Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Documents
Comprehensive guide for Google Apps Script development covering all built-in services (SpreadsheetApp, DocumentApp, GmailApp, DriveApp, CalendarApp, FormApp, SlidesApp), triggers, authorisation, error handling, and performance optimisation. Use when automating Google Sheets operations, creating Google Docs, managing Gmail/email, working with Google Drive files, automating Calendar events, implementing triggers (time-based, event-based), building custom functions, creating add-ons, handling OAuth scopes, optimising Apps Script performance, working with UrlFetchApp for API calls, using PropertiesService for persistent storage, or implementing CacheService for temporary data. Covers batch operations, error recovery, and JavaScript ES6+ runtime. Do NOT use for standalone Node.js scripts, Google Cloud Functions, Cloud Run, or any non-Apps-Script JavaScript runtime — those use different APIs and quotas.
Layered technical-writing standard: Diátaxis structure, Google developer style sentences, STE instruction rules, Global English syntax. Use for /technical-writing or when writing or reviewing docs, RFCs, readmes, PR descriptions, or commit messages.
Use when you need to mine a conversation, session transcript, or design discussion for architectural decisions before writing ADRs. Identifies problem-solution pairs, trade-off debates, technology choices, and explicit "[ADR]" tags. Triggers on "what decisions did we make", "extract decisions from this chat", "find the choices in our discussion", or "summarize architectural decisions". Also useful after long planning sessions to capture decisions that were made implicitly. Does NOT write ADR documents — use adr-writing or write-adr for that.
Quick BI-SmartQ skill with multiple data analysis capabilities: 1. **File Q&A**: Upload Excel/CSV files for intelligent analysis via Quick BI API 2. **Dataset Q&A**: Natural language queries on Quick BI platform datasets, with automatic intelligent table selection and matching 3. **Document Parsing**: Parse PDF/Word/Excel/CSV/images, extract text, and support extracting key fields to generate structured Excel 4. **Dashboard Skill Generation**: Auto-convert QuickBI dashboards into data query skills 5. **Data Insight**: Deep data insight analysis on Quick BI datasets 6. **Data Report**: Auto-generate professional data reports based on analysis results Use when users mention data analysis, smart Q&A, querying data, file analysis, document parsing, dashboard skills, data insight, or data reports.
Removes AI writing artifacts from documentation and code. Use when editing LLM-generated prose, reviewing READMEs, polishing docs before publishing, or cleaning up AI-generated code. Use for emdash cleanup, formulaic phrase removal, tone calibration, over-commented code, verbose naming, and AI code smell detection.
Create high-quality README and system documentation for software projects. Use when you need to: (1) Write or update project README documentation, (2) Add system operation principle flowcharts using Mermaid, (3) Write detailed step-by-step usage instructions, (4) Organize and optimize configuration descriptions (environment variables, config files), (5) Make documentation more user-friendly and easier to understand. Applicable to open-source projects, internal tools, API services, and any project requiring improved documentation.
Multi-step Excel data analysis orchestrator. Covers: (1) reading multi-sheet Excel files and counting rows, (2) large-file detection (automatic Parquet optimization at ≥10k rows), (3) data cleaning (missing values, text normalization, invalid characters), (4) conditional filtering and category extraction, (5) cross-sheet statistical aggregation, (6) exporting Excel/CSV with download links. Covers the full flow from data reading to report generation, orchestrating capability sub-skills step by step. **Proactively use this skill in any of the following cases instead of answering with a few lines of pandas**: ① the user uses trigger words: Excel 分析 / 表格分析 / 数据分析 / 数据清洗 / 数据统计 / 数据筛选 / 数据可视化 / 数据导出 / 汇总统计 / 透视表 / 分组统计 / 交叉分析 / 趋势分析 / 对比分析 / 异常值检测 / 去重 / 缺失值处理 / Excel 报告 / 生成报表 / analyze Excel / data analysis / data cleaning / pivot table; ② the user uploads or specifies a .xlsx / .xls / .csv file and asks for analysis, cleaning, statistics, or visualization; ③ the task involves any of multi-sheet reading, conditional filtering, category aggregation, or chart generation; ④ the user asks to export a formatted Excel report or a download link. Not for: pure text processing that does not involve tabular data, image analysis (use sn-da-image-caption), or simple Q&A with a single formula calculation.
Safe workflow for reading, mapping, and writing Google Sheets without shifting columns or overwriting the wrong ranges. Use when the user wants to read, update, or append spreadsheet data, fix sheet structure, or sync data into Google Sheets.
Researches questions across Notion sources and synthesizes cited briefs, comparisons, or reports. Use when answering requires discovering and reconciling workspace information, optionally with external research, and returning or saving the findings.
Read, write, and update Google Sheets data via CLI. Use when the user asks to read spreadsheet data, update cells, append rows, or work with Google Sheets. Triggers on mentions of spreadsheets, sheets, Google Sheets, tabular data in the cloud, or specific sheet names like "Projects" or "Tasks".
Compile a structured literature survey on any AI/ML topic. Agent curates a research bundle (taxonomy + sections + bibliography of real papers) from a public anchor resource, then a chosen LLM generates the survey artifact. Output target is a wiki page (markdown), not a one-off HTML — survey lands in `<wiki>/derived/surveys/<slug>.md` with full bibliography rows in `sources.md`. Provider-agnostic (Anthropic/OpenAI/OpenRouter/Fireworks/custom OpenAI-compat). Use when the user asks for a "survey", "literature review", "lit review", or "deep dive" on a technical topic.
Use this skill any time the user wants to create, draft, or generate a written document or report. This includes: competitive analysis, market research reports, technical design docs, PRDs, project proposals, meeting summaries, white papers, business plans, literature reviews, due diligence reports, industry analysis, executive summaries, SOPs, memos, and any request where the output is a structured document. Also trigger when: user says 写个文档, 做个竞品调研, 写份报告, 产品需求文档, 技术方案, 项目提案, 行业分析, 会议纪要整理成文档. If a document or report needs to be created, use this skill.
Sắp xếp gọn kho tài liệu LOCAL (llmwiki/html/ + wiki/sources/draft/) khi phình to. Ba tầng — PROMOTE bản chất quý lên wiki ADR/concept (travel, sống sót clone), ARCHIVE render ephemeral (cặp proposal đã xong, bản bị thay thế, report cũ → archive/), KEEP canonical — rồi RE-INDEX đầy đủ. Trigger khi "dọn docs", "sắp xếp/merge tài liệu", "tidy docs", "html/draft quá nhiều", "/docs-curate".
Always consult this before writing or outputting anything at the start of any communication with the user. It applies to all situations where text is written or output, such as chat responses, reports, explanations, work updates, and documentation.
Write clear, concise, and accurate technical documentation including API references, user guides, tutorials, changelogs, and architecture docs, tailored to the target audience. Use when the user requests technical writing or provides relevant inputs for this workflow.
Render a self-contained HTML viewer for a pro-workflow wiki. Pages, sources, claims, seed queue, page-link graph and full-text search all in one file. No external dependencies, no JS framework, S3-uploadable. Use when the user wants to browse a wiki visually, share its current state with someone, audit research progress, or hand off a knowledge base. Inspired by Thariq Shihipar's "Unreasonable Effectiveness of HTML" — favors information density and shareability over markdown-only outputs.
Feishu online spreadsheet (Sheets) operations, including creating, reading, writing, appending data, and managing worksheets. Use when the user mentions Feishu Sheets, online spreadsheets, or electronic spreadsheets (not Bitable/multidimensional tables). Supports creating spreadsheets, writing and reading cell values, appending rows, inserting or deleting rows and columns, and managing worksheets.
Convert long-form Markdown (plan, spec, system design, RFC, runbook, postmortem, brainstorm, notes) into a single self-contained HTML page with Mermaid diagrams, step timelines, callouts, sidebar TOC. Claude-orange light+dark theme. Multi-language. Portable across Claude Code / Codex / Antigravity / any AI agent.
AI-powered comprehensive codebase documentation generator. Analyzes project structure, identifies architecture patterns, creates C4 model diagrams, and generates professional technical documentation. Use when users need to document codebases, understand software architecture, create technical specs, or generate developer guides. Supports all programming languages. Alternative to Litho/deepwiki-rs that uses Claude Code subscription without external API costs.
Removes invisible Unicode, zero-width and tag characters, unusual whitespace, and textual AI provenance/watermarks from text the user owns or is authorized to process, and provides a best-effort statistical rewriting workflow. Applies to Traditional Chinese, Simplified Chinese, English, Markdown, HTML, and plain text; use when the user says "去文字浮水印", "去 Claude 浮水印", "清理不可見字元", "清除 AI provenance", or asks to check text markings. Text watermarks are a different problem from file C2PA/EXIF metadata and visible image watermarks; this skill does not handle the latter two directly.
Use when drafting a medical research manuscript or any IMRAD section. Runs an 8-phase pipeline from outline to submission-ready draft for original articles, AI validation studies, case reports, meta-analyses, technical notes and more. Checking a draft is /self-review.
Refine academic writing for computer science research papers targeting top-tier venues (NeurIPS, ICLR, ICML, AAAI, IJCAI, ACL, EMNLP, NAACL, CVPR, WWW, KDD, SIGIR, CIKM, and similar). Use this skill whenever a user asks to improve, polish, refine, edit, or proofread academic or research writing — including paper drafts, abstracts, introductions, related work sections, methodology descriptions, experiment write-ups, or conclusion sections. Also trigger when users paste LaTeX content and ask for writing help, mention "camera-ready", "rebuttal", "paper revision", or reference any academic venue or conference. This skill handles both full paper refinement and section-by-section editing.
Unified entry point for Feishu cloud space, covering Drive enhanced upload/download and asynchronous import/export, basic file/media operations, wiki knowledge base nodes and space management, document comments, collaborators, and public permissions. Must be used when the user mentions Drive files, folders, large-file chunking or resumable transfers, asynchronous tasks, directory mirroring, knowledge base, wiki, assets, comments, sharing permissions, collaborators, public links, share passwords, ownership transfer, or permission requests. Wiki belongs to this Skill only when managing spaces, node structures, or members; it is explicitly forbidden for reading or summarizing Wiki/document bodies, which use feishu-cli-docs. Meeting recordings, Magic Share, and verbatim transcripts use feishu-cli-meetings; Drive-scope search belongs to this Skill; global document/message/app search uses feishu-cli-platform.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.