, analyze trends, check rules and flairs, craft content, draft for review
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
Comprehensive collection of 128+ ready-to-use scientific skills for Claude enabling research across biology, chemistry, medicine, genomics, and advanced analysis domains.
Real-time web research/search with sources (outputs JSON).
Using Density-Fit Correlations in Coot
RDKit molecular manipulation and visualization within Coot's Python environment. Use when working with Coot and need to (1) Create RDKit molecules from Coot monomers, (2) Modify molecular structures (e.g., atom substitution), (3) Generate 2D chemical structure diagrams, (4) Perform cheminformatics operations on ligands or small molecules loaded in Coot.
Comprehensive structure validation combining model-to-map analysis and unmodeled density detection
Best practices for protein structure refinement and validation in Coot. Use when performing (1) Residue refinement operations, (2) Model building and fitting, (3) Rotamer fixing, (4) Scripted/automated refinement workflows, (5) Validation and correlation checking.
How to handle Unmodelled Density Blobs
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+ scientific tools including machine learning models, datasets, APIs, and analysis packages. Use when searching for scientific tools, executing computational biology workflows, composing multi-step research pipelines, accessing databases like OpenTargets/PubChem/UniProt/PDB/ChEMBL, performing tool discovery for research tasks, or integrating scientific computational resources into LLM workflows.
Use this skill for requests related to web research; it provides a structured approach to conducting comprehensive web research