deslop
Deslop removes unnecessary AI-generated code artifacts from a branch, including redundant comments, defensive checks, boilerplate, type casts, and style inconsistencies introduced by code generation tools. Use this skill specifically when cleaning up AI-written code noise rather than conducting broader code reviews, security audits, or final verification checks.
git clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-Skills /tmp/deslop && cp -r /tmp/deslop/bundled/skills/deslop ~/.claude/skills/deslopSKILL.md
# Remove AI Code Slop Check the diff against main and remove all AI-generated slop introduced in this branch. ## Routing Boundary Use this skill when the user's problem is cleanup of AI-generated code noise. If the user asks for a broad correctness/maintainability review, use `code-reviewer`. If the user asks whether existing review comments should be accepted, use `receiving-code-review`. ## What to Remove - Extra comments that a human wouldn't add or are inconsistent with the rest of the file - Extra defensive checks or try/catch blocks that are abnormal for that area of the codebase (especially if called by trusted/validated codepaths) - Casts to `any` to get around type issues - Inline imports in Python (move to top of file with other imports) - Any other style that is inconsistent with the file ## Process 1. Get the diff against main: `git diff main...HEAD` 2. Review each changed file for slop patterns 3. Remove identified slop while preserving legitimate changes 4. Report a 1-3 sentence summary of what was changed
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash flow), earnings, options data, market news/sentiment, insider transactions, GDP, CPI, treasury yields, gold/silver/oil prices, Bitcoin/crypto prices, forex exchange rates, or calculating technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands). Requires a free API key from alphavantage.co.
Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Full-stack software development agent for design, implementation, testing, and deployment. Use when the user explicitly asks for end-to-end project creation, feature development, bug fixing, or code refactoring.
Unified biological database evidence owner. Use for gene annotation, variant clinical significance, cancer mutation evidence, GWAS trait associations, pathway mapping, target-disease evidence, protein structures, protein interaction networks, reference single-cell census queries, and cross-database biological ID mapping. Do not use for full single-cell analysis, bulk RNA-seq differential expression, BAM/VCF processing, protein embedding models, metabolic flux modeling, genomic interval ML, or flow-cytometry file parsing.