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Found 393 Skills
Comprehensive Python expertise covering language fundamentals, idiomatic patterns, software design principles, and production best practices. Use when writing, reviewing, debugging, or refactoring Python code. Triggers: Python, .py files, pip, uv, pytest, dataclasses, asyncio, type hints, or any Python library.
Create or improve Makefiles with minimal complexity. Templates available: base, python-uv, python-fastapi, nodejs, go, chrome-extension, flutter.
Download videos from 1800+ platforms (YouTube, Bilibili, Twitter/X, TikTok, Vimeo, Instagram, etc.) and generate complete resource package with video, audio, subtitles, and AI summary. Actions: summarize, download, transcribe, extract video content. Platforms: youtube.com, bilibili.com, twitter.com, x.com, tiktok.com, vimeo.com, instagram.com, twitch.tv. Outputs: MP4 video, MP3 audio, VTT subtitles with timestamps, TXT transcript, MD AI summary. Auto-installs uv, yt-dlp, ffmpeg. Python dependencies managed by uv.
Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy. Performs scRNA-seq QC, normalization, PCA, UMAP, Leiden/Louvain clustering, differential expression (Wilcoxon, t-test, DESeq2), cell type annotation, per-cell-type statistical analysis, gene-expression correlation, batch correction (Harmony), trajectory inference, and cell-cell communication analysis. NEW: Analyzes ligand-receptor interactions between cell types using OmniPath (CellPhoneDB, CellChatDB), scores communication strength, identifies signaling cascades, and handles multi-subunit receptor complexes. Integrates with ToolUniverse gene annotation tools (HPA, Ensembl, MyGene, UniProt) and enrichment tools (gseapy, PANTHER, STRING). Supports h5ad, 10X, CSV/TSV count matrices, and pre-annotated datasets. Use when analyzing single-cell RNA-seq data, studying cell-cell interactions, performing cell type differential expression, computing gene-expression correlations by cell type, analyzing tumor-immune communication, or answering questions about scRNA-seq datasets.
Academic paper writing skill with 12-agent pipeline. v2.4: LaTeX output formatting hardening — mandatory apa7 class, text justification fix, table column width formula, bilingual abstract centering, standardized font stack, PDF must compile from LaTeX. Supports IMRaD, literature review, theoretical, case study, policy brief, and conference paper structures. APA 7.0 (default), Chicago, MLA, IEEE, Vancouver citation formats. Bilingual abstracts (zh-TW + EN). Multi-format output (LaTeX, DOCX, PDF, Markdown). Triggers on: write paper, academic paper, paper outline, write abstract, revise paper, check citations, convert to LaTeX, guide my paper, parse reviews, revision roadmap, 寫論文, 學術論文, 論文大綱, 寫摘要, 修改論文, 檢查引用, 引導我寫論文, 帶我規劃論文, 逐章規劃, 論文架構, 審查意見, 修訂路線圖.
PM Agent Team - Automated product discovery, strategy, and PRD generation. Runs 4 specialized PM agents in parallel to produce a comprehensive PRD before PDCA Plan phase. Integrates pm-skills frameworks (MIT). Use proactively when user wants product analysis before development, needs a PRD, or asks for PM-level planning. Triggers: /pdca pm, pm analysis, product discovery, PRD, pm team, PM 분석, 제품 기획, 제품 발견, PM팀, PRD 작성, PM分析, プロダクト分析, 产品分析, 产品发现, análisis PM, descubrimiento de producto, analyse PM, découverte produit, PM-Analyse, Produktentdeckung, analisi PM, scoperta prodotto Do NOT use for: implementation, code review, existing PDCA phases (plan/design/do/check).
Install, troubleshoot, and use Scrapling CLI to extract HTML, Markdown, or text from webpages. Use this skill whenever the user mentions Scrapling, `uv tool install scrapling`, `scrapling extract`, WeChat/mp.weixin articles, browser-backed page fetching, or needs help deciding between static and dynamic extraction.
Automatically detect GPU vendor, find appropriate PyTorch container image, launch with correct mounts, and validate GPU functionality. Supports NVIDIA, Ascend, Metax, Iluvatar, and AMD/ROCm. Use when user says "setup container", "start pytorch container", or invokes /gpu-container-setup.
Extraire le contenu propre d'articles depuis des URLs (billets de blog, articles, tutoriels) et sauvegarder en texte lisible. À utiliser quand l'utilisateur veut télécharger, extraire ou sauvegarder un article/billet de blog depuis une URL sans publicités, navigation ou encombrement.
Plan Plus — Brainstorming-Enhanced PDCA Planning. Combines intent discovery from brainstorming methodology with bkit PDCA's structured planning. Produces higher-quality Plan documents by exploring user intent, comparing alternatives, and applying YAGNI review before document generation. Use proactively when user mentions planning with brainstorming, intent discovery, exploring alternatives, or wants a more thorough planning process. Triggers: plan-plus, plan plus, brainstorming plan, enhanced plan, deep plan, 플랜 플러스, 브레인스토밍, 기획, 의도 탐색, 대안 탐색, プランプラス, ブレインストーミング, 企画, 意図探索, 计划加强, 头脑风暴, 深度规划, 意图探索, plan mejorado, lluvia de ideas, planificación profunda, plan amélioré, remue-méninges, planification approfondie, erweiterter Plan, Brainstorming, vertiefte Planung, piano migliorato, brainstorming, pianificazione approfondita Do NOT use for: simple tasks that don't need planning, code-only changes.
Modern Python coaching covering language foundations through advanced production patterns. Use when asked to "write Python code", "explain Python concepts", "set up a Python project", "configure Poetry or PDM", "write pytest tests", "create a FastAPI endpoint", "run uvicorn server", "configure alembic migrations", "set up logging", "process data with pandas", or "debug Python errors". Triggers on "Python best practices", "type hints", "async Python", "packaging", "virtual environments", "Pydantic validation", "dependency injection", "SQLAlchemy models".
Create new agent skills with best-practice templates. Guides through skill level selection (L0 pure prompt, L0+ with helper scripts, L1 with business scripts), environment strategy (stdlib/uv/venv), and generates ready-to-edit project files following runtime UX best practices. This skill should be used when creating a new skill, scaffolding a skill project, initializing skill templates, or when the user says 'help me build a skill', 'create a skill', '创建技能', '新建 skill'.