MLOps
Operacions d’aprenentatge automàtic
edge-candidate-agent
tradermonty
Generate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeline Phase I. Use when users ask to turn hypotheses/anomalies into reproducible research tickets, convert validated ideas into strategy.yaml + metadata.json, or preflight-check interface compatibility (edge-finder-candidate/v1) before running pipeline backtests.
transformers
ynulihao
Work with state-of-the-art machine learning models for NLP, computer vision, audio, and multimodal tasks using HuggingFace Transformers. This skill should be used when fine-tuning pre-trained models, performing inference with pipelines, generating text, training sequence models, or working with BERT, GPT, T5, ViT, and other transformer architectures. Covers model loading, tokenization, training with Trainer API, text generation strategies, and task-specific patterns for classification, NER, QA, summarization, translation, and image tasks. (plugin:scientific-packages@claude-scientific-skills)
flow-nexus-neural
ruvnet
Train and deploy neural networks in distributed E2B sandboxes with Flow Nexus
translate
sarvamai
Translate text between English and Indian languages using Sarvam AI's Mayura model. Use when the user needs to translate content, localize applications, or convert text between Hindi, Tamil, Bengali, Telugu, and 7 other Indian languages. Supports bidirectional translation, script control, and code-mixed text.
numerai-research
numerai
"End-to-end Numerai research workflow for trying a new idea: design experiments, implement new model types if needed, run scout→scale experiments, write a full experiment.md report with standard plots, and optionally package/upload a Numerai pickle. Use when a user asks to “try/test a new idea”, “run an experiment”, “sweep configs”, “compare model variants”, or otherwise do new Numerai research."
causal-inference
brycewang-stanford
Production-grade Bayesian causal inference with PyMC, CausalPy, and DoWhy. Enforces DAG-first thinking, mandatory user checkpoints for assumptions, design-specific refutation, and defensible reporting with causal language guardrails. Trigger on: causal inference, causal effect estimation, treatment effects, counterfactuals, difference-in-differences (DiD), synthetic control, regression discontinuity (RDD), interrupted time series (ITS), instrumental variables (IV), propensity scores, DAGs, causal graphs, confounders, backdoor criterion, do-calculus, interventional distributions, pm.do(), pm.observe(), CausalPy, DoWhy, mediation analysis, refutation, sensitivity analysis, parallel trends, placebo tests, or any question of the form "does X cause Y" or "what is the effect of X on Y."
iv-estimation
brycewang-stanford
Econometrics skill for instrumental variables and treatment effect estimation. Activates when the user asks about: "instrumental variables", "IV estimation", "2SLS", "two-stage least squares", "endogeneity", "weak instruments", "first stage", "Sargan test", "overidentification", "propensity score matching", "PSM", "average treatment effect", "ATT", "LATE", "local average treatment effect", "endogenous regressor", "instrument validity", "工具变量", "两阶段最小二乘", "内生性", "弱工具变量", "倾向得分匹配", "平均处理效应", "处理效应", "局部平均处理效应"
ml-causal
brycewang-stanford
Econometrics skill for machine learning methods in causal inference. Activates when the user asks about: "causal forest", "generalized random forest", "GRF", "double machine learning", "DML", "debiased machine learning", "LASSO for variable selection", "post-LASSO", "heterogeneous treatment effects", "CATE", "conditional average treatment effect", "BLP analysis", "CLAN analysis", "causal tree", "honest estimation", "因果森林", "双重机器学习", "异质性处理效应", "条件平均处理效应", "LASSO变量选择", "机器学习因果推断", "去偏机器学习"
arboreto
Microck
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
reading-metaskill
kangarooking
当用户想养成阅读习惯、问「读什么书/怎么读」「如何学习新领域/怎么入门某学科」时调用。 核心理念: 阅读是终极元技能; 读你所爱直到爱上阅读, 没有读完义务; 读原著与经典优先; 以教促学; 每天1-2小时即可进入极少数人行列。 不适用于: 具体某本书的书评、考试备考资料选择。 Triggers: 阅读/读书/怎么学习/入门/原著/书单/reading/how to learn
reading-metaskill
kangarooking
当用户想养成阅读习惯、问「读什么书/怎么读」「如何学习新领域/怎么入门某学科」时调用。 核心理念: 阅读是终极元技能; 读你所爱直到爱上阅读, 没有读完义务; 读原著与经典优先; 以教促学; 每天1-2小时即可进入极少数人行列。 不适用于: 具体某本书的书评、考试备考资料选择。 Triggers: 阅读/读书/怎么学习/入门/原著/书单/reading/how to learn
naval-almanack
kangarooking
当用户询问《纳瓦尔宝典》(The Almanack of Naval Ravikant) 相关内容，或在以下领域寻求建议时调用: 财富构建(怎么赚钱/股权期权/杠杆/副业与职业定位/时薪与外包决策)、判断与决策(重大选择纠结/心智模型/打工vs创业)、 幸福与心智(欲望与焦虑/接受与内耗/冥想/刷手机上瘾/愤怒与期望/攀比妒忌)、人生哲学(生命意义/该不该信玄学/诚实沟通/选朋友圈子/怎么读书学习)。 不适用于: 具体投资标的与选股、临床心理治疗、急性危机(先求助专业人士)。 Triggers: 纳瓦尔/Naval/纳瓦尔宝典/致富/财富/幸福/判断力/冥想/读书/人生意义
decision-heuristics
kangarooking
当用户在重大选择上纠结（换工作/买房/搬城/合伙/结婚）、列了利弊表还是拿不定主意时调用。 核心理念: 无法决定就答否; 三个重大决定(住哪/和谁/做什么)值得花一两年; 两个均等选择选短期更痛苦的路。 不适用于: 日常琐碎选择（晚饭吃什么）、信息查询。 Triggers: 纠结/拿不定主意/要不要/该不该/利弊/decision/hesitate/should I
principal-agent
kangarooking
当用户选择职业/组织/合作方式、困惑「为什么大公司磨洋工/小公司拼命」「该不该自己干」时调用。 核心理念: 委托人(主人)会把事做好, 代理人会为自己利益优化; 收益与创造价值绑得越紧, 越像委托人; 别让媒体洗脑你需要代理人。 不适用于: 具体薪酬谈判数字、组织架构设计细节。 Triggers: 激励/代理/主人/打工 vs 创业/利益绑定/principal/agent/incentive
decision-heuristics
kangarooking
当用户在重大选择上纠结（换工作/买房/搬城/合伙/结婚）、列了利弊表还是拿不定主意时调用。 核心理念: 无法决定就答否; 三个重大决定(住哪/和谁/做什么)值得花一两年; 两个均等选择选短期更痛苦的路。 不适用于: 日常琐碎选择（晚饭吃什么）、信息查询。 Triggers: 纠结/拿不定主意/要不要/该不该/利弊/decision/hesitate/should I
naval-almanack
kangarooking
当用户询问《纳瓦尔宝典》(The Almanack of Naval Ravikant) 相关内容，或在以下领域寻求建议时调用: 财富构建(怎么赚钱/股权期权/杠杆/副业与职业定位/时薪与外包决策)、判断与决策(重大选择纠结/心智模型/打工vs创业)、 幸福与心智(欲望与焦虑/接受与内耗/冥想/刷手机上瘾/愤怒与期望/攀比妒忌)、人生哲学(生命意义/该不该信玄学/诚实沟通/选朋友圈子/怎么读书学习)。 不适用于: 具体投资标的与选股、临床心理治疗、急性危机(先求助专业人士)。 Triggers: 纳瓦尔/Naval/纳瓦尔宝典/致富/财富/幸福/判断力/冥想/读书/人生意义
identity-work
kangarooking
当用户发现自己在为立场辩护、看不清现实、或反复立志却改变不了自己时调用。 核心理念: 身份是认知牢笼也是行为杠杆——放下身份才能看清真相(痛苦=真相时刻), 更新自我形象才能替代自律(对外承诺)。 不适用于: 需要专业心理咨询的深层创伤。 Triggers: 立场/身份/自我/看不清现实/改变不了自己/自律失败/identity/self-image
naval-almanack
kangarooking
当用户询问《纳瓦尔宝典》这本书本身(书名/作者/章节/整书概览/术语)，或涉及本书未独立成 Skill 的主题时调用: 幸福训练/欲望管理、接受与内耗、冥想与心猴、地位游戏与攀比、选朋友与圈子、打工vs创业(委托代理)、 诚实沟通、身份与自我形象、期望与愤怒、生命意义、该不该信玄学(理性验证)、长期复利与声誉、判断力训练。 以下意图优先使用同包独立 Skill: wealth-structure(致富/股权期权)、productize-yourself(职业定位/副业方向)、 decision-heuristics(重大选择纠结)、hourly-rate-time(时薪/外包/时间分配)、reading-metaskill(怎么读书学习)、screen-detox(刷手机上瘾)。 Triggers: 纳瓦尔/Naval/纳瓦尔宝典/幸福/冥想/内耗/攀比/圈子/复利/人生意义
rational-buddhism
kangarooking
当用户问某主张/玄学/灵修该不该信、想建立自己的验证标准、或寻找科学与精神的结合点时调用。 核心理念: 尝试一切, 亲自测试, 保持怀疑, 保留有用的, 舍弃没用的; 不可证伪的主张不进入基本真理; 冥想/接纳等内在技术可保留。 不适用于: 需要信仰安慰的临终/哀伤场景(尊重个体选择)。 Triggers: 该不该信/玄学/灵修/冥想/可证伪/验证/理性/rational buddhism/verify/belief
AgentDB Learning Plugins
ruvnet
"Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience."
cellxgene-census
K-Dense-AI
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
esm
K-Dense-AI
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.
aeon
K-Dense-AI
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.
geniml
K-Dense-AI
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.