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Found 5,953 Skills
Patterns and conventions for creating a good PR
Workday integration. Manage Organizations, Deals, Leads, Projects, Pipelines, Goals and more. Use when the user wants to interact with Workday data.
E-Magazine × E-Ink; 10 Layouts + 5 Color Palettes (Ink/Indigo Porcelain/Forest Ink/Kraft Paper/Dune)
Minimal product pre-launch landing page with email capture, logo, and decorative layers
Invoke this skill when the user asks how to USE the 10x-cli day-to-day — fetching lessons, listing modules, switching AI tools, troubleshooting errors, understanding where artifacts land, or working on a specific OS (Windows, Linux, macOS). Covers commands (get, list, doctor, auth --status/--logout), tool profiles, artifact locations, common errors, and platform-specific tips. Excludes: first-time installation and onboarding (use 10x-cli-setup instead), developing or contributing to 10x-cli source code, and general programming help.
Captures and organizes chaotic brain dumps into a structured, actionable system with zero information loss. Use this skill whenever the user says 'capture this', 'brain dump', 'let me dump some ideas', 'I've got a bunch of thoughts', 'here's everything on my mind', 'idea dump', 'let me get this out of my head', 'I need to organize my thoughts', 'here's what I'm thinking', or any variation where someone is unloading a messy stream of ideas, tasks, thoughts, and plans wanting them turned into something coherent. Also trigger when the user pastes or dictates a long, unstructured block of mixed ideas — even without the exact phrase — the intent is the same. Fast-to-action by design: no upfront intake. Output is four sections (Projects/Ideas, Tasks, Connections, How I Can Help) ending with a directive question. Asks at most one mid-organization clarifying question when a single item is genuinely ambiguous between task and project.
Corrige e analisa escrita acadêmica em PDFs ou documentos de TCCs, dissertações, teses e artigos científicos com rigor de banca examinadora. Use esta skill SEMPRE que o usuário pedir para revisar, corrigir, analisar ou comentar um trabalho acadêmico, TCC, artigo, dissertação, monografia, relatório científico ou qualquer documento de escrita acadêmica. Também acione quando o usuário mencionar: "revisar meu TCC", "corrigir artigo", "feedback acadêmico", "análise do meu trabalho", "comentar PDF", "revisar escrita", "normas ABNT", ou pedir avaliação de estrutura, coerência, referências, metodologia ou linguagem formal. Realiza verificação ativa das fontes citadas via busca na web, detecta afirmações sem suporte bibliográfico, identifica inconsistências metodológicas e gera um PDF anotado com comentários técnicos detalhados e um relatório com nível de rigor de parecerista de periódico científico.
Product research for TikTok Shop — viral potential scoring, creator demand, category analysis
Use this skill when the user requests to "read paper", "analyze paper", "summarize paper", "read a paper", "analyze literature", "help me look at this paper", "paper notes", or provides a PDF file that appears to be an academic paper. It is specialized for CV/DL papers. It also supports Zotero integration: "read this paper...", "quickly look at this paper...", "critically analyze this paper...", "read XXX from Zotero", "batch read papers under the VLA category in Zotero" **Important Trigger Phrases**: "read XXX", "read this one", "help me read" → must call this skill
AI agent skill for CompressO — a free, open-source, offline desktop tool for batch video and image compression built with Tauri + React. Use when the user needs to compress, trim, convert, or embed subtitles into video/image files locally without any network dependency. Covers installation (Homebrew, DMG, MSI, AppImage, DEB), build from source (Rust + Node.js + pnpm), and guidance on FFmpeg/pngquant/jpegoptim/gifski pipelines. Triggers on: compresso, compress video, compress image, batch compression, ffmpeg compression, tauri desktop compression, offline video compress.
Outsider-perspective end-to-end review of a plan, PR, or code change. First questions intent and whether a simpler/more elegant approach would achieve the same goal, then traces the actual code path (not just the diff) to verify the change does what it claims. Output is concise, actionable, and every call carries its rationale. Trigger on /scrutinize and proactively whenever the user asks to review, audit, sanity-check, or get a second opinion on a plan, PR, diff, design doc, or proposed code change.
Guides cleaning and standardizing tabular datasets before analysis, modeling, or reporting—profiling, quality rules, missing values, duplicates, outliers, type coercion, encoding fixes, record linkage, deduplication, high-level PII handling (not legal advice), actuarial/insurance field scrubbing, reproducible scrub pipelines, validation checks, and sign-off. Distinct from warehouse ETL or statistical modeling. Use when the user asks for "data scrubbing", "clean this dataset", "scrub the data", "data cleaning", "dedupe records", "handle missing values", "outlier treatment", "standardize columns", "data quality rules", "profile this table", or "prepare data for modeling". Not warehouse pipelines (data-warehouse-engineer), ML modeling (data-scientist, actuary), privacy programs (compliance-engineer), FinOps only (finops-analyst), or assumption governance (assumption-setting).