TablaCognita
v0.2.0
io.github.PStryder/tablacognita
MCP-native collaborative markdown editor with real-time AI document editing
“Document” 共 460 个结果
v0.2.0
io.github.PStryder/tablacognita
MCP-native collaborative markdown editor with real-time AI document editing
v1.0.3
io.github.UnMarkdown/mcp-server
The document publishing layer for AI tools. Convert markdown to 6 destinations, 62 templates.
v0.3.2
io.github.ai-aviate/better-notion
Operate Notion with a single Markdown document — read, create, and update pages in one call.
v0.1.1
io.github.yotsuda/markdown-pointer
Markdown viewer for AI-assisted document review. Click any element to copy file path + line number.
v1.2.1
io.github.RECERQA/rq-scan
MCP Server for RQ-SCAN - AI-powered document OCR and data extraction platform
v0.1.0
io.github.m2ai-mcp-servers/mcp-ratchet-clinical-charting
MCP server for clinical charting with Claude - document patient visits to EMR
v2.12.1
io.github.MUSE-CODE-SPACE/vibe-coding
Auto-document vibe coding sessions - collect, summarize, and publish
v0.2.0-rc.3
io.github.nonatofabio/local-faiss-mcp
Local FAISS vector database for RAG with document ingestion, semantic search, and MCP prompts.
v3.0.0
io.github.MusaddiqueHussainLabs/mhlabs_mcp_tools
An MCP server that provides text preprocessing, NLP components, and document analysis
v0.4.4
io.github.NeerajG03/vector-memory
Semantic document memory using Redis vector store. Save and recall files with natural language.
v1.0.0
io.github.ptyagiegnyte/egnyte-mcp-server
Official Egnyte MCP Server for AI integration with document search, analysis, and collaboration.
v1.0.0
io.github.huoshuiai42/huoshui-file-converter
An MCP server that provides document format conversion
vmain
io.github.anthropics/skills/xlsx
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
vmain
io.github.anthropics/skills/docx
Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
vmain
io.github.microsoft/typescript/security-report-check
Are you doing security research on this repo? This document covers what guarantees and non-guarantees are provided. Consult this document before reporting a security issue or conducting security research.
vmain
skillsmp.nidhinjs-prompt-master-skill-md
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other non-prompt-engineering work.
vmain
io.github.nexu-io/open-design/orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and renders the digest as a native Notion page (callout / toggle / database table primitives). This skill should not be triggered manually — it is invoked by Orbit's daily-digest scheduler against live Notion data.
vmain
io.github.nexu-io/open-design/orbit-notion
Open Orbit briefing skill — selected by the Orbit pipeline when Notion is the user's only connected connector, or when the user explicitly scopes their daily digest to Notion. Pulls the past 24 hours of document edits, comments, mentions, and database row changes from the user's authenticated Notion connection and renders the digest as a native Notion page (callout / toggle / database table primitives). This skill should not be triggered manually — it is invoked by Orbit's daily-digest scheduler against live Notion data.
vmain
io.github.mattpocock/skills/handoff
Compact the current conversation into a handoff document for another agent to pick up.
vmain
io.github.affaan-m/ECC/orch-build-mvp
Orchestrate bootstrapping a working MVP from a design or spec document — ingest the doc, plan thin vertical slices, scaffold the first end-to-end slice, then TDD-implement, review, and gated commit. Use to turn an SDD/PRD into a running starting point. Use when a design or spec document must become a running MVP through planned vertical slices.
vcanary
io.github.vercel/next.js/sandbox-bench
Benchmark React or Next.js changes on Vercel Sandbox VMs with paired A/B statistics: react PR/commit vs base, or Next.js PR/commit vs base, measured end-to-end through the bench/render-pipeline app (rps, latency, p95; TTFB, RSS and document/Flight bytes when the Next side captures them) and, for React changes, through the react repo's flight-ssr-bench fixture (Node AND Edge web-streams paths, Fizz and Flight+Fizz). Use whenever the user asks to bench, perf test, or A/B a React PR, a react-server-dom / Flight / vendored React change, or a Next.js PR ("is this PR faster", "does this regress RSC?", "measure the perf impact of <commit>"), even if they don't say "benchmark" — any request to quantify a server-side performance difference between two revisions belongs here. Runs remotely (laptop-free), applies correctness gates before measuring, and reports boot-level confidence intervals.
vmain
io.github.anthropics/skills/xlsx
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
vmain
io.github.EveryInc/compound-engineering-plugin/ce-doc-review
Review requirements, plans, or specs with role-specific lenses. Use when the user wants to improve an existing planning document.
vdevelop
io.github.HoangNguyen0403/agent-skills-standard/database-schema-design
Design relational or document schemas from access patterns, cardinality, and lifecycle. Use when modeling entities, choosing embed vs normalize, or shaping schema boundaries before implementation.