📊 Data & AI

Reduces AIGC detection rates and AI-like tone in academic papers when using Claude Code and Codex. Based on detectors such as 知网, 万方, PaperPass, and PaperPure, it applies scanning, deterministic replacement, sentence-level restructuring, and Anti-AI auditing to cut templated AI traces while keeping academic register, facts, data, citations, and terminology consistent. Use when the user asks to lower AI tone, reduce AIGC rates, avoid AI detection, reduce false-positive AI similarity, make writing sound more human, or mentions 'remove AI tone', 'reduce similarity', or 'AIGC check'.

This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.

Delegate coding tasks to the Blackbox AI multi-model CLI.

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

Use this when setting up claude-mem on Cursor: local or remote worker, local host-login observer or remote cmem.ai inference.

Admin / analytics dashboard in a single HTML file. Fixed left sidebar, top bar with user/search, main grid of KPI cards and one or two charts. Use when the brief asks for a "dashboard", "admin", "analytics", or "control panel" screen.

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

A consumer-feeling dating / matchmaking dashboard — left rail navigation, ticker bar of community signals, headline KPIs, a 30-day mutual-matches bar chart, and a match-rate trend block. Editorial typography, restrained accent. Use when the brief asks for a "dating site", "matchmaking", "community dashboard", "social network dashboard", or any consumer product where the data is the story.

Hyperliquid market data, account history, trade review.

This skill should be used when the user asks to "set up claude-mem", "pair claude-mem", "connect cmem", "add my cmem key", "set up cloud sync in Cowork", or provides cmem.ai Connect values (sync token, user id, SyncHub URL) for this plugin. Configures the claude-mem-cowork plugin credentials.

Delegate coding to OpenHands CLI (model-agnostic, LiteLLM).

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

Deep-dive e-commerce review and VOC analysis skill that turns product reviews into actionable insights. Uses 22-dimension AI tagging to extract user personas, pain points, and real needs, then delivers a 15-chapter insight report and themed dashboards while surfacing optimization opportunities from negative reviews and winning traits from positive ones. Supports CSV as the primary input with optional SellerSprite enhancement and Feishu sync; not for keyword research or product-radar tasks.

Systematic framework for evaluating scholarly and research work based on the ScholarEval methodology. This skill should be used when assessing research papers, evaluating literature reviews, scoring research methodologies, analyzing scientific writing quality, or applying structured evaluation criteria to academic work. Provides comprehensive assessment across multiple dimensions including problem formulation, literature review, methodology, data collection, analysis, results interpretation, and scholarly writing quality.

Write scientific manuscripts. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), abstracts, for research papers and journal submissions.

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.

Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

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 don't fit in memory.

A $10K/hr X/Twitter growth mentor built on the methodologies of Nicolas Cole, Dickie Bush, Sahil Bloom, Justin Welsh, Dan Koe, and Alex Hormozi, plus deep analysis of X’s open-source algorithm and AI/tech niche strategies. It distills six core mental models, ten decision heuristics, and a complete ideation-writing-growth playbook, with general methods as the foundation and AI/tech as the specialization. Trigger whenever users mention X/Twitter operations, tweet/thread writing, follower growth, X strategy, or related phrases.

An intelligent YouTube clipping tool. It downloads videos and subtitles and uses AI to generate fine-grained chapters, then automatically edits selected clips, translates subtitles into bilingual Chinese-English, burns them into the video, and generates summary copy.