Total 50,320 skills, Document Processing has 738 skills
Showing 12 of 738 skills
Ingest Pi coding agent session history into the Obsidian wiki. Use this skill when the user wants to mine their past Pi sessions for knowledge, import their ~/.pi/agent/sessions folder, extract insights from previous coding sessions, or says things like "process my Pi history", "add my Pi sessions to the wiki", "ingest ~/.pi", or "what have I worked on in Pi". Also triggers when the user mentions Pi sessions, Pi agent history, ~/.pi/agent/sessions, or Pi conversation logs.
Use when verifying citations, bibliography, manuscript claims, source support, factual accuracy, numerical results, citation drift, or evidence provenance in academic work.
Trace how a contract has changed across its base agreement and all amendments — either a summary of all changes over time, or a provision trace for a specific clause. Use when the user says "what changed in this contract over time", "show me the amendment history", "where's the latest [clause]", "how has [provision] evolved", or uploads multiple versions of an agreement.
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
Generate comprehensive OpenSpec specifications directly from the current project state. Use when the user wants to create or populate main specs by analyzing existing code, documentation, AGENTS.md, GitHub issues, and pull requests — without going through the change/proposal workflow. Ideal for bootstrapping specs on a project that already has working code but no specs yet, or for refreshing specs to match the current implementation.
Create, read, edit .pptx decks, slides, notes, templates.
11 latex skills. Trigger: LaTeX typesetting, formatting papers, mathematical notation, Beamer. Design: template-based guides with package recommendations and compilation tips.
Rewrite English content into Simplified Chinese. It is used for rewriting English articles, documents, and blogs into Chinese. Adopt the deverbalization technique: understand the original meaning, break away from the English structure, and express naturally in Chinese instead of word-for-word translation. Preserve Markdown formatting and AI-specific terminology.
Parse PDFs locally (CPU) into Markdown/JSON using MinerU. Assumes MinerU creates per‑doc output folders; supports table/image extraction.
Download PDFs (when available) and extract plain text to support full-text evidence, writing `papers/fulltext_index.jsonl` and `papers/fulltext/*.txt`. **Trigger**: PDF download, fulltext, extract text, papers/pdfs, 全文抽取, 下载PDF. **Use when**: `queries.md` 设置 `evidence_mode: fulltext`（或你明确需要全文证据）并希望为 paper notes/claims 提供更强 evidence。 **Skip if**: `evidence_mode: abstract`（默认）；或你不希望进行下载/抽取（成本/权限/时间）。 **Network**: fulltext 下载通常需要网络（除非你手工提供 PDF 缓存在 `papers/pdfs/`）。 **Guardrail**: 缓存下载到 `papers/pdfs/`；默认不覆盖已有抽取文本（除非显式要求重抽）。
Clean and reconstruct raw auto-generated captions (Zoom, YouTube, Teams, Google Meet, Otter.ai, etc.) into readable, coherent transcripts. Use when the user provides raw caption files (.txt, .vtt, .srt), meeting transcripts with timestamps and speaker tags, or asks to clean up/refine a transcript. Handles: timestamp removal, speaker tag normalization, filler word removal, broken sentence reconstruction, transcription error correction, paragraph formation. Preserves every piece of substantive content while removing noise. Trigger phrases: 'clean this transcript', 'refine captions', 'fix this transcript', 'process Zoom captions', 'clean up meeting notes'.
Comprehensive patterns and techniques for removing AI-generated verbosity and slop