Results for “text-moderation”

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

Clinical copy-editor that reviews text for communication issues. Use when user says review for prose or improve the prose

Clinical copy-editor that reviews text for communication issues. Use when user says review for prose or improve the prose

Clinical copy-editor that reviews text for communication issues. Use when user says review for prose or improve the prose

Improves typography by fixing font choices, hierarchy, sizing, weight, and readability so text feels intentional. Use when the user mentions fonts, type, readability, text hierarchy, sizing looks off, or wants more polished, intentional typography.

Publication Specialist - Writing, Review, Pre-registration & Quality Assurance Light VS applied: Avoids template-based writing + audience-specific message design Absorbed G3 (Peer Review Strategist), G4 (Pre-registration Composer), F1-F3 (Quality functions) capabilities Use when: writing abstracts, creating summaries, peer review response, pre-registration, reporting checklists, reproducibility Triggers: abstract, plain language, press release, summary, communication, peer review, revision, pre-registration, OSF, PRISMA, CONSORT, reproducibility

Proofread and correct text for grammar, spelling, punctuation, style, clarity, and consistency, with support for multiple style guides and readability analysis. Use when the user requests proofreading or provides relevant inputs for this workflow.

Improves typography by fixing font choices, hierarchy, sizing, weight consistency, and readability, making text feel intentional and polished.

Strip AI-generated tells from a draft and rewrite it in Heath's personal voice (GTM Juice). Fire whenever the user asks to "humanize", "de-AI", "clean up", "rewrite in my voice", "make this sound like me", "kill the AI tells", or "polish this draft". Also fires as the mandatory final pass inside juice-content-dispatch — every LinkedIn post, newsletter, and video script drafted there must run through this skill before the Notion write. Applies the 29-pattern Wikipedia "Signs of AI writing" catalog (em dashes, significance inflation, promotional language, rule of three, synonym cycling, inline-header lists, sycophantic tone, hyphenated word pairs, persuasive-authority tropes, signposting, etc.), then applies a hardcoded Heath-voice calibration: self-implicating first, failure-derived, plain declaratives over bumper-sticker aphorisms, no bow-tied endings, never Mixmax corporate brand voice. Forked from blader/humanizer (MIT).

Correct grammar errors, typos, and improve academic readability in LaTeX, Markdown, or plain-text manuscripts. Scans the document once, then walks through every issue one-by-one asking for approval before applying each fix. Trigger when the user says "readability", "check grammar", "fix typos", "proofread for grammar", "improve readability", "polish wording", "语言润色", "修语法", or asks you to clean up the prose in a paper/chapter/section without wanting full content restructuring.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Detects and fixes patterns including: inflated symbolism, promotional language, superficial analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

CapsFusion: Rethinking Image-Text Data at Scale

Discord Actions

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Rewrite text to remove AI writing patterns and add a natural, human voice.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Detects and fixes patterns including: inflated symbolism, promotional language, superficial analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Detects and removes AI writing patterns from Chinese text, making it sound more natural and human-like. Based on Wikipedia's comprehensive guide to AI writing characteristics.

润色中文学术论文，识别并替换模板化、机械化的AI表达，使文本更自然、更符合人类学者的写作习惯。

Deploy Meta's LlamaGuard moderation model to filter LLM inputs and outputs across 6 safety categories using HuggingFace, vLLM, or FastAPI.

Transforms plain text or markdown into well-structured, reader-friendly markdown with frontmatter, titles, summaries, headings, bold, lists, and code blocks.

Retrieves live messages from local messaging surfaces, including iMessage and DMs, recovers recent one-time codes, and inspects threads before replying, with evidence of the source checked.

Identify grammar, logical, and flow errors in text and suggest targeted fixes without rewriting the entire document.

Select, pair, and implement typefaces for web projects with guidance on readability, CSS implementation, and performance optimization.

Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.

Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.

Humanize text: strip AI-isms and add real voice.

Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Humanize text: strip AI-isms and add real voice.