Agent Builder
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
当系统提示词需要为 AI 输出定义格式规范、长度约束、风格指南或反"AI味"策略时调用此 Skill。适用于聊天机器人、CLI 工具、移动端助手、设计生成器等需要自适应输出的场景。不适用于：纯内容生成（无格式要求）、内部推理链设计、安全策略制定。当需求仅涉及"用什么格式返回数据"而非"如何控制输出的风格与密度"时，这不是最佳 Skill。
Install Output Formatting
$ npx skills add kangarooking/system-prompt-skills --skill output-formatting -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install kangarooking/system-prompt-skills output-formatting --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/output-formatting .claude/skills/output-formatting && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "output-formatting" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/output-formatting into .claude/skills/output-formatting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "output-formatting", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/kangarooking/system-prompt-skills/tree/main/output-formattingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add kangarooking/system-prompt-skills --skill output-formatting -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install kangarooking/system-prompt-skills output-formatting --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/output-formatting .agents/skills/output-formatting && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "output-formatting" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/output-formatting into .agents/skills/output-formatting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "output-formatting", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add kangarooking/system-prompt-skills --skill output-formatting -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install kangarooking/system-prompt-skills output-formatting --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/output-formatting .cursor/skills/output-formatting && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "output-formatting" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/output-formatting into .cursor/skills/output-formatting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "output-formatting", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/kangarooking/system-prompt-skills.git --path output-formatting--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add kangarooking/system-prompt-skills --skill output-formatting -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install kangarooking/system-prompt-skills output-formatting --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/output-formatting .gemini/skills/output-formatting && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "output-formatting" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/output-formatting into .gemini/skills/output-formatting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "output-formatting", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install kangarooking/system-prompt-skills output-formattingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add kangarooking/system-prompt-skills --skill output-formatting -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/output-formatting .github/skills/output-formatting && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "output-formatting" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/output-formatting into .github/skills/output-formatting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "output-formatting", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add kangarooking/system-prompt-skills --skill output-formatting -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install kangarooking/system-prompt-skills output-formatting --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/output-formatting .opencode/skills/output-formatting && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "output-formatting" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/output-formatting into .opencode/skills/output-formatting/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "output-formatting", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
output-formatting- GitHub stars
- 205
- Token cost
- ~694 tokens
- SKILL.md length
- 216 words
- Files
- 1
- Skills in repo
- 15
- Repo updated
- First seen
- Licence
- MIT
At a glance
当系统提示词需要为 AI 输出定义格式规范、长度约束、风格指南或反"AI味"策略时调用此 Skill。适用于聊天机器人、CLI 工具、移动端助手、设计生成器等需要自适应输出的场景。不适用于：纯内容生成（无格式要求）、内部推理链设计、安全策略制定。当需求仅涉及"用什么格式返回数据"而非"如何控制输出的风格与密度"时，这不是最佳 Skill。
- Works in 7 steps: 复杂度分级 — 按问题深度映射输出长度档位（一句话 / 短列表 / 两三段 /… → 平台感知 — 检测目标载体（CLI / 移动端 / 桌面 /… → 前置答案原则 — 结论先行，解释后置；"Always lead with the… → …
- AI & LLM Engineering work in your project
- SKILL.md covers R — 原文 (Reading), I — 方法论骨架 (Interpretation), A1 — 案例分析 (Past Application) and A2 — 触发场景 (Future Trigger) ★, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Output Formatting is an agent skill from kangarooking/system-prompt-skills. 当系统提示词需要为 AI 输出定义格式规范、长度约束、风格指南或反"AI味"策略时调用此 Skill。适用于聊天机器人、CLI 工具、移动端助手、设计生成器等需要自适应输出的场景。不适用于：纯内容生成（无格式要求）、内部推理链设计、安全策略制定。当需求仅涉及"用什么格式返回数据"而非"如何控制输出的风格与密度"时，这不是最佳 Skill。
Its SKILL.md is about 690 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering. The repository describes itself as: 从 165 个顶级 AI 产品系统提示词中蒸馏出的 15 个可执行 Agent skill. The licence is MIT.
When your agent uses it
- AI & LLM Engineering work in your project
Example prompts
- “用什么格式返回数据”
- “如何控制输出的风格与密度”
- “/output-formatting”
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- 复杂度分级 — 按问题深度映射输出长度档位（一句话 / 短列表 / 两三段 / 完整文档）
- 平台感知 — 检测目标载体（CLI / 移动端 / 桌面 / API），应用对应格式约束
- 前置答案原则 — 结论先行，解释后置；"Always lead with the answer"
- 反 AI slop 规则集 — 禁止套话开头、无意义表情符号、过度格式化、破折号滥用
- 领域标记语言 — 为特定平台定制标记（Notion 压缩 URL {{1}}、GPT-4o image_group JSON）
- 格式降级策略 — 默认散文体，仅在必要时使用列表/表格，绝不"为了好看而格式化"
- 引用密度控制 — 代码引用使用 file_path:line_number 格式，摘要限制在 1-2 句
What it can do on your machine
Read from SKILL.md and the folder at commit 252cd52. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Output Formatting loads about 694 tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 216 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from kangarooking/system-prompt-skills at commit 252cd52, republished under its MIT licence (© kangarooking). 216 words, ~694 tokens.
Download SKILL.mdSave it as
.claude/skills/output-formatting/SKILL.md (or your agent's skills folder).- name
- output-formatting
- description
- 当系统提示词需要为 AI 输出定义格式规范、长度约束、风格指南或反"AI味"策略时调用此 Skill。适用于聊天机器人、CLI 工具、移动端助手、设计生成器等需要自适应输出的场景。不适用于：纯内容生成（无格式要求）、内部推理链设计、安全策略制定。当需求仅涉及"用什么格式返回数据"而非"如何控制输出的风格与密度"时，这不是最佳 Skill。
- tags
- 输出格式, 风格控制, 反AI味, 自适应长度, 平台适配
- related_skills
- conversation-flow, context-management
输出格式与风格控制
R — 原文 (Reading)
多个 AI 供应商系统提示词中共同浮现的输出控制模式：按复杂度自适应长度、按平台定制格式、用反"AI slop"启发式规则消除机械化表达。Claude Mobile 按屏幕尺寸分四档长度；Gemini CLI 限制三行以内；Meta AI 禁用"Here's a..."等套话；Le Chat 用表格替代列表；Claude Design 禁用圆角容器和 Inter/Roboto 字体。
I — 方法论骨架 (Interpretation)
- 复杂度分级 — 按问题深度映射输出长度档位（一句话 / 短列表 / 两三段 / 完整文档）
- 平台感知 — 检测目标载体（CLI / 移动端 / 桌面 / API），应用对应格式约束
- 前置答案原则 — 结论先行，解释后置；"Always lead with the answer"
- 反 AI slop 规则集 — 禁止套话开头、无意义表情符号、过度格式化、破折号滥用
- 领域标记语言 — 为特定平台定制标记（Notion 压缩 URL
{{1}}、GPT-4o image_group JSON） - 格式降级策略 — 默认散文体，仅在必要时使用列表/表格，绝不"为了好看而格式化"
- 引用密度控制 — 代码引用使用
file_path:line_number格式，摘要限制在 1-2 句
A1 — 案例分析 (Past Application)
案例: Claude Mobile 屏幕尺寸自适应输出
- 问题: 移动端屏幕空间有限，长回复导致用户需要大量滚动，体验差
- 设计模式的使用: Claude Mobile 系统提示词定义了四级长度策略：简单问题 → 1-2 句话；操作指南 → 短列表；实质性问题 → 2-3 段；复杂主题 → 不超过两屏。同时强制"答案先行"原则
- 结论: 该策略在有限屏幕空间内最大化信息密度，用户可快速获取核心答案，需要时再向下滚动查看细节
案例: Meta AI 反套话策略
- 问题: AI 回复普遍带有"Here's a..."、"Great question!"等机械化开场白，用户反感
- 设计模式的使用: Meta AI 系统提示词明确禁止此类套话，要求使用扁平列表、表格呈现结构化数据，禁止破折号，保持直接风格
- 结论: 消除 AI slop 后用户满意度显著提升，说明格式控制不仅是美学问题，更是体验核心
A2 — 触发场景 (Future Trigger) ★
用户在什么情境下需要?
- 设计聊天机器人系统提示词时，需要控制回复长度和格式
- 为 CLI 工具定义输出规范，要求简洁无废话
- 为移动端 AI 助手适配屏幕尺寸约束
- 设计品牌专属的 AI 输出风格指南（如设计工具中禁止特定字体和视觉元素）
- 构建多平台 AI 产品，需要为不同载体定义差异化输出策略
语言信号
- "回复太长了/太啰嗦了"
- "输出格式需要适配手机屏幕"
- "不要那些 AI 常用的套话"
- "简洁一点，像终端工具那样输出"
- "品牌风格指南要求避免..."
与相邻 skill 的区分
- 与
conversation-flow的区别: 本 Skill 聚焦输出的"形式"（长度/格式/风格），conversation-flow 聚焦交互的"流程"（路由/澄清/自主度） - 与
context-management的区别: context-management 管理输入侧的 token 预算，本 Skill 管理输出侧的格式密度
E — 可执行步骤 (Execution)
定义复杂度分级表 — 完成标准: 建立至少三档长度映射（简单/中等/复杂），每档有明确的句子数或行数上限，并与目标平台视口尺寸挂钩
编写反 AI slop 黑名单 — 完成标准: 列出至少 10 条禁止项（套话开场白、无意义表情符号、过度格式化、破折号滥用、Inter/Roboto 字体、圆角容器等），每条附带替代方案
设计平台格式规范 — 完成标准: 为每个目标平台（CLI/移动端/桌面/API）定义默认格式模式（如 CLI 默认三行散文体、移动端默认答案先行+短列表），包含领域专用标记语法说明
建立格式降级规则 — 完成标准: 明确"默认散文体 → 必要时列表 → 复杂时表格 → 极少时代码块"的升级路径，以及每级的使用触发条件
添加输出自检钩子 — 完成标准: 在系统提示词末尾加入输出自检指令："生成后检查：是否套话开头？是否过度格式化？答案是否在前两句内出现？"
B — 边界 (Boundary) ★
不要在以下情况使用
- 用户明确要求某种特定格式（如 JSON Schema、XML）— 这属于数据契约而非风格控制
- 输出对象是机器而非人类（API 返回值、结构化数据）
- 场景是创意写作或文学创作，格式自由度是需求的一部分
- 仅需控制内容安全性（如敏感词过滤），不涉及格式和风格
常见失败模式
- 过度约束导致回复僵化：把"简洁"理解为"只回答是/否"，丢失关键上下文
- 反 AI slop 走向极端：禁止所有表情符号和格式标记，导致技术文档可读性下降
- 忽视平台差异：在桌面端适用的回复长度直接搬到移动端，不做适配
- 混淆输入压缩与输出控制：试图通过限制输出来解决 token 预算问题，应该用 context-management 而非本 Skill
© kangarooking, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in output-formatting of kangarooking/system-prompt-skills.
Open the folder on GitHubat commit 252cd52
Compare with similar skills
Output Formatting next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Output Formatting this skillkangarooking/system-prompt-skills | 205 | — | ~694 | Automated safety check: Pass | MIT | |
| Agent BuildershareAI-lab/learn-claude-code | 78k | 6 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| Peft Fine TuningOrchestra-Research/AI-Research-SKILLs | 13k | 9 repos | ~3.1k | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 9 repos | ~3.3k | Automated safety check: Pass | MIT | |
| 1passwordtrpc-group/trpc-agent-go | 1.8k | 15 repos | ~656 | Automated safety check: Pass | Apache-2.0 |
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Personality System
kangarooking/system-prompt-skills
当需要在基础身份之上叠加可切换的人格风格层时调用此 skill。典型场景包括：为同一产品提供多种人格选项（如 GPT-5.1 的 friendly/professional/quirky 模式）、设计人格切换机制、防止人格泄露到用户内容中。
Conversation Flow
kangarooking/system-prompt-skills
当系统提示词需要定义 AI 如何分类用户意图、路由到不同处理流程、决定澄清策略和自主度级别时调用此 Skill。适用于多任务型 AI 助手、客服机器人、编程工具、研究助手等需要结构化对话管理的场景。不适用于：纯问答型系统（无任务执行）、单轮交互（无对话状态）、简单的 prompt 模板（无路由逻辑）。当需求仅涉及"输出什么格式"而非"如何决定输出什么"时，应该用…
Safety Guardrails
kangarooking/system-prompt-skills
当需要为 AI 系统设计多层安全防线、内容过滤策略和伦理边界时调用此 skill。典型场景包括：设计拒绝策略与升级机制、防御 prompt 注入攻击、实现领域特定安全规则（教育、医疗、金融等）、定义 AI 的价值观锚点。
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Questions about Output Formatting
What does Output Formatting do?
当系统提示词需要为 AI 输出定义格式规范、长度约束、风格指南或反"AI味"策略时调用此 Skill。适用于聊天机器人、CLI 工具、移动端助手、设计生成器等需要自适应输出的场景。不适用于：纯内容生成（无格式要求）、内部推理链设计、安全策略制定。当需求仅涉及"用什么格式返回数据"而非"如何控制输出的风格与密度"时，这不是最佳 Skill。. Output Formatting is an agent skill from kangarooking/system-prompt-skills.
When should I use Output Formatting?
Output Formatting fits situations like: AI & LLM Engineering work in your project.
How do I install Output Formatting in Claude Code?
Run `npx skills add kangarooking/system-prompt-skills --skill output-formatting -a claude-code`. Or copy the skill folder (output-formatting in kangarooking/system-prompt-skills) into .claude/skills/output-formatting in your project. Claude Code loads it when a task matches its description.
How do I install Output Formatting in Codex?
Run `npx skills add kangarooking/system-prompt-skills --skill output-formatting -a codex`. Or copy the skill folder (output-formatting in kangarooking/system-prompt-skills) into .agents/skills/output-formatting in your project. Codex loads it when a task matches its description.
Can I use Output Formatting in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add kangarooking/system-prompt-skills --skill output-formatting -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/output-formatting, .gemini/skills/output-formatting, .github/skills/output-formatting and .opencode/skills/output-formatting in your project.
What does Output Formatting need to run?
SKILL.md names no scripts, command-line tools or credentials: Output Formatting is instructions for the agent only.
Does Output Formatting access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Output Formatting safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Output Formatting use?
Output Formatting is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Output Formatting use?
About 694 tokens (SKILL.md is roughly 2.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Output Formatting?
Skills that share tags, products or a category with Output Formatting: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), Peft Fine Tuning (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Output Formatting?
kangarooking (a GitHub user) maintains it in kangarooking/system-prompt-skills, which has 205 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on May 4, 2026.
Source: kangarooking/system-prompt-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.