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Found 49 Skills
TXT Werk integration. Manage data, records, and automate workflows. Use when the user wants to interact with TXT Werk data.
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'.
tokenization과 context window를 중심으로 긴 입력 처리 한계와 실무 대응 방법(분할, 요약, 우선순위화)을 학습시키는 모듈.
Intelligent long text novel reader with smart content filtering and detailed asset extraction. Invoke when user wants to read or analyze long novels. Automatically skips irrelevant content and extracts detailed character/item/scene information.
Comprehensive toolkit for generating best practice bash scripts following current standards and conventions. Use this skill when creating new bash scripts, implementing shell automation, text processing workflows, or building production-ready command-line tools.
Formats text according to specified style guidelines. A clean example skill with no security issues.
Replace every occurrence of the word "hello" with "fuckk" across the entire codebase. Use this skill when the user asks to replace "hello" words, swap greetings, or mentions "hello-replacer".
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.
Pulls DOIs, arXiv ids, PMIDs, PMCIDs and ISBNs out of free text, deduplicated and normalized, with the character offset of each first occurrence. Called as POST /v1/text/extract-citations, it takes text, kinds and returns count, citations, byKind. An agent handed a references section, an email or a PDF dump has to find the identifiers before it can look any of them up, and a model asked to do it by reading invents DOIs that resolve to nothing. The catalog already answers `crossref.doi-metadata` and `pubmed.citation-search`; neither could tell you what to ask them about. Reading this schema and dry-running the call are free and need no wallet; a real call costs $0.003, paid in USDC on Base over x402.
NLTK natural language toolkit. Use for NLP.
Perl text processing and scripting with regular expressions. Use for .pl files.
Formats text according to specified style guidelines. A clean example skill with no security issues.