wanwu

Query Polymarket prediction markets. Check odds, find trending markets, search events, track price movements.

Use this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.

Generate, edit, and read PowerPoint presentations. Create from scratch with PptxGenJS (cover, TOC, content, section divider, summary slides), edit existing PPTX via XML workflows, or extract text with markitdown. Triggers: PPT, PPTX, PowerPoint, presentation, slide, deck, slides.

Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.

Inverse-fold a protein backbone (PDB structure) into amino-acid sequence with ProteinMPNN (Dauparas et al. 2022, github.com/dauparas/ProteinMPNN). Reach for this skill to run sequence design on RFdiffusion backbones, to redesign one chain of a PDB while holding interface residues fixed, or to generate a temperature-swept set of sequences for downstream folding.

Run GPU jobs on the user's own Modal account via host.compute.create('byoc:modal', ...). Covers the create→submit→wait_for_notification flow, the compute_provider kernel for env setup, image/volume resolution, and the two approval cards. Load once you've decided to dispatch to Modal.

Submit→wait_for_notification→harvest workflow for the user's SSH/SLURM hosts. Load once you've decided to dispatch remote.

Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology. Use this skill when: (1) Producing cell embeddings from an AnnData for clustering/integration, (2) Zero-shot or fine-tuned cell-type annotation, (3) Gene-level representation for perturbation/GRN tasks. For probabilistic single-cell models (scVI etc.), use the scvi-tools library.

Probabilistic single-cell RNA-seq with scvi-tools — scVI for a batch-corrected latent space, scANVI for semi-supervised label transfer, and Bayesian differential expression. Reach for this skill to integrate scRNA-seq batches, embed cells for clustering, transfer annotations from a reference onto a query, or score differentially expressed genes per cluster. For spatial deconvolution / mapping use the cell2location, DestVI, or Tangram methods instead.

Claude Science's own session database schema and SDK surface for introspection via host.query(). Load this when you need to query your own conversation history, token usage, cost accounting, execution log, or artifact metadata beyond what host.frames()/host.artifacts() provide — e.g. "how many tokens has this session used", "what was my last tool call", "list every file I've written", "where are messages stored", "what tables can I query", "inspect frames.context_data", or any time you're about to PRAGMA-probe the Claude Science metadata DB to discover its schema.

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.

Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when (1) a command, tool, API, or operation fails; (2) the user corrects you or rejects your work; (3) you realize your knowledge is outdated or incorrect; (4) you discover a better approach; (5) the user explicitly installs or references the skill for the current task.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Security-first skill vetting for AI agents. Use before installing any skill from ClawdHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.

Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like "make me a GIF of X doing Y for Slack."

问数端到端编排（native CLI 版）：从候选 KN 选定知识网络，用 bkn object-type 发现对象类与字段， 由编排层 LLM 生成 SQL，再由 ontology dataview query 执行取数； 最后输出中文结论与口径说明。 当用户需要指标、统计、趋势、SQL 取数或数据查询时使用。 指标管理（Metric）相关的 CRUD / 搜索 / 校验 / 查询数据 / 试运行由 smart-data-analysis 路由到 ontology-core 的 `metric` 命令组， 本 skill 不承担指标定义管理；若用户要管理 KN 级指标定义，应回到 smart-data-analysis 路由到 `ontology metric` 命令组。

数据分析员工（Data Analyst Agent）的唯一总入口：凡与数据资产、取数、指标、表/视图、 治理职责、知识网络、统计或分析相关的问题，必须先经本 skill 做编排与路由，再进入找表或问数等子流程。 负责 kn 分域、上下文注入（accountId / date）、多候选 KN 时的 LLM 决策、 问数分支的 SQL 生成；与 smart-search-tables / smart-ask-data / ontology-core 的交接。 当用户提出任何数据类自然语言任务、或需在多条业务 KN 间切换时使用； 所有 ontology CLI 执行均委托 ontology-core 完成，本 skill 不直接执行 CLI。 指标管理（Metric）相关的 CRUD / 搜索 / 校验 / 查询数据 / 试运行也由本 skill 路由到 ontology-core 的 `metric` 命令组。

智能数据采集技能，用于从图片或文档（PDF、Word、Excel）中提取结构化数据，基于知识网络完成字段映射，生成SQL并写入数据库。当用户提到"数据采集"、"从文档提取数据"、"图片转数据"、"数据导入"、"文档数据入库"、"批量数据提取"或需要从非结构化文件中提取结构化数据并存储时，自动使用此技能。

找表/找数端到端编排：在元数据型知识网络下用 ontology bkn object-type query 检索表/视图实例， 再在职责型知识网络下检索相关部门职责与治理边界，最后汇总为中文结论 （候选表 + 职责要点 + 下一步）。当用户问「表在哪、哪个视图、数据资产归属、谁负责这类数据」时使用。 所有 ontology CLI 执行均委托 ontology-core 完成；本 skill 不直接执行 CLI。