Loading...
Loading...
Found 55 Skills
Apply Web Scraping with Python practices (Ryan Mitchell). Covers First Scrapers (Ch 1: urllib, BeautifulSoup), HTML Parsing (Ch 2: find, findAll, CSS selectors, regex, lambda), Crawling (Ch 3-4: single-domain, cross-site, crawl models), Scrapy (Ch 5: spiders, items, pipelines, rules), Storing Data (Ch 6: CSV, MySQL, files, email), Reading Documents (Ch 7: PDF, Word, encoding), Cleaning Data (Ch 8: normalization, OpenRefine), NLP (Ch 9: n-grams, Markov, NLTK), Forms & Logins (Ch 10: POST, sessions, cookies), JavaScript (Ch 11: Selenium, headless, Ajax), APIs (Ch 12: REST, undocumented), Image/OCR (Ch 13: Pillow, Tesseract), Avoiding Traps (Ch 14: headers, honeypots), Testing (Ch 15: unittest, Selenium), Parallel (Ch 16: threads, processes), Remote (Ch 17: Tor, proxies), Legalities (Ch 18: robots.txt, CFAA, ethics). Trigger on "web scraping", "BeautifulSoup", "Scrapy", "crawler", "spider", "scraper", "parse HTML", "Selenium scraping", "data extraction".
Finds data scientists and ML engineers to recruit using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find data scientists on Twitter for recruiting, discover machine learning engineers or AI researchers to hire from X, find data analysts or ML practitioners by specialization on Twitter, identify NLP computer vision or LLM engineers via social signals, find data science professionals open to work on Twitter, build a data science talent pipeline from social, or find researchers posting about job opportunities. Returns handle, name, ML specialty (from bio/tweets), stack (Python/R/TensorFlow), follower count, and open-to-work signals. Ideal for ML engineering hiring managers, AI research labs, and data-driven startups.
Detect crisis signals in user content using NLP, mental health sentiment analysis, and safe intervention protocols. Implements suicide ideation detection, automated escalation, and crisis resource integration. Use for mental health apps, recovery platforms, support communities. Activate on "crisis detection", "suicide prevention", "mental health NLP", "intervention protocol". NOT for general sentiment analysis, medical diagnosis, or replacing professional help.
Write and revise ML, CV, NLP, and systems research papers with strong claim-evidence flow. Use when drafting an abstract, introduction, related work, method, experiments, ablations, discussion, or rebuttal; structuring figures and tables; tightening reviewer-facing logic; or preparing a paper for submission. Triggers on: research paper, paper writing, academic writing, ML paper, CVPR paper, NeurIPS paper, ICLR paper, experiments section, rebuttal, abstract rewrite, contribution framing, reviewer response.
Bengali (Bangla) text processing patterns including Unicode normalization, script detection, tokenization, conjunct handling, and Bangla-specific NLP best practices for AI applications.
dontbesilent AI writing feature recognition. It scans for AI-generated traces in copy and outputs a detection report. By default, it only performs diagnosis without modification. Trigger methods: /dbs-ai-check, /AI检测, "Help me check if there is AI-like tone", "Detect AI features" AI writing fingerprint detection. Scans copy for AI-generated patterns and outputs a diagnostic report. Diagnosis only by default. Trigger: /dbs-ai-check, "check for AI writing", "does this sound like AI"
Use to interpret qualitative feedback, trends, and risks across community channels.
Calculate text similarity using lexical and semantic methods for matching and deduplication. Use this skill when the user needs to find similar documents, detect near-duplicates, or measure semantic closeness between texts — even if they say 'how similar are these texts', 'find duplicates', or 'semantic matching'.
Diffbot integration. Manage Articles, Products, Images, Discussions, Videos. Use when the user wants to interact with Diffbot data.
AI text humanization: reduce AI-detection patterns, natural phrasing, tone adjustment
Wit.ai integration. Manage data, records, and automate workflows. Use when the user wants to interact with Wit.ai data.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.