openclaw/building-agentskills/examples/minimal-skill/SKILL.md
Use when the user asks to test a minimal skill template, or types "ping minimal"; replies with a one-line confirmation that the skill activated. Copy this file as a starting point for your own skill; replace name, description, and body to fit your domain.
npx skillsauth add adminlove520/xiaoxi-skills minimal-skillInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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A working minimal SKILL.md the quickstart references. Copy this file as your starting point; replace the frontmatter and body with your own.
NO SKILL.md WITHOUT A DESCRIPTION THAT NAMES A TRIGGER
If your description does not name when the skill should activate, the agent will never trigger your skill. See docs/05-authoring/triggers.md in the building-agentskills repo for the discipline.
When the user types "ping minimal" (or asks to test a minimal skill template), reply with exactly:
Hello from the minimal-skill template. Activation works.
Then stop. Do not add any other text.
name, description, license). No Claude Code extensions; works on every spec-compatible harness.name (must match the parent directory name).description (front-load triggers; aim for under 1,024 characters).data-ai
Spaced-repetition flashcard system. Create cards from facts or text, chat with flashcards using free-text answers graded by the agent, generate quizzes from YouTube transcripts, review due cards with adaptive scheduling, and export/import decks as CSV.
development
Canvas LMS integration — fetch enrolled courses and assignments using API token authentication.
development
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
devops
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.