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tangledgroup

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• 309 skills
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• 10 hours ago last updated

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   Nodejs 24 14 0 · tangledgroup bundle

   Complete Node.js 24.14 runtime toolkit covering core modules, async programming, HTTP servers, file system operations, streams, cryptography, process management, and ES modules. Use when building server-side JavaScript applications, CLI tools, microservices, or any Node.js project requiring built-in APIs for networking, I/O, encryption, and child processes.
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   Openpyxl 3 1 3 · tangledgroup bundle

   Complete toolkit for openpyxl 3.1.3 providing Excel xlsx/xlsm/xltx/xltm file creation, reading, writing, styling, charts, conditional formatting, data validation, tables, filters, and Pandas integration. Use when building Python programs that need to generate, modify, or read Microsoft Excel spreadsheets programmatically.
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   Pandoc 3 9 0 2 · tangledgroup bundle

   Universal document converter supporting 50+ input formats (Markdown, HTML, LaTeX, docx, EPUB, Org, RST, AsciiDoc) and 60+ output formats including PDF. Provides metadata blocks, citations, math rendering, templates, Lua filters, and custom readers/writers. Use when converting between document formats, generating PDFs from markup, transforming Markdown to HTML/LaTeX/Word, processing bibliographic citations, or building document conversion pipelines.
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   Paramiko 4 0 0 · tangledgroup bundle

   Complete toolkit for Paramiko 4.0.0, a pure-Python SSHv2 protocol implementation providing both client and server functionality. Use when building Python applications that require SSH connections, remote command execution, SFTP file transfers, SSH tunneling/port forwarding, running an in-Python sshd, or programmatic SSH authentication with password, public key, agent, certificate, or GSS-API/Kerberos methods.
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   Peg 2026 05 03 · tangledgroup bundle

   Parsing Expression Grammars (PEGs) — recognition-based formal grammars using ordered choice to eliminate ambiguity, with linear-time packrat parsing. Covers operators, left recursion handling, grammar design, practical implementations (CPython pegen, DuckDB, LPeg), and CFG/regex comparisons. Use when designing language parsers, implementing scannerless parsing, or prototyping new language grammar.
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   Pi Mono 0 66 1 · tangledgroup bundle

   Complete implementation guide for pi-mono monorepo architecture covering provider abstraction, agent runtime, terminal UI rendering, extension system, session management, and tool implementation patterns. Use when building, debugging, or extending the pi coding agent — a minimal terminal-based AI coding harness that runs in interactive, print, JSON, RPC, and SDK embedding modes.
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   Pi Mono 0 71 0 · tangledgroup bundle

   Complete implementation guide for pi-mono monorepo architecture covering provider abstraction, agent runtime, terminal UI rendering, extension system, session management, and tool implementation patterns. Use when building, debugging, or extending the pi coding agent — a minimal terminal-based AI coding harness that runs in interactive, print, JSON, RPC, and SDK embedding modes.
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   Pi Mono 0 74 0 · tangledgroup bundle

   Complete toolkit for the pi coding agent monorepo v0.74.0 covering five packages: pi-coding-agent (interactive CLI), pi-ai (unified LLM API), pi-agent-core (agent runtime), pi-tui (terminal UI framework), and pi-web-ui (web chat components). Provides provider setup, extension authoring, SDK embedding, session management, and customization guidance. Use when building, extending, or integrating with the pi coding agent ecosystem — writing extensions, configuring providers, embedding via SDK, or troubleshooting pi's interactive mode.
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   Pyarrow 24 0 0 · tangledgroup bundle

   Complete toolkit for PyArrow 24.0.0 providing columnar in-memory data structures, vectorized compute functions, Parquet/CSV/ORC/JSON file I/O, Pandas and NumPy zero-copy integration, IPC serialization, tabular datasets, and Arrow Flight RPC. Use when building Python data pipelines, converting between Pandas/NumPy/Arrow formats, reading or writing Parquet files, performing vectorized array computations, serializing data via IPC, or working with partitioned datasets.
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