Loading...
Loading...
Found 304 Skills
Convert Markdown documents to professionally typeset PDF files with reportlab. Handles CJK/Latin mixed text, fenced code blocks, tables, blockquotes, cover pages, clickable TOC, PDF bookmarks, watermarks, and page numbers. Supports multiple color themes (Warm Academic, Nord, GitHub Light, Solarized, etc.) and is battle-tested for Chinese technical reports. Use this skill whenever the user wants to turn a .md file into a styled PDF, generate a report PDF from markdown, or create a print-ready document from markdown content — especially if CJK characters, code blocks, or tables are involved. Also trigger when the user mentions "markdown to PDF", "md转pdf", "报告生成", or asks for a "typeset" or "professionally formatted" PDF from markdown source.
Summarize Hebrew tech lectures and meetings from transcription files into structured Markdown. Use when the user asks to summarize a transcription, create meeting notes, summarize a lecture/presentation in Hebrew, or mentions סיכום/תמלול/הרצאה.
Explore-lane experimental execution skill for deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with results summarized in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, or implicit experimentation.
This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits. A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of managing token budgets and session longevity.
Build a production-quality CLI tool for any module or application. Auto-detects language, recommends CLI libraries, and follows a 5-step approval-gated workflow: Analyze, Design, Plan, Execute, Summarize. Don't use for building GUI/TUI apps, web APIs, or authoring one-off shell scripts.
Turn vague "what did I do?" into evidence-backed impact statements for performance reviews, self-reviews, promotion packets, and weekly updates. Uniquely mines Copilot CLI session logs to reconstruct forgotten work, plus git commits and GitHub PRs. Enforces a 3-part impact contract (action → result → evidence). Works standalone with zero dependencies. Trigger for: "brag", "log work", "what did I do", "backfill my work history", "performance review", "self-review", "self assessment", "write impact statement", "review prep", "promo packet", "promotion case", "weekly update", "status report", "accomplishments", "what did I ship", "I forgot to log my work", "summarize my work", "track my wins", "what should I highlight", "end of half", "career growth", "work journal", or any request to document, summarize, or organize work accomplishments.
Generate exactly one high-quality Conventional Commit message from the current Git diff. Use when Codex needs to inspect staged changes, summarize the dominant intent, and return only the final commit message with no analysis or extra text.
Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.
Choose the right MoE token dispatcher (`alltoall`, DeepEP, or HybridEP) for the hardware, EP degree, and optimization stage. Summarizes patterns from DSV3, Qwen3, Qwen3-Next, and VLM bring-up work.
Fetch and summarize review comments from the active pull request
Use when writing, rewriting, or reviewing PR/CL descriptions, commit messages, or code-change summaries that explain what changed and why. TRIGGER on "write PR description", "improve this commit message", "summarize this diff", "CL description", "change description", or "make this PR/CL description easier to review". DO NOT TRIGGER for code review, PR splitting, code restructuring, user-facing changelogs, or reviewer replies unless the requested output is a change description.
Use this skill whenever the user asks to read, summarize, review, create, revise, polish, format, preview, or export PowerPoint/PPT/PPTX presentations on Windows, including presentation creation from notes, documents, images, synthesized content, an existing deck, or a template. Use PowerPoint desktop automation through Windows COM for file-producing or editing work, with explicit confirmation before writes. For any new PPT based on documents, PDFs, reports, or synthesized source materials, require a detailed approved Markdown slide plan before producing PPTX unless the user already supplied a sufficient plan. For clear academic paper or literature-presentation tasks, offer optional coordination with nature-paper2ppt.