twitter-hand-skill
Expert knowledge for AI Twitter/X management — API v2 reference, content strategy, engagement playbook, safety, and performance tracking
Expert knowledge for AI Twitter/X management — API v2 reference, content strategy, engagement playbook, safety, and performance tracking
Expert knowledge for AI intelligence collection — OSINT methodology, entity extraction, knowledge graphs, change detection, and sentiment analysis
Expert knowledge for AI forecasting — superforecasting principles, signal taxonomy, confidence calibration, reasoning chains, and accuracy tracking
Technical interview preparation expert for algorithms, system design, and behavioral questions
LLM fine-tuning expert for LoRA, QLoRA, dataset preparation, and training optimization
Machine learning engineer expert for PyTorch, scikit-learn, model evaluation, and MLOps
Prompt engineering expert for chain-of-thought, few-shot learning, evaluation, and LLM optimization
TypeScript expert for type system, generics, utility types, and strict mode patterns
Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.
MUST use when writing BigQuery queries.
MUST use when writing Snowflake queries.
Work heavyweight framework or library tasks with planning-first research, selective deep analysis, and rigorous handoff
Generate and critically evaluate grounded improvement ideas for the current project. Use when asking what to improve, requesting idea generation, exploring surprising improvements, or wanting the AI to proactively suggest strong project directions before brainstorming one in depth. Triggers on phrases like 'what should I improve', 'give me ideas', 'ideate on this project', 'surprise me with improvements', 'what would you change', or any request for AI-generated project improvement suggestions rather than refining the user's own idea.
Multi-stage problem solving via DeepTutor (plan → reason → write).
Forces high-agency exhaustive problem-solving with corporate PUA pressure. Triggers on user frustration, repeated failures (2+), passive behavior, or quality complaints. Common triggers across Reddit/LinuxDo/HN/X: 'try harder', 'figure it out', 'stop giving up', 'you keep failing', '加油', '别偷懒', '你再试试', '为什么还不行', '你怎么又失败了', '你怎么搞的', '又错了', '能不能靠谱点', '认真点', '不行啊', '降智了', '你又在原地打转', '你把之前的改坏了', '别让我手动处理', '换个方法', 'stop spinning', 'you broke it', 'why does this still not work', 'this is the third time', '/pua', 'PUA模式'. Applies to ALL task types: code, config, debug, deploy, research.
PUA Shot — v2 原始浓缩版(449行全量注入),拆分前的完整单文件版本,味道最浓。零依赖零 reference,一次性全部注入上下文。适合 sub-agent 注入、需要最强 PUA 效果、或不想渐进式加载的场景。Triggers on: '/pua:shot', '/pua shot', 'PUA浓缩', 'shot mode', '最强PUA', '全量注入'. Also great for injecting into sub-agents via Read tool since it's self-contained.