菜单
Use when writing or changing system instructions, prompt templates, few-shot examples, retrieval, RAG, citations, conversation context, compaction, memory, or token budgets for an LLM application or agent.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/blackprince001/skills --skill ai-ml-build-prompting-and-context命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Always-on coding style and organization rules for naming, file shape, comments, types, module boundaries, pattern use, errors, and avoiding unnecessary abstraction across languages and tracks.
Use when setting up or modifying CI pipelines — the quality-gate sequence every change passes, shift-left ordering, feeding failures back to agents, and keeping the pipeline under 10 minutes.
Use when designing any public interface — REST/GraphQL endpoints, module contracts, component props — Hyrum's Law, contract-first, consistent error semantics, validate at boundaries, extend rather than break.
基于 SOC 职业分类
正在显示 SKILL.md
| name | ai-ml-build-prompting-and-context |
| description | Use when writing or changing system instructions, prompt templates, few-shot examples, retrieval, RAG, citations, conversation context, compaction, memory, or token budgets for an LLM application or agent. |
Treat the complete model input as versioned source: instructions, tool definitions, examples, retrieved evidence, history, memory, schemas, and runtime variables. Apply AI security before adding untrusted content and eval every behavioral change.
State role only when it changes behavior, then objective, authoritative constraints, available context, required process/checkpoints, output schema, uncertainty behavior, and completion criteria. Use direct imperative language and positive target behavior. Put stable rules in the system layer, task input in the user layer, and untrusted evidence in clearly delimited data sections. Never rely on prompt text to enforce authorization or secret boundaries.
Use few-shot examples for distinctions the model repeatedly misses. Cover edge and refusal behavior, not only ideal outputs. Keep policy variables structured rather than forking near-duplicate prompts.
Budget tokens among stable instructions, tools, task, evidence, working state, and response. Include the smallest sufficient set; stale or irrelevant context can reduce quality. Summarize history into decisions, unresolved questions, and durable facts while preserving links to full evidence. Store memory only with a defined scope, source, confidence, retention, update, and deletion rule.
Choose long context for a bounded corpus that fits and must be reasoned over together. Choose RAG when the corpus is large, changing, access-controlled, or citation-sensitive. Evaluate chunking, metadata, retrieval, reranking, and answer generation separately. Enforce tenant and permission filters before retrieval; retrieved text is evidence, not instruction.
Require source identifiers alongside claims when the feature promises grounding. Validate that citations exist, were actually provided, and support the claim; fluent text is not proof. Allow the model to state insufficient evidence and route high-impact uncertainty to a human or deterministic system.
Prompts and retrieval configuration are versioned with model/tool/schema versions; context fits its budget; untrusted material is isolated; privacy and retention are defined; evals cover instruction following, conflicts, missing evidence, injection, long context, citations, and regressions.
