Total 46,971 skills
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Platform-specific guidelines, best practices, and strategies for social media marketing
Drupal cache contexts implementation guide. Use when asked about request-based cache variations, user.roles vs user context, URL contexts, language contexts, custom cache contexts, or cache context hierarchy. Helps prevent cache explosion from overly broad contexts.
When the user wants to optimize delivery routes, solve vehicle routing problems, or minimize transportation costs. Also use when the user mentions "route planning," "delivery optimization," "VRP," "TSP," "multi-stop routing," "route sequencing," or "dispatch optimization." For fleet sizing and management, see fleet-management. For last-mile specific challenges, see last-mile-delivery.
When the user wants to optimize truck loading, load delivery vehicles, or plan vehicle capacity utilization. Also use when the user mentions "truck loading," "delivery vehicle optimization," "van loading," "cargo van packing," "multi-drop vehicle loading," "delivery route loading," "axle weight distribution," or "vehicle utilization." For container loading, see container-loading-optimization. For route optimization, see route-optimization.
When the user wants to model inventory systems with uncertain demand, optimize safety stock levels, implement (s,S) or (Q,r) policies, or analyze service levels under uncertainty. Also use when the user mentions "stochastic inventory," "probabilistic inventory," "(Q,r) policy," "(s,S) policy," "base stock policy," "safety stock optimization," "service level constraints," "lead time demand distribution," "fill rate calculation," or "inventory with demand uncertainty." For deterministic models, see economic-order-quantity or lot-sizing-problems. For single-period uncertainty, see newsvendor-problem.
Use ezBookkeeping API Tools script to record new transactions, query transactions, retrieve account information, retrieve categories, retrieve tags, and retrieve exchange rate data in the self hosted personal finance application ezBookkeeping.
Use this skill whenever a user wants to run, install, configure, or understand open-ralph-wiggum (ralph). This skill can be used by any AI assistant or IDE agent (GitHub Copilot, Claude Code, Cursor, Windsurf, etc.). Triggers on: "ralph", "ralph wiggum", "agentic loop", "iterative AI loop", "autonomous coding loop", "how to install ralph", "how to use ralph with Claude Code / Codex / Copilot / OpenCode", "ralph --agent", "ralph --tasks", "ralph --status", "--max-iterations", "--rotation", "how do I run ralph in VS Code / Cursor / JetBrains / Neovim", or any question about looping an AI coding agent until a task is done. Even if the user doesn't say "ralph" explicitly — if they want to run an AI agent in a loop until a promise tag appears in its output, use this skill.
GENERator DNA 序列生成模型的昇腾 NPU 迁移 Skill，适用于将基于 HuggingFace Transformers 的 Causal LM 从 CUDA 迁移到华为 Ascend NPU，覆盖环境搭建、依赖安装、代码适配、多进程处理和 sequence recovery 验证。
Used for reviewing GitCode PRs, generating in-depth review conclusions or publishing line-by-line comments by combining PR metadata, diffs, and the context of the entire code repository. It is used when users want to review a GitCode PR, check a GitCode PR link, analyze change risks, or publish review comments to a GitCode PR. Typical trigger phrases include "review this PR", "inspect this PR", "check PR", or directly providing a GitCode PR link, such as https://gitcode.com/owner/repo/pull/123.
Enforce code readability and state minimisation before opening or updating a pull request. Use when code is functionally complete and needs a final simplification pass focused on skimmability: reducing arguments, removing optionality and overrides, collapsing unnecessary abstractions, preferring discriminated unions, adding assertions at boundaries, handling variants exhaustively, deleting incidental changes, and making the diff shorter, clearer, and easier to review.
Calculate MFU (Machine FLOP Utilization) for operators such as matmul/GEMM, and provide clear formulas and derivation processes.
Migrate GPU/CUDA Triton operators to Triton-Ascend, or rewrite Python/PyTorch operators into Triton-Ascend implementations that can run on Ascend NPU. When clear optimization opportunities are identified, directly output the optimized code, minimal validation script, and troubleshooting instructions. This skill should be prioritized when users mention 昇腾 (Ascend), Ascend, NPU, triton-ascend, Triton operator migration, PyTorch operator rewriting, coreDim, UB overflow, 1D grid, physical core binding, block_ptr, stride, memory access alignment, mask performance, dtype degradation, operator optimization, or directly ask questions like "How to use this skill", "How to run it in the command line", "How to perform migration/validation in a container", even if users do not explicitly say "write a skill" or "perform migration".