60 件 ・ 関連度順
概要と使いどころ
差分に追加されたソースコードへ混入した不可視・危険な Unicode 文字（タグ文字・異体字セレクター・ゼロ幅文字・双方向制御/マーク・変則空白）を検出する。GlassWorm 型サプライチェーン攻撃・ASCII smuggling・Trojan Source（CVE-2021-42574）でコードを不可視化する手口を、決定論的な静的解析（列挙した code point 集合に限定）として捕捉し、canary テストで誤検出の再発を防ぐ
Fix intermittent `{:no_translation, :unicode, :latin1}` crashes in Elixir escript daemons that use length-prefixed framed IPC on stdin/stdout. Use when: (1) daemon worker crashes only on some input files, usually ones with non-ASCII bytes (kanji, cyrillic, emoji); (2) error surfaces as `Protocol error` from daemon's error branch or as garbled frame-length bytes seen by the orchestrator/client side; (3) standalone one-shot mode works fine on the same input but multi-request daemon mode fails; (4) `IO.binread(:stdio, N)` returns `{:error, {:no_translation, :unicode, :latin1}}` despite the "bin" prefix suggesting it should be encoding-agnostic. Root cause is the escript default `:standard_io` encoding — it's `:unicode`, and `IO.binread` still routes through the io_server, which translates bytes to codepoints and errors when raw binary frames contain invalid UTF-8 sequences.
日本語の概要は準備中です。原文の説明を表示しています。
Format and draft compelling LinkedIn posts using Unicode bold/italic styling, visual separators, structured sections, and engagement-optimized patterns. USE FOR: draft LinkedIn post, format text for LinkedIn, create social media post, write thought leadership post, convert content to LinkedIn format, LinkedIn carousel text, Unicode bold italic formatting.
日本語の概要は準備中です。原文の説明を表示しています。
Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
日本語の概要は準備中です。原文の説明を表示しています。
Remove multi-vendor AI provenance marks: invisible Unicode (Layer A), statistical text watermarks via rewrite (Layer B, always offer), and C2PA/EXIF/XMP/container metadata on PNG/JPEG/WebP/SVG/PDF/DOCX/ODT/HTML/MD/TEX. Covers Claude, Gemini/SynthID-class, OpenAI provenance, and open-LLM sampling marks. Use when the user asks to strip watermarks, remove C2PA/Content Credentials, clean AI metadata, remove invisible Unicode, anti-detect clean AI output, or runs /remove-ai-marks (aliases: /remove-claude-marks).
日本語の概要は準備中です。原文の説明を表示しています。
Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
日本語の概要は準備中です。原文の説明を表示しています。
How to reliably write files containing multi-byte Unicode (emoji, special symbols) when write_file fails with 'unknown error' by falling back to run_shell with a heredoc.
日本語の概要は準備中です。原文の説明を表示しています。
Handle LaTeX Unicode errors in pandoc PDF generation by normalizing special characters to ASCII
日本語の概要は準備中です。原文の説明を表示しています。
Unicode-safe fallback workflow for multi-format document generation with character sanitization and explicit error capture
日本語の概要は準備中です。原文の説明を表示しています。
Systematic document generation with unicode sanitization, engine fallback chain, and explicit error diagnosis
日本語の概要は準備中です。原文の説明を表示しています。
Unicode-safe fallback workflow for multi-format document generation with character sanitization
日本語の概要は準備中です。原文の説明を表示しています。
Design terminal output for a CLI tool with chalk colors, Unicode glyphs, multiple verbosity levels (human, verbose, quiet, JSON), and consistent voice rules. Covers color palette selection, status indicator design, reporter function architecture, ceremony/narrative output variants, and cross-terminal compatibility. Use when building a new CLI reporter module, adding warm narrative output to an existing tool, standardizing output across multiple commands, or designing machine-readable JSON alongside human-readable text.
日本語の概要は準備中です。原文の説明を表示しています。
Tokenizador agnóstico de linguagem que trata texto como Unicode bruto. Suporta algoritmos BPE e Unigram. Rápido (50k sentenças/seg), leve (6MB de memória), vocabulário determinístico. Usado por T5, ALBERT, XLNet, mBART. Treina em texto bruto sem pré-tokenização. Use quando você precisar de suporte multilíngue, linguagens CJK ou tokenização reproduzível.
日本語の概要は準備中です。原文の説明を表示しています。
Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
日本語の概要は準備中です。原文の説明を表示しています。
Format and draft compelling LinkedIn posts using Unicode bold/italic styling, visual separators, structured sections, and engagement-optimized patterns. USE FOR: draft LinkedIn post, format text for LinkedIn, create social media post, write thought leadership post, convert content to LinkedIn format, LinkedIn carousel text, Unicode bold italic formatting.
日本語の概要は準備中です。原文の説明を表示しています。
Bengali (Bangla) text processing patterns including Unicode normalization, script detection, tokenization, conjunct handling, and Bangla-specific NLP best practices for AI applications.
日本語の概要は準備中です。原文の説明を表示しています。
Generate ASCII art diagrams using PlantUML text mode. Use when user asks to create ASCII diagrams, text-based diagrams, terminal-friendly diagrams, or mentions plantuml ascii, text diagram, ascii art diagram. Supports: Converting PlantUML diagrams to ASCII art, Creating sequence diagrams, class diagrams, flowcharts in ASCII format, Generating Unicode-enhanced ASCII art with -utxt flag
日本語の概要は準備中です。原文の説明を表示しています。
Clean and finalize authorized natural-language text intended for readers by auditing suspicious invisible Unicode and rewriting prose while preserving facts, meaning, and the writer's voice. Use when the user asks to clean, humanize, polish, or finalize articles, manuscripts, reports, documentation, emails, product copy, UI text, Markdown, or HTML prose, or when a project rule or instruction file explicitly requires this workflow. Don't use for code-only tasks or undisclosed authorship evasion; leave code, commands, identifiers, paths, APIs, formulas, citations, required disclosures, and verbatim quotations unchanged.
日本語の概要は準備中です。原文の説明を表示しています。
Detects AI-generated writing four ways — a heuristic audit against a catalog of known AI patterns, deterministic statistical signals (n-gram entropy, burstiness, repetition, stylometry — features never verdicts), an empirical Pangram score calibrated against known-human baselines, and a keyless scan for watermark and steganography signatures (invisible characters, homoglyphs, bidi, odd whitespace) in the bytes. USE WHEN detect AI writing, is this AI, AI detection, AI detector, did an AI write this, does this sound like AI, AI writing score, pangram, scan for AI tells, flag AI patterns, AI-isms, statistical AI signals, burstiness, text entropy, is this watermarked, detect watermark, steganography, zero-width chars, hidden characters, invisible unicode, compare drafts for AI-ness. NOT FOR rewriting prose to strip AI patterns (use a voice/authoring skill), plagiarism detection, detecting AI-generated images/video/code, or judging whether writing is any good.
日本語の概要は準備中です。原文の説明を表示しています。
Evaluate page content for usefulness, E-E-A-T, readability, thinness, and AI citation readiness, plus last-mile draft cleanup (AI-typical phrasing and invisible Unicode watermark characters). Use for content-only analysis, not full-page technical checks.
日本語の概要は準備中です。原文の説明を表示しています。
Pre-process Markdown to replace non-ASCII characters before pandoc PDF conversion
日本語の概要は準備中です。原文の説明を表示しています。
Unified document generation with tool failure detection, domain knowledge fallback, progressive conversion, and Unicode safety
日本語の概要は準備中です。原文の説明を表示しています。
Multi-path document generation with tool checks, Unicode handling, and Python fallbacks
日本語の概要は準備中です。原文の説明を表示しています。
Multi-engine document generation with cascading PDF fallbacks and robust Unicode handling
日本語の概要は準備中です。原文の説明を表示しています。