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AI Agent Skills Directory with categorization, English/Chinese translation, and script security checks.

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All Skills

Total 56,830 skills, AI & Machine Learning has 9448 skills

Categories

Showing 12 of 9448 skills

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AI & Machine Learningrohitg00/ai-engineering-f...

check-understanding

Phase quiz for AI Engineering from Scratch. Trigger with "quiz me", "test phase", "check my understanding", "do I know phase 3", or `/check-understanding <phase>`.

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13
AI & Machine Learninggupsammy/claudest

repair-skill

This skill should be used when the user asks to "fix my skill" or "audit this skill". Make sure to use this skill whenever the user mentions skill quality, structural issues, broken skills, or skill diagnostics — even if they don't explicitly say "repair-skill". Not for adding features or improving effectiveness — use improve-skill. Not for agents — use repair-agent.

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13
AI & Machine Learningbklit/bklit-ui

wiki-llms-txt

Generates llms.txt and llms-full.txt files for LLM-friendly project documentation following the llms.txt specification. Use when the user wants to create LLM-readable summaries, llms.txt files, or make their wiki accessible to language models.

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13
AI & Machine Learningdonchitos/claude-code-gam...

team-polish

Orchestrate the polish team: coordinates performance-analyst, technical-artist, sound-designer, and qa-tester to optimize, polish, and harden a feature or area for release quality.

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13
AI & Machine Learningstiffjobs/po-once-agent-s...

po-once

Use Po Once's organization-scoped agent API to list connected accounts, upload media, create content, schedule or publish posts, inspect status, and delete eligible scheduled posts through a local helper script.

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13
1 scripts/Attention
AI & Machine Learningvinvcn/mattpocock-skills-...

qa

Interactive QA session where users report bugs or issues through conversation, and the agent creates GitHub issues. Explore the codebase in the background to obtain context and domain language. Use when user wants to report bugs, do QA, file issues conversationally, or mentions "QA session".

🇨🇳|ChineseTranslated
13
AI & Machine Learningowainlewis/agent-skills

clarify

Turn a vague, messy, or multi-part user ask into a clean, self-contained prompt that a fresh agent could execute without further questions. Interview the user one question at a time — walking down the decision tree, branching on each answer — until the prompt is tight, then output the final prompt as the deliverable. Trigger eagerly: any voice-dictated input, filler-heavy prose, underspecified references ("the thing", "that script"), multi-part requests, or any plan the user wants stress-tested. The skill itself can be skipped for trivial one-line requests where producing a prompt artifact would be pure ceremony — but once invoked, always produce the prompt, even if execution looks trivial.

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13
AI & Machine Learningarchieindian/openclaw-sup...

using-superpowers

Bootstrap skill — teaches the agent how to find and invoke skills. Use when starting any new task or session.

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13
AI & Machine Learningnvidia/skills

nemo-mbridge-perf-sequence-packing

Validate and use packed sequences and long-context training in Megatron-Bridge, distinguishing offline packed SFT for LLMs from in-batch packing for VLMs, and applying the right CP constraints.

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13
AI & Machine Learningnvidia/skills

nemo-mbridge-multi-node-slurm

Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures. Covers srun-native vs uv run torch.distributed approaches, container setup, NCCL timeouts, OOM sizing for MoE models, and interactive allocation.

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13
AI & Machine Learningnvidia/skills

nemo-mbridge-perf-cpu-offloading

Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.

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13
AI & Machine Learningnvidia/skills

nemo-mbridge-perf-moe-optimization-workflow

Systematic workflow for MoE training optimization in Megatron Bridge, based on the Megatron-Core MoE paper. Covers the Three Walls framework, parallel folding, recompute strategy, dispatcher choice, and CUDA-graph bring-up.

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