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Found 6,369 Skills
Web data extraction using 55+ Apify Actors for AI-driven scraping. Supports Instagram, Facebook, TikTok, YouTube, Google, and more. Auto-selects best Actor for the task. Structured output in JSON/CSV with rate limiting and ethical scraping guidelines.
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of "Word doc", "word document", ".docx", or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a "report", "memo", "letter", "template", or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
Facilitates the learning step of a proven customer-interview method — the recording half: turning raw material from ONE customer conversation (transcript, notes, memory dump) into a brief per-person debrief file, mapped against the numbered interview-question list (Q1, Q2, … with H-number tags). One concise answer per question actually asked, key phrases kept verbatim, unasked questions honestly skipped, a one-line commentary only where in-the-moment context helps later analysis, and an addenda section for everything heard that fit no question. Load when the user has just done a customer interview and says 'here's the transcript,' 'process my call notes,' 'file this conversation against my questions,' or 'add this interview to the record.' Do NOT load for writing goals, hypotheses, or interview questions (earlier steps), for synthesizing patterns across multiple interviews and updating hypotheses (the next step), or for summarizing meetings or transcripts unrelated to customer interviews.
Facilitates the third step of a proven customer-interview method: translating hypotheses into open-ended, unbiased interview questions — each a miniature experiment designed to test one hypothesis without leading the witness. Two modes: given a single hypothesis, it grills the question into shape and outputs the final question in chat; given a HYPOTHESES.md file, it iterates the whole list, grouping related hypotheses, and maintains a QUESTIONS.md file (numbered Q1, Q2, … mapped to H-numbers) as a live, resumable artifact. Load when the user has hypotheses and wants interview questions, asks how to phrase a question for customers without biasing the answer, or says 'turn my hypotheses into questions' or 'help me ask about X without leading.' Do NOT load for writing goal questions or hypotheses (earlier steps), for conducting or analyzing the interviews themselves, for survey/questionnaire design, or for job interviews.
Interrogates a user's decision, plan, pitch, positioning, target market, pricing, or still-forming idea with sharp, unsparing questions — extended when useful to hostile or even unfair framings — until the plan sharpens into defensible decisions, or the user concludes it isn't a good idea after all. Acts as the constructive devil's advocate that the user cannot be for themselves. Load when the user wants to stress-test, attack, pressure-test, find holes in, or play devil's advocate against something they're attached to; when they ask to be "grilled," "beat up," or told "why I'm wrong"; when they suspect their plan contains wishful thinking, fluffy language, or an unnamed elephant in the room; or when they need to convert vague aspirations into real strategic decisions (with consequences accepted). Do NOT load for ordinary code review, prose editing, or general feedback requests without an adversarial frame.
Facilitates the final step of a proven customer-interview method: distilling everything a round of interviews produced (GOALS.md, HYPOTHESES.md, QUESTIONS.md, and a directory of per-interview debriefs) into a single FINAL-REPORT.md the whole company can use. Top: a summary as brief as possible without losing salient information. Below: numbered findings (F1, F2, …) tagged validated / disproved / directional / watch / untested, every one citing debriefs and quoting customers verbatim, plus per-area briefs that marshal the evidence for ideal-customer definition, positioning, pricing, marketing & sales, and product priorities. Load when the user says 'write up what we found from the interviews,' 'summarize the interview results for the team,' or 'turn the interviews into a report.' Do NOT load for updating hypotheses from interviews (the synthesis step), for recording one conversation (the debrief step), or for actually doing the positioning, ideal-customer, or pricing work the report feeds.
Facilitates the first step of a proven ideal-customer (ICP) method: gathering raw, honest, specific observations about what a company and product actually are — before any judgment about strengths or weaknesses. Walks the user through twelve unsparing question categories (what customers praise, the complaint with no defense, what separates your most profitable customers, and more) — or processes a team's write-storm notes one observation at a time — and records the results in OBSERVATIONS.md (numbered O1, O2, …), vivid and unevaluated. For a company operating online, it first scans public reviews and press into an External Research section that seeds it. Load when the user wants to figure out their ideal customer, take an honest look at their company, run a strengths-and-weaknesses exercise from scratch, or says 'who is our Carol' or 'what are we actually good at.' Do NOT load to classify observations into strengths and weaknesses (the next step), or for personal self-reflection unrelated to a company.
Upgrade Three.js games from basic/prototype visuals to premium AAA-inspired browser graphics. Combines art-direction critique, procedural model building, technical art, mandatory external asset sourcing decisions, threejs-3d-generator assets, threejs-image-generator concept/texture workflows, scene visual polish, material/texture libraries, world prop kits, shaders, VFX readability, render budgets, LOD/instancing, render pipeline, and visual scorecard gates. For premium games with characters, vehicles, ships, weapons, buildings, signature props, skies, textures, decals, logos, icons, or GUI art, load the relevant generator skills before deciding procedural assets are enough.
Build production UI that reads as a deliberate choice for this project rather than an LLM default, and audit shipped UI for the tells that give it away. Use when the user says "build this page", "make this UI not look AI-generated", "this looks like slop", "design this screen", "audit our UI", "make the frontend look good", or "/uikit". Reads a project's DESIGN.md when one exists; never writes it.
Review EXISTING test code for quality, smells, and testability issues. Detects test smells across six dimensions — readability, reliability, diagnostic value, design, AI-generated, and coverage — analyzes testability of application code, and backs the qualitative smells with mutation testing. Use when: "review my tests," "test quality audit," "test smells," "testability analysis," "are these tests any good." Not for: generating new tests — use `ai-test-generation`. Not for: testing AI features in your product — use `ai-system-testing`. Related: unit-testing, shift-left-testing, coverage-analysis, ai-test-generation.
Diagnose HPC runtime and scheduler problems for materials simulations, including MPI/OpenMP/GPU layout, modules, CUDA/Kokkos hints, scratch paths, walltime, job arrays, restart strategy, scheduler portability, and resource mismatch. Use when an ALREADY-SUBMITTED job failed, ran slowly, got killed, or behaved differently on a cluster than on a workstation. To author or vet a NEW submission script before running, use slurm-job-script-generator instead.