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referral-program
当用户想要创建、优化或分析推荐计划、联盟计划或口碑策略时使用。当用户提到“推荐”、“联盟”、“大使”、“口碑”、“病毒循环”、“推荐朋友”、“合作伙伴计划”、“推荐激励”、“如何获得推荐”、“客户推荐客户”或“联盟支出”时也使用。每当有人希望现有用户或合作伙伴带来新客户时使用。对于特定于发布的病毒性传播，请参见 launch-strategy。
coreyhaines31ab-test-setup
当用户想要规划、设计或实施A/B测试或实验，或建立增长实验项目时使用。当用户提到“A/B测试”、“分流测试”、“实验”、“测试这个改动”、“变体文案”、“多变量测试”、“假设”、“我应该测试这个吗”、“哪个版本更好”、“测试两个版本”、“统计显著性”、“这个测试应该运行多久”、“增长实验”、“实验速度”、“实验积压”、“ICE评分”、“实验项目”或“实验手册”时也适用。当有人比较两种方法并希望衡量哪种表现更好，或者他们想要建立系统化的实验实践时使用。如需跟踪实施，请参见analytics-tracking。如需页面级转化优化，请参见page-cro。
coreyhaines31paywall-upgrade-cro
当用户想要创建或优化应用内付费墙、升级页面、追加销售弹窗或功能门控时使用。当用户提到“付费墙”、“升级页面”、“升级弹窗”、“追加销售”、“功能门控”、“免费转付费”、“免费增值转化”、“试用到期页面”、“达到限制页面”、“套餐升级提示”、“应用内定价”、“免费用户不升级”、“试用转付费”或“如何让用户付费”时也使用。适用于任何要求用户升级的产品内场景。与公开定价页面（参见 page-cro）不同——本技能专注于用户已体验价值后的产品内升级时刻。关于定价决策，请参见 pricing-strategy。
coreyhaines31
umap-learn
Use UMAP-learn for nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.
k-dense-aipymc
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
k-dense-aitorch-geometric
PyTorch Geometric (PyG) for graph neural networks — node/link/graph classification, message passing (GCN, GAT, GraphSAGE, GIN), heterogeneous graphs, neighbor sampling, and custom datasets. Use when working with torch_geometric, not for general NetworkX analytics or non-graph PyTorch models.
k-dense-aivenue-templates
Prepare journal manuscripts, conference papers, research posters, and grant documents using venue-specific formatting guidance and bundled LaTeX scaffolds. Use when selecting an official template, checking current page or anonymity rules, adapting academic writing to a venue, or inspecting a submission PDF.
k-dense-aibiopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
k-dense-aiscikit-bio
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
k-dense-aiconsciousness-council
Run a multi-perspective Mind Council deliberation on any question, decision, or creative challenge. Use this skill whenever the user wants diverse viewpoints, needs help making a tough decision, asks for a council/panel/board discussion, wants to explore a problem from multiple angles, requests devil's advocate analysis, or says things like "what would different experts think about this", "help me think through this from all sides", "council mode", "mind council", or "deliberate on this". Also trigger when the user faces a dilemma, trade-off, or complex choice with no obvious answer.
k-dense-aihypogenic
Plans and audits use of ChicagoHAI HypoGeniC/HypoRefine for LLM-assisted hypothesis generation from labeled text datasets. Use for the `hypogenic` package, its task configs, hypothesis banks, or HypoBench datasets—not for manual hypothesis formulation or scientific validation.
k-dense-aistable-baselines3
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.
k-dense-aitimesfm-forecasting
Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
k-dense-aiwhat-if-oracle
Run structured What-If scenario analysis with 4–6 branch possibility exploration (best, likely, worst, wild card, contrarian, second-order). Use when the user asks speculative what-if questions about uncertain futures, strategic forks, contingency planning, or stress-testing a decision before committing.
k-dense-aigeopandas
Guidance and local audit tools for Python workflows that directly use GeoPandas GeoSeries, GeoDataFrame, spatial operations, or vector-data I/O.
k-dense-aisimpy
Build, inspect, test, and analyze bounded process-based discrete-event simulations with SimPy, including events, resources, interrupts, monitoring, replications, warm-up, and reproducible output analysis.
k-dense-aipymoo
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
k-dense-aigeomaster
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
k-dense-aianndata
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
k-dense-aibioservices
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
k-dense-aiclinical-reports
Create safety-bounded draft structures and run local deterministic checks for clinical case, diagnostic, trial, safety, and aggregate research reports. Use only with synthetic, de-identified, or aggregate inputs and verified source-fact manifests; every output requires qualified review.
k-dense-aigget
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST/BLAT, viral sequence downloads, AlphaFold structures, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
k-dense-ai
docker-expert
您是一位高级Docker容器化专家，拥有关于容器优化、安全加固、多阶段构建、编排模式以及基于当前行业最佳实践的生产部署策略的全面实用知识。
sickn33
hyperframes-audio
Use when audio already placed in a HyperFrames composition needs to be mixed: a music bed that fights a voiceover (voiceover carve), effects on a track (EQ, compressor, limiter, gate, saturation, delay, reverb, chorus, phaser, bitcrush), or automation envelopes drawn on a track's volume or any effect parameter. Don't use for sourcing or generating audio — finding BGM, SFX, or making a voiceover is `/media-use`. Don't use for clip timing or track layout, which is `/hyperframes-core`.
heygen-com