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提示词

2026年10月23日 收录
  1. X · @Voxyz_ai引用

    Agent Skills 标准捐献与技能编写六条铁律

    Agent Skills 标准捐入 Linux Foundation,附官方技能编写最佳实践与一键沉淀技能的提示词。

    Anthropic just donated the Agent Skills standard to the Linux Foundation, the same home MCP went to. Since it launched last October, 𝟭 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝘀𝗸𝗶𝗹𝗹𝘀 have been published and installed nearly 280 million times, and 70+ agent platforms support it, including ChatGPT, Cursor, GitHub Copilot and VS Code.
    
    Over that year, how to write a skill settled into a standard method, and the official guide went to the foundation too. The 6 most useful rules from it:
    → Do the task with your agent for real first, then turn the steps that worked, the corrections you made, the input and output formats and the context you added into the skill. A model writing from general knowledge gives you filler like "handle errors appropriately"
    → Ask of every line: would the agent get this wrong without it? If not, cut it
    → At startup the agent reads only the name and description, so the description decides whether the skill ever gets used. Write it as "use this skill when...", lean pushy, list the cases users won't spell out, and stay under 1,024 characters
    → Keep SKILL.md under 500 lines and 5,000 tokens, move details into references/, and say when to read which file
    → Hard-code the exact command for fragile steps, explain the why where there's room for judgment, and when there are several ways to do something, give one default
    → In many skills the most valuable part is 𝘁𝗵𝗲 𝗴𝗼𝘁𝗰𝗵𝗮𝘀 𝗹𝗶𝘀𝘁: every time you have to correct the agent, add a line
    
    Right after your agent finishes a task you hand it often, send this to Claude Code in the same session to save how you did it as a standard skill 👇
    "Following https://t.co/YN17Wv07wD, turn the task we just did into a skill. Use only the steps that worked in this conversation, the places I corrected you, and related fixes in CLAUDE.md, AGENTS.md and the git history; don't fill gaps from general knowledge. Write the description as 'use this skill when...', include cases I didn't spell out but that should trigger it, and keep it under 1,024 characters. Keep SKILL.md under 500 lines, move details into references/ with a note on when to read each file, and give the gotchas their own section. Use lowercase letters, numbers and hyphens for the name, matching the folder name. Finally, write 2-3 real test cases in evals/evals.json (prompt plus expected output), run each one, and show me the results and what you did."

    提示词 · 技能 · Agent 工程 · AI 应用

  2. X · @davekiss图片

    AGENTS.md 反过度设计:不为理论风险扩复杂度

    Dave Kiss 点赞 opencode 的 AGENTS.md 规则:加复杂度前先讲清失败模式与成本,征得用户同意。

    This is such a banger rule in @opencode's AGENTS.md
    
    "Before adding complexity for a speculative or vanishingly unlikely race or security edge case, explain the concrete failure mode, likelihood, and complexity cost to the user and get their buy-in. Do not silently expand scope for theoretical robustness."
    AGENTS.md 反过度设计:不为理论风险扩复杂度 的效果图

    提示词 · 软件工程提示词 · Agent 工程 · 范围控制 · 过度工程 · 研发效能

  3. X · @thomaszhou_引用

    AGENTS.md 优化与死代码清理提示词合集

    整理 AGENTS.md 优化、死代码清理与实验审计的可复制 Prompt,附 OpenAI 大规模清理案例。

    很有用的最佳实践!AGENTS.md的优化几乎每个人都能用到。
    
    我挑了文章里面几个常用场景,把prompt贴过来了,方便大家直接复制带走:
    
    1. 科学优化你的 AGENTS.md
    
    Prompt:
    查看我最近的 Codex 会话,找出一条似乎误导了智能体或导致不必要工作的 AGENTS.md 指令,并选出发生该问题时的原始用户提示词。
    使用相同的代码仓库快照和模型,将该提示词重新运行两次:
    保持当前 AGENTS.md 指令不变。
    仅删除或修正那一条指令。
    比较两次运行的任务完成质量、智能体行为、工具调用、Token 用量、运行时间和费用。展示这条指令,并说明修改它是否带来了改善。
    
    2. 清理死代码
    
    Prompt:
    帮我识别并清理仓库中的死代码,你可以自由检查 Slack、Statsig(AB实验平台)或任何其他你需要的来源,以确认这些内容确实不再使用,并且可以被清理或简化。
    
    3. 全仓库实验审计与 CPU 成本优先级排序
    
    Prompt:
    /goal 找出整个代码库中所有过期、固定不变、已废弃或已全量发布的 Statsig 门控、配置、实验和图层,确保它们可以在不影响生产环境的情况下被安全移除。审计所有的 Statsig 项目和清单,而不仅仅是标记为过期的对象。 找出通过枚举、常量、包装器、别名、模式、动态拼接名称以及跨服务消费者进行的间接调用。核对每一个在生产环境中被引用到的常量;不要仅仅生成一份保守的简短列表就停止。 考虑每个对象的云资源使用情况(包括 CPU)。在可用的情况下利用生产环境遥测数据和性能分析文件来识别高开销的无效检查。 生成一份 Markdown 报告,将所有验证过的清理候选项按可并行处理的归属团队/文件工作流进行分组,包含生产环境的值、证据、负责人、调用点,以及观察到的评估开销或 CPU 成本(如有)。不要修改线上配置或服务。

    提示词 · 软件工程提示词 · Agent 工程 · 死代码清理 · 代码审计 · 研发效能

  4. X · @mattpocockuk

    用「一次一问」的斜杠命令澄清欠定义需求

    Matt Pocock 分享:问题极度欠定义时,用 /grill-me 让 AI 一次只问一个问题来做需求澄清。

    I still sometimes say:
    
    /grill-me about this, one question at a time
    
    Usually when I REALLY have no idea what I'm building. When the problem is hugely underspecified and a single Q&A round could send us in massively different directions

    提示词 · 需求澄清 · 提问策略 · 逐问引导 · 技能 · Agent 工程

  5. X · @mattpocockuk

    用 Pre-commit 钩子限制 AI 生成文件体积的提示词

    Matt Pocock 分享一条提示词:用 pre-commit 钩子拦截超 5000 token 的文件,迫使 Agent 拆分模块、保持代码库整洁。

    Prompt of the day:
    
    Create a pre-commit hook that prevents files over 5,000 tokens. Tokens = bytes/4.
    
    Run only on staged files. Ignore files which are not authored by agents, like lockfiles or codegen.
    
    Provide explicit instructions on how to split files in the error message.
    
    The simplest investment I ever made in my codebase. So good.

    提示词 · 软件工程提示词 · 预提交钩子 · 文件拆分 · 代码质量 · Agent 工程

  6. X · @onevcat

    onevcat 全局 AGENTS.md 实践:反谄媚与 gh stack

    onevcat 在全局 AGENTS.md 新增两条规则:让 Agent 如同事般指出用户错误,并用 gh stack 管理连锁 PR。

    最近给自己的全局 AGENTS.md 加了两条:
    1. 用户可能会出错,当用户出现明显错误或决策冲突时,毫不留情地指出。你是同事,不是奴仆。
    2. 多个连锁 PR,使用 gh stack
    
    前一条因为模型比我聪明多了,大部分时候任务失败都是我自己犯蠢。后一条现在模型们还不喜欢用 gh stack,可能过两天就能删。

    提示词 · 软件工程提示词 · Agent 工程 · 研发效能 · 代码协作 · 协作规范

  7. X · @mattpocockuk

    wait-what 提示词:回收 Coding Agent 长任务中散落的疑问

    一条让自主运行数小时的 Coding Agent 汇总途中疑问的实用提示词技巧。

    Prompt of the day:
    
    /wait-what questions did you have for me
    
    Great for when the agent has gone off on its own for hours and has random questions scattered through its context window.

    提示词 · 助手提示词 · 上下文管理 · 斜杠命令 · Agent 工程

  8. X · @dotey引用

    Fable 出方案、Opus 执行、Advisor 验收的省钱闭环

    用高能力模型写方案、性价比模型执行并自动验收,形成省 Token 的多 Agent 协作闭环。

    最近摸索了一种新的节约 Token 但是效果不错的模式(都是被逼的,Token 太贵):
    
    Fable 写设计方案 -> 
    (Loop 开始)
    Opus 执行 -> Advisor(Fable) 验收
    (Loop 结束)
    
    步骤是这样的:
    
    1. 先 Claude 里面设置一下 advisor 为 Fable
    > /advisor fable
    
    这样在使用 Opus,Sonnet 模型的时候,遇事不决它会请教 Fable,可能会有无谓的 Fable 消耗,但还是可以接受也值得
    
    就好比请了个高级工程师当顾问
    
    2. 复杂一点的问题先让 Fable 出方案,最大化利用 Fable 的能力。
    
    就好比你让一个高级工程师写一个技术方案,让普通工程师去执行
    
    3. 方案写好了确认了,让 Opus 去执行,但是执行的时候,加一句提示词:
    
    > 完成后让你的 advisor 验收一下,如果验收有问题就按 advisor 的反馈修复,修复完成后再让 advisor 验收,直到通过 advisor 的验收为止
    
    可以配合 /goal 使用效果更佳
    
    这样 Opus 在实施完成后就会找 Advisor 验收,通过 Advisor 就会给出反馈,然后 Opus 继续修改。
    
    这样的好处就是整个过程你不需要人工去用 Fable 确认—— 以前我就是这么干的,Opus 执行完,我得回到之前 Fable 的会话去让它验收,或者新开一个会话,把文档发给 Fable 验收。
    
    现在 Agent 自己就能形成闭环,不用你自己参与。
    
    就好比你请了个高级顾问,复杂问题找顾问出方案,执行让你的工程师去按照方案执行,每次工程师做完自己去找顾问验收,顾问不仅验收还会告诉工程师哪里不对,然后工程师自己改,不用说每次做完找你,你再找顾问。

    提示词 · 软件工程提示词 · Agent 工程 · 成本优化 · 研发效能

  9. X · @mirku21视频 · 引用

    Claude Code 分层编排:Opus 顾问、Sonnet 主建、Haiku 蜂群

    基于官方 advisor 工具的三级模型编排配方:Opus 在关键节点审查,Sonnet 主力构建,Haiku 并行蜂群,附可复制配置提示词。

    holy sh*t. Official Anthropic tip for Claude Code: stop burning Opus 5.5 context on tasks Haiku 5.5 can swarm, while Sonnet 5.5 builds
    
    put the architect on call with /advisor
    
    claude --advisor opus --subagents haiku
    
    Sonnet 5.5 drives the primary session on high effort, writing diffs and running test suites
    Haiku 5.5 subagents swarm the repo in parallel at 340 tok/s, handling file discovery and spec docs lookup
    Opus 5.5 stays on call in the background as the advisor, only stepping in at three critical moments:
    
    → before a plan locks: does the implementation plan miss auth invariants or schema contracts?
    → when a test breaks twice: are we fixing the root cause or falling into a recursive rabbit hole?
    → before calling done: did the full diff introduce hidden regressions or break pre-flight rules?
    
    Opus 5.5 reviews. Sonnet 5.5 builds. Haiku 5.5 swarms
    
    Jev engineering runs the identical pattern one layer down: mechanical decisions that need no reasoning (which file to open, which tool to invoke, retry or abort) execute in 16ms, so the frontier models only wake up when execution paths actually diverge
    
    Plan on high. Delegate on medium. Keep Opus on call.
    
    - the full advisor setup
    > Opus 5.5 on call reads full session history and catches deep architectural traps
    > Sonnet 5.5 lead drives edits, writes core logic, and executes test harnesses
    > Haiku 5.5 subagents swarm AST parsing, grep scans, and API docs in parallel
    > JEV micro-fork layer resolves 1,500+ mechanical routing branches in under 16ms
    > advisor stays completely silent on routine bash execution to protect context
    
    paste the setup and this prompt into Claude Code below:
    
    "Configure my Claude Code workspace for hierarchical advisor orchestration:
    
    1. Audit ~/.claude/settings.json and project config for model roles fitting lead, subagents, and advisor:
    > Pin main session model: sonnet with effortLevel: high for primary execution
    > Pin subagents model: haiku with effortLevel: medium for parallel file discovery and docs retrieval
    > Pin advisor model: opus on call for strategic review
    
    2. Enable the advisor tool in ~/.claude/settings.json:
    > Set advisorModel to claude-opus-5-5
    > Enable subagent parallel dispatcher pool (3x workers)
    
    3. Configure automatic advisor consultation checkpoints in ~/.claude/CLAUDE.md:
    > Consult /advisor opus before finalizing multi-file architecture plans
    > Automatically summon /advisor opus when the same test or compiler error fails twice
    > Enforce advisor diff contract audit before declaring tasks complete or staging git commits
    
    4. Constrain subagent scopes:
    > Haiku subagents return structured AST summaries and docs snippets only without modifying main session context
    
    Show every configuration diff first. Do not apply edits until confirmed"
    
    ↳
    https://t.co/gvAWk6KbDn

    提示词 · 软件工程提示词 · Agent 工程 · 多模型编排 · 上下文管理 · 成本优化

  10. X · @mattpocockuk

    三档递进子代理提示词重构 AGENTS.md 省 Token

    分享一条可复用提示词,用三个由温和到激进的子代理重构 AGENTS.md,去除无效指令、渐进披露并拆分编码规范。

    Prompt of the day:
    
    /writing-for-agents my AGENTS.md is a hot mess, propose a series of restructurings that:
    
    - Remove no-ops
    - Use progressive disclosure
    - Move instructions to CODING_STANDARDS.md
    
    Apply your work over three subagents, each more radical than the last. Create a single PR.
    
    AGENTS.md is the most common source of token inefficiency. This prompt kills it dead.

    提示词 · 软件工程提示词 · 上下文工程 · 代码规范 · 渐进披露 · 文档重构 · Agent 工程

  11. X · @mattpocockuk

    用 Coding Agent 复盘 PR 评论,沉淀团队编码规范

    Matt Pocock 分享可复用提示词:让 Agent 扫描团队 GitHub PR 评审评论,归纳更新 CODING_STANDARDS.md。

    Prompt of the day:
    
    /retro take a look at as many GitHub PR review comments as you have access to from my team. Create suggestions for updates to CODING_STANDARDS.md (breaking into multiple files as needed).
    
    Makes your PR comments inform the code going forward.

    提示词 · 软件工程提示词 · 代码规范 · 代码评审 · 研发效能 · Agent 工程

  12. X · @RoundtableSpace

    Claude Code 自治智能体团队工作区配置方案

    用 teams 角色、git worktree 隔离、文件锁与对手评审,把 Claude Code 配成多智能体协作团队。

    Configure my Claude Code workspace for autonomous agent teams:
    
    In ~/.claude/teams, create the missing roles. Workers run on Sonnet (medium effort) in separate git worktree branches. The adversary runs on Fable.
    In ~/.claude/settings.json, set CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 and lock the main session to effortLevel: high.
    Initialize tasks.json with file-level locking so workers can coordinate directly.
    In CLAUDE.md, require an adversary review before any PR.
    
    Show me the config diffs before applying anything.

    提示词 · 多智能体 · Agent 工程 · 工作流配置 · 代码审查 · 研发效能

  13. X · @ErwinWu000引用

    用 /retro 与 /writing-for-agent 治理 Agent 上下文文档

    组合 /retro 与 /writing-for-agent,以渐进式披露清理 AGENT.md,保持 Agent 上下文清爽并节省 token。

    我非常建议大家在每个阶段完成后调用/writing-for-agent和/retro
    
    可能有人从没有管过自己的AGENT.md,里面塞了一大堆中间状态都不知道…这些玩意每轮都会进入AI的上下文,不但耗token还会误导它的工作
    
    或者是文档之间各说各的,有的还没有引用跳转,整个文档系统让AI用得很难受
    
    /retro搭配/writing-for-agent使用可以有奇效!
    
    它们会用渐进式披露原则检查你的README.md和AGENT.md文件,把详细信息用导航的形式放到专用的文档里,把无用的中间过程全部删掉,保持AI的上下文文件清爽

    提示词 · 软件工程提示词 · 上下文管理 · 文档治理 · Agent 工程 · 渐进式披露 · 仓库导航

  14. X · @mattpocockuk

    codebase-design:用删除测试清理代码库冗余抽象

    Matt Pocock 分享的 Agent 技能提示词,通过删除测试找出浅模块与冗余抽象,持续精简代码库。

    mattpocock/skills prompt of the day:
    
    /codebase-design Take a look in the repo for shallow modules, applying the deletion test and look for candidates for deletion.
    
    Helps kill unnecessary abstractions.

    提示词 · 软件工程提示词 · 技能 · 代码库设计 · 删除测试 · 架构优化 · Agent 工程

  15. X · @thedelost视频 · 引用

    Codex 多智能体编排提示词:主模型写码、架构师按需审查

    一条可复制的 Codex 配置提示词:分层模型树加按需唤起的架构师代理,降低编码成本。

    Codex tip: once GPT-6.1 Sol is your main model, stop running Astra on every turn
    
    put Astra on call as an architect agent
    
    GPT-6.1 Sol keeps writing the code
    Astra only gets spawned at three points:
    
    → before a plan: is this the right approach?
    → when the same error comes back: am I digging in the wrong place?
    → before "done": what did I miss?
    
    Astra reviews. Sol ships
    
    Jev engineering is the same move one layer down: the forks that need no thinker (which file, which tool, retry or stop) go to Jev in under half a second, and the big models only see the ones that split
    
    - the full tree
    > GPT-6.1 Sol on high runs the main session
    > explorer reads the code on Luna
    > worker edits and runs tests on Sol
    > researcher pulls the docs on Luna
    > all three on medium
    > Astra on call as the architect
    > auto_review checks every approval
    
    paste the tree and this prompt into Codex ↓
    
    "Rebuild my Codex setup around this tree:
    
    1. Check ~/.codex/agents and .codex/agents for agents that already fit explorer, worker and researcher.
    
    > Draft new TOML files only for missing roles
    > explorer and researcher on gpt-6-luna, worker on gpt-6.1-sol, all with model_reasoning_effort medium
    > Add an architect agent on gpt-6-astra, model_reasoning_effort high, whose only job is reviewing plans, repeated errors and finished work
    > Skip any that pin a different model and list them
    
    2. In ~/.codex/config.toml set model to gpt-6.1-sol, model_reasoning_effort to high and approvals_reviewer to auto_review
    
    3. Find anything that would override this (active profiles, flags in my shell aliases, agents.default_subagent_model). Report it, change nothing
    
    4. Add one rule to AGENTS.md: spawn the architect before a large plan, when an error repeats, and before calling a long task done
    
    Show me every change as a diff first. No edits until I say go."
    
    ↳ https://t.co/eyMF8gZKLE

    提示词 · 软件工程提示词 · Agent 工程 · 研发效能

  16. X · @yifanxu_ephai

    给编码 Agent 立规:算法优化优先用 SQLite 别手搓

    作者分享一条 Agent 规则:复杂度优化时优先复用 SQLite 的索引与缓存,而非让模型手搓数据结构。

    最近加了一个rule告诉agent:
    
    O(n) -> O(nlogn) 或是near O(1)的优化,能用sqlite就不要自己手搓。
    
    SQLite 有现成的 B+ 树、index、page,还有缓存,不香吗?
    
    自己临时手搓的数据结构和算法不仅性能做不到sqlite那么快,还老是cache和ram遇到大瓶颈🤣

    提示词 · 软件工程提示词 · Agent 工程 · 架构与基础设施

  17. X · @Voxyz_ai图片

    让编码 Agent 先复述意图再动手的权衡提示词

    一条可复制的提示词:让 Claude Code/Codex 先确认理解、按 UX/DX/AX 权衡决策、不越界不破坏现有功能。

    When you hand Claude Code or Codex a long task, add this. If it misunderstood you, 𝘆𝗼𝘂'𝗹𝗹 𝗰𝗮𝘁𝗰𝗵 𝗶𝘁 𝗿𝗶𝗴𝗵𝘁 𝗮𝘄𝗮𝘆:
    
    "Before you touch anything, tell me in 2–3 sentences what you think I'm after and what problem we're solving. Start only after I say yes.
    
    When there's a tradeoff, weigh all three:
    → UX: is it easy for users to use?
    → DX: is it easy for developers to change later?
    → AX: can the next agent understand it and keep going?
    
    Don't touch anything outside this task, and don't break anything that already works. When you're done, tell me in 2–3 lines what you picked, what you gave up, and why."
    让编码 Agent 先复述意图再动手的权衡提示词 的效果图

    提示词 · 软件工程提示词 · 任务对齐 · 边界控制 · Agent 工程

  18. X · @realchendahuang

    一句话提示词:Agent 零成本搭建并部署个人网站

    分享可复制的 Agent 提示词:自动装 Cloudflare CLI、生成极简响应式主页并一键部署,零成本几分钟上线。

    谁不想拥有一个自己的个人网站呢?现在搭网站的门槛已经低到了极限,一句话真能直接干完。
    
    注册一个 Cloudflare 免费账号。打开手头任何能干活的 Agent,直接把下面这段话甩给它:
    
    请帮我从零搭建一个属于我的个人网站并直接发布上线。先检查系统里有没有装 Cloudflare 刚出的 cf 命令行工具,没有就自动运行 npm i -g cf 安装好;然后帮我设计一个极简高级的个人主页,包含自我介绍、作品展示和联系方式,自适应手机端与暗黑模式;最后在本地打包并通过 cf deploy 自动部署到 Cloudflare,把生成的线上访问网址发给我。
    
    发过去之后,AI 会在后台自己把工具装好、把页面写好并打包。中间终端会弹出一个网页,顺手点一下授权登录刚才注册的账号。
    
    授权一过,AI 就会顺着命令把网站推上全球边缘网络。全程不要一分钱,几分钟就能拿到一个全世界都能秒开的专属主页。
    
    Cloudflare 官网注册入口:
    https://t.co/5BZcgDsgWK

    提示词 · 软件工程提示词 · 个人网站 · 边缘部署 · 零成本部署 · Agent 工程

  19. X · @smk7z

    Agent 加持:5 分钟搭建个人 VLESS+Reality 节点

    用自然语言 Prompt 驱动 Agent 操作 sing-box 一键脚本,把个人 VPN 节点搭建从半天压缩到 5 分钟。

    现在搭建个人 VPN 节点,真的只要 5 分钟了
    
    1. 购买一个 VPS 服务器,CN2 GIA最好。拿到 ssh 的账号密码
    
    2. 发给 Agent这段 Prompt:
    “ssh  root@xx.xx.xx.xx,密码是XXXXX,根据https://t.co/hhzxnktyUE 给这台VPS 服务器安装 Vless + reality,然后把可导入 V2rayN 的 分享地址发给我”
    为了安全,安装后建议换 ssh 密码
    
    3. 在电脑上安装 V2rayN
    https://t.co/i5CgBn4VIa
    
    4. 在 V2rayN 界面把第二步的链接粘贴进去,结束了,开始用你的独享节点
    
    感慨,还记得2020 年跟着不良林老师,用 vultr + x-ui +vmess 一步步搭建,搞了半天才搞定😭

    提示词 · 助手提示词 · Agent 工程 · VPN 搭建 · VPS · 网络安全 · 实操教程

  20. X · @xiaohu视频

    用 /checkup prompt-audit 体检修复 Claude Code 配置

    一条命令审计 CLAUDE.md、技能与 Agent 配置,清理失效指令,释放模型真实能力。

    使用这个命令
    
    可以修复和整理你的Claude.md和技能
    
    /checkup prompt-audit
    
    它会检查你的 CLAUDE.md、技能和Agent,找出你的当前模型不再需要的指令和阻碍它发挥能力的提示内容,还会修复你的技能...
     https://t.co/wo0ywbyh72

    提示词 · 技能 · Agent 工程 · 研发效能 · 提示词清理