Total 53,748 skills, Document Processing has 770 skills
Showing 12 of 770 skills
Use when a user asks to "proofread", "review and correct", "fix grammar", "improve readability while keeping my voice", and to proofread a document file and save an updated version.
Convert legal texts (legal provisions or legal cases) into standardized Markdown format and remove promotional redundant information. This skill shall be used when users need to process legal provisions (such as the Civil Code, Criminal Law, etc.), organize legal cases (such as typical cases of the Supreme People's Court, judgment documents, etc.), or format legal documents from pasted text. Note: This skill is only responsible for formatting and content cleaning, and does not have content crawling capability. Content acquisition shall be completed by other skills (such as wechat-article-fetch), and AI will automatically determine the skill collaboration sequence.
Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.
Scan and normalize existing intent/planning files to IDD standard. Auto-fixes mechanical issues (frontmatter, directory structure), tags content issues for pickup. Use /intent-normalize to scan current project, or /intent-normalize <path> for specific directory.
Extracts key specifications from component datasheet PDFs for maker projects. Use when user shares a datasheet PDF URL, asks about component specs, needs pin assignments, I2C addresses, timing requirements, or register maps. Downloads and parses PDF to extract essentials. Complements datasheet-parser for quick lookups.
Rewrite `outline/claim_evidence_matrix.md` as a projection/index of evidence packs (NO PROSE), so claims/axes are driven by `outline/evidence_drafts.jsonl` rather than outline placeholders. **Trigger**: claim matrix rewriter, rewrite claim-evidence matrix, evidence-first claim matrix, matrix index, 证据矩阵重写, 从证据包生成矩阵. **Use when**: `outline/subsection_briefs.jsonl` + `outline/evidence_drafts.jsonl` are ready and you want a clean claim→evidence index for QA/writing. **Skip if**: `outline/claim_evidence_matrix.md` is already refined and consistent with evidence packs. **Network**: none. **Guardrail**: NO PROSE; do not invent facts; only cite keys present in `citations/ref.bib`; if evidence is abstract/title-only, claims must be provisional.
Expert in creating, editing, and automating Word documents (.docx) using python-docx and docx.js. Use when generating Word documents, modifying existing docx files, or automating document workflows.
Straightforward text extraction from document files (text-based PDF only for now, no OCR or docx). Use when you just need to read/extract text from binary documents.
NSFC Grant Citation and Bib Management: Add/verify paper information (title/author/year/journal/DOI) and write to `references/ccs.bib` or `references/mypaper.bib`, ensuring no hallucinated citations; applicable when users request "add citations/supplement references/verify paper information/write bibtex/update .bib".
This skill should be used when the user asks to "what type of resume is this", "identify my resume format", "is this a federal resume", "what kind of CV is this", or when the user provides a resume and the type is unclear. Also called automatically by the review and rewrite skills as their first step. Works for all resume and CV types: standard US, federal, academic, legal, medical, consulting, tech, executive, military transition, education, nonprofit, trades, creative, investment banking, and EU/Europass formats.
Automatically add [[wikilinks]] to all mentions of existing entities within a file or entire world. Scans for entity names, aliases, and partial matches, then wraps them in wikilink syntax. Use when user wants to "linkify", "auto-link", "add links to existing entities", or "wikilink this file".
Summarize documents, extract key points, and generate structured outlines