Loading...
Loading...
Found 1,278 Skills
Best practices for prompt engineering and context engineering for Coding Agent prompts
Strip AI writing patterns from text — em dashes, stock phrases, promotional inflation, performed authenticity, rule-of-three lists. Use when user says "humanize this", "make it sound human", "strip AI patterns", "clean up the copy", or after /content-gen or /landing-gen produces output.
Agentica server + Claude proxy setup - architecture, startup sequence, debugging
This guide covers the design philosophy, core concepts, and practical usage of the AgentScope framework. Use this skill whenever the user wants to do anything with the AgentScope (Python) library. This includes building agent applications using AgentScope, answering questions about AgentScope, looking for guidance on how to use AgentScope, searching for examples or specific information (functions/classes/modules).
Explain and document MTHDS bundles. Use when user says "what does this pipeline do?", "explain this workflow", "explain this method", "walk me through this .mthds file", "describe the flow", "document this pipeline", "how does this work?", or wants to understand an existing MTHDS method bundle.
硅基流动（SiliconFlow）云服务平台文档。用于大语言模型 API 调用、图片生成、向量模型、在 Claude Code 中使用硅基流动、Chat Completions API、Stream 模式等。
Adapter boundary rules for plugin integrations. Trigger: Changes in plugin scripts/hooks for Claude, OpenCode, Gemini, or Codex.
Fetch, organize, and analyze LangSmith traces for debugging and evaluation. Use when you need to: query traces/runs by project, metadata, status, or time window; download traces to JSON; organize outcomes into passed/failed/error buckets; analyze token/message/tool-call patterns; compare passed vs failed behavior; or investigate benchmark and production failures.
Merges valuable content into permanent documentation, then deletes source files. Use when you have untracked *_REPORT.md or *_ANALYSIS.md files, git status shows markdown artifacts that shouldn't be committed, preparing PR and need to clean up working artifacts, preserving insights from code reviews. Do not use when files are already in docs/ or skills/ locations. DO NOT use when: files are intentionally temporary scratch notes. DO NOT use when: source files have no extractable value.
Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.
Adversarial code review using the opposite model. Spawns 1–3 reviewers on the opposing model (Claude spawns Codex, Codex spawns Claude) to challenge work from distinct critical lenses. Triggers: "adversarial review".
DeepSeek AI large language model API via curl. Use this skill for chat completions, reasoning, and code generation with OpenAI-compatible endpoints.