youtube-seo-optimizer

Grills the user about a planned feature or code change before any code is written. It reads the relevant code and docs first so every question is specific to the repo, asks one decision at a time with a recommended answer, works through scope, behaviour, edge cases, data, failure modes and rollout, then writes a SPEC.md with numbered requirements, Given/When/Then acceptance criteria tied to them and a test plan, checked by a linter that flags vague words and untraced requirements. Use when the user asks to grill or interview them about a feature, pressure-test an implementation plan, write a spec or PRD for a change, or turn a rough feature request into testable requirements before coding.

Researches and writes SEO-optimized, genuinely helpful blog articles, covering keyword and search-intent analysis, competitor outline gaps, title and meta description, headings, FAQ, internal links, and schema, in the brand's voice and following Google's helpful-content and E-E-A-T principles. Use when the user asks to write a blog post or article, do SEO content, or rank for a keyword.

Converts a screenshot, mockup, or Figma export into a production-ready UI component that reuses the project's existing design system (React/Next, Vue, Svelte, React Native, Flutter, or SwiftUI with Tailwind, CSS modules, or a component library), so the result is responsive and accessible. Use when the user shares an image of a UI and asks to build, implement, or recreate it in code.

Roasts a GitHub profile with humor, then gives genuinely useful advice. It analyzes public repos, stars, languages, missing descriptions and licenses, abandoned projects, lazy commit messages, and activity with a data script, and produces a shareable scorecard and a glow-up plan. Use when the user asks to roast, rate, review, or improve a GitHub profile, or asks "what does my GitHub say about me".

Rewrites a repository's README into a polished, star-worthy page with badges, a hero tagline, demo section, quickstart, features, usage, and contributing info. Use when the user asks to improve, rewrite, polish, or "glow up" a README, or when a repo has a missing or weak README.

Scans an AI agent's instruction and tool-config files (CLAUDE.md, AGENTS.md, skills, slash commands, subagents, Cursor and Cline rules, hooks, MCP server configs) for prompt injection, hidden Unicode or HTML-comment instructions, exfiltration commands, and risky permissions or unpinned MCP packages, then recommends what to remove or quarantine. Use when the user installs third-party skills or plugins, clones an unfamiliar repo, adds an MCP server, or asks whether their agent setup is safe to trust.

Reviews a pull request or local branch diff like a senior engineer, covering correctness, security, performance, naming, and missing tests, and produces prioritized, actionable comments (optionally posted to GitHub). Use when the user asks to review a PR, a diff, a branch, or "check my changes before I push".

Brutally honest but constructive critique of a landing page's copy, UX, design, and conversion. Gives a scored roast plus a prioritized fix list and rewritten headline and CTA options, working from a URL, screenshot, or the page's source code. Use when the user asks to roast, review, critique, or improve the conversion of a landing page, homepage, or pricing page.

Rigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow code-only or run-only exploration, passive repo analysis, verified novelty claims, or implicit experimentation.

Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized `repro_outputs/` bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, score chasing, or broad research assistance outside repository-grounded reproduction.

Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized `train_outputs/`. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.

Rigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.

Search, scrape, crawl, extract structured data, and monitor web pages via the ScrapeGraph AI CLI. Use when the user asks to search the web, scrape a webpage, grab content from a URL, extract JSON from a site, crawl documentation or site sections, monitor a page for changes, inspect request history, check ScrapeGraph credits, or validate API setup.

Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative

When the user wants to create sales collateral, pitch decks, one-pagers, objection handling docs, or demo scripts. Also use when the user mentions 'sales deck,' 'pitch deck,' 'one-pager,' 'leave-behind,' 'objection handling,' 'deal-specific ROI analysis,' 'demo script,' 'talk track,' 'sales playbook,' 'proposal template,' 'buyer persona card,' 'battle card,' 'battlecard,' 'competitive one-pager,' 'win-loss analysis,' 'why are we losing deals,' 'loss reasons,' 'how do I respond to this objection,' 'prospect just said,' 'help my sales team,' 'sales materials,' or 'what should I give my sales reps,' 'cold call script,' or 'voicemail script.' Use this for any document or asset that helps a sales team close deals. For public competitor comparison pages, see competitors. For running buyer interviews or churn research, see customer-research. For marketing website copy, see copywriting. For cold outreach emails, see cold-email. For the offer being sold (bonuses, guarantees, pricing structure), see offers.

When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," "PMF survey," "product/market fit survey," "customer interview questions," "interview outreach," "Sales Safari," or "find out why customers churn/convert/buy." Use for analyzing existing research assets, mining online sources, AND running primary research (interviews and surveys). For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.