Content & Media

Monorepo script commands and conventions for this codebase. Use when running builds, tests, formatting, linting, or type checking.

Social media post guidelines for LinkedIn, Reddit, and Twitter/X. Use when drafting posts, announcements, or sharing technical content on social platforms.

Behavioral guideline for providing brutally honest feedback. Use always - this skill defines core interaction expectations for code review and technical discussions.

Technical writing, README guidelines, and punctuation rules. Use when writing documentation, READMEs, technical articles, or any prose that should avoid AI-generated feel.

GitHub issue comment guidelines for community interaction. Use when responding to GitHub issues, bug reports, feature requests, or any GitHub discussion.

CSS and Tailwind styling guidelines. Use when writing styles, creating UI components, reviewing CSS/Tailwind code, or deciding on wrapper element structure.

This skill should be used when handling issue intake rotation duties for the Positron repository. It provides workflows for reviewing and organizing new issues, responding to discussions, handling support tickets, and searching for related content. Use this skill when on intake rotation duty, when helping someone with intake tasks, or when learning the intake rotation process.

This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions

Build and run LLM-powered data processing pipelines with DocETL. Use when users say "docetl", want to analyze unstructured data, process documents, extract information, or run ETL tasks on text. Helps with data collection, pipeline creation, execution, and optimization.

Run unit tests that require the Spanner emulator. Use this skill when the user wants to run tests in packages like satellite/metabase, satellite/metainfo, or any other tests that interact with Spanner. Automatically handles checking for and configuring the Spanner emulator environment.

Applies Notion Avatar's hand-drawn minimalist style to any artifact that may benefit from having the project's look-and-feel. Use it when brand colors, style guidelines, visual formatting, or design standards apply.

Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.

CLI/Python toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl/UniProt), AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, genome downloads. For advanced BLAST/batch processing, use biopython. For multi-database integration, use bioservices.

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process: (1) create section outlines with key points using research-lookup, (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

Generate or edit images using AI models (FLUX, Gemini). Use for general-purpose image generation including photos, illustrations, artwork, visual assets, concept art, and any image that is not a technical diagram or schematic. For flowcharts, circuits, pathways, and technical diagrams, use the scientific-schematics skill instead.

Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genotype-guided dosing decisions.

Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.