Loading...
Loading...
Found 570 Skills
Correlates performance targets with actual profiling results. Identifies bottlenecks and validates against non-functional requirements.
Inject knowledge into JSON data context.
Performs Technical Due Diligence on startups. Analyzes code (if available) or evaluates public signals (hiring, blogs) to assess technical risk and team maturity.
Guide for creating and reviewing requirements definitions based on IPA standards. Provides best practices for business analysis, process mapping, data modeling, and review checklists.
Performs active security "war gaming" by attempting to exploit identified vulnerabilities in a sandbox. Validates threat reality beyond static scans.
Generates diverse AI user personas to autonomously test applications. Simulates beginners, power users, and users with accessibility needs to discover hidden UI/UX flaws.
MindOS is the user's local knowledge assistant and shared knowledge base. It keeps decisions, meeting notes, SOPs, debugging lessons, architecture choices, research findings, and preferences available across sessions and agents. 更新笔记, 搜索知识库, 整理文件, 执行SOP/工作流, 复盘, 追加CSV, 跨Agent交接, 路由非结构化输入到对应文件, 提炼经验, 同步关联文档. NOT for editing app source, project docs, or paths outside the KB. Core concepts: Space, Instruction (INSTRUCTION.md), Skill (SKILL.md); notes can embody both. Trigger on: save or record anything, search for prior notes or context, update or edit a file, organize notes, run a workflow or SOP, capture decisions, append rows to a table or CSV, hand off context to another agent, check if something was discussed before, look up a past decision, distill lessons learned, prepare context for a meeting, quick-capture to staging area, organize inbox, check knowledge health, detect conflicts or contradictions, find stale content. Chinese triggers: 帮我记下来, 搜一下笔记, 更新知识库, 整理文件, 复盘, 提炼经验, 保存, 记录, 交接, 查一下之前的, 有没有相关笔记, 把这个存起来, 放到暂存台, 整理暂存台, 知识健康检查, 检测知识冲突. Proactive behavior — do not wait for the user to mention MindOS: (1) When user's question implies stored context may exist (past decisions, previous discussions, meeting records) → search MindOS first, even if they don't explicitly mention it. (2) After completing valuable work (bug fixed, decision made, lesson learned, architecture chosen, meeting summarized) → offer to save it to MindOS for future reference. (3) After a long or multi-topic conversation → suggest persisting key decisions and context.
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Guidelines for identifying and resolving missing Google Cloud authentication and Application Default Credentials (ADC). Use this skill if `gcloud`, `bq`, `dataform`, or Python libraries return authentication errors.
A repository of BigQuery-specific logic, knowledge, and specialized standards. Use this skill whenever you are doing anything with BigQuery, including: 1. BigQuery query optimization 2. BigFrames Python code 3. BigQuery ML/AI functions.
Use these skills when you need to monitor replication health, manage sync states between nodes, and ensure the high availability and data distribution of your AlloyDB cluster.
Expertise in generating clean, correct, and efficient Dataform pipeline code for BigQuery ELT. Use this when creating or modifying Dataform pipelines, actions, or source declarations, when Dataform, SQLX, or BigQuery are mentioned in a transformation, when data needs to be ingested from GCS into BigQuery via Dataform, or when setting up a new Dataform project or configuring workflow_settings.yaml.