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Found 9,575 Skills
Eino component selection, configuration, and usage. Use when a user needs to choose or configure a ChatModel, Embedding, Retriever, Indexer, Tool, Document loader/parser/transformer, Prompt template, or Callback handler. Covers all component interfaces and their implementations in eino-ext including OpenAI, Claude, Gemini, Ollama, Milvus, Elasticsearch, Redis, MCP tools, and more.
Création, édition et analyse complète de tableurs avec support des formules, du formatage, de l'analyse de données et de la visualisation. Quand Claude doit travailler avec des tableurs (.xlsx, .xlsm, .csv, .tsv, etc.) pour : (1) Créer de nouveaux tableurs avec formules et formatage, (2) Lire ou analyser des données, (3) Modifier des tableurs existants en préservant les formules, (4) Analyse et visualisation de données dans les tableurs, ou (5) Recalculer des formules.
Boîte à outils complète pour la manipulation de PDF : extraction de texte et tableaux, création de nouveaux PDF, fusion/découpage de documents et gestion de formulaires. Quand Claude doit remplir un formulaire PDF ou traiter, générer ou analyser des documents PDF de manière programmatique et à grande échelle.
Generate CLAUDE.md and AGENTS.md by exploring the codebase
A skill that implements the SDD-RIPER methodology into strictly executable processes. It is applied in code/architecture tasks for "function-level and project-level CodeMap generation, full-modal requirement context bundling, Spec-driven R&D, and RIPER phase gate advancement", and is suitable for multi-round collaborative development with Claude/Codex/other CLI Agents.
Review the latest changes and check whether they comply with the project's documented guidelines (AGENTS.md, CLAUDE.md, or equivalent). Use when reviewing local diffs, recent commits, or feature work and you need a findings-first assessment of architecture, reuse, testing, and repo-specific rules.
Keep AI tooling files (.claude, .codex, .cursor, .windsurf, .augment, .kiro, .cline, .roo, .gemini, etc.) on dev branch but exclude them from main/master. Use when managing branches, creating PRs to main, merging to main, or setting up a repo's branch strategy for AI-assisted development. Triggers on git merge/PR operations targeting main or master.
Multi-Harness Portability is the engineering discipline of writing agent skills, prompts, and configurations that work across every major AI coding harness — Claude Code, Cursor, Codex, Gemini CLI, OpenCode, and beyond.
Manage skills across 20+ AI platforms (Claude Code, Cursor, Copilot, Gemini, OpenClaw, Hermes, etc.). Use `list` as the unified entrypoint. Default behavior is listing skills only; only guide/recommend when the user explicitly asks what skill to use.
Analyzes and compares existing skills from any source (skills.sh, GitHub, Claude marketplace, or local files) against a target skill or requirement. Fetches skill content, evaluates it across 10 dimensions, produces a structured comparison table, identifies gaps, and recommends whether to adopt, adapt, or build from scratch. Trigger when: analyze this skill, compare skills, is this skill good enough, what does this skill do, skill evaluation, should I use this skill, skill gap analysis, paste a skills.sh URL, GitHub skill URL, or upload a SKILL.md file for review.
Install, initialize, verify, and troubleshoot RTK (Rust Token Killer) for AI coding agents. Use when you need to reduce shell-command token output, confirm that the correct `rtk` binary is installed, choose between Homebrew, install.sh, or Cargo installation, wire `rtk init` for Claude Code, Codex, Gemini CLI, Cursor, Copilot, Windsurf, Cline, or OpenCode, or use compact wrappers such as `rtk git status`, `rtk read`, `rtk grep`, `rtk test`, `rtk lint`, and `rtk gain`. Triggers on: rtk, rust token killer, token saver cli, rtk init, rtk gain, codex rtk, gemini rtk, opencode rtk, claude hook token reduction.
Extract frames from video files using ffmpeg for AI/LLM analysis. Use when (1) the user asks to analyze, describe, or summarize a video file, (2) the user wants to extract frames or screenshots from a video, (3) the user provides a video file (.mp4, .mov, .avi, .mkv, .webm, etc.) and asks questions about its visual content, (4) the user wants to identify scenes, objects, or events in a video, (5) the user wants timestamps overlaid on extracted frames for temporal reference. Converts video into JPEG frames that can be attached to LLM prompts as images. Requires ffmpeg on PATH. Supports scene-change detection, model-aware optimization (Claude/OpenAI/Gemini), quality presets (efficient/balanced/detailed/ocr), grayscale and high-contrast OCR mode, and automatic FPS calculation via --max-frames.