LLM Torch Profiler Analysis
sgl-project/sglang
Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed.
分析原始提示，识别意图和差距，匹配ECC组件（技能/命令/代理/钩子），并输出一个可直接粘贴的优化提示。仅提供咨询角色——绝不自行执行任务。触发时机：当用户说“优化提示”、“改进我的提示”、“如何编写提示”、“帮我优化这个指令”或明确要求提高提示质量时。中文等效表达同样触发：“优化prompt”、“改进prompt”、“怎么写prompt”、“帮我优化这个指令”。不触发时机：当用户希望直接执行任…
Install Prompt Optimizer
$ npx skills add affaan-m/ECC --skill prompt-optimizer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install affaan-m/ECC prompt-optimizer --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/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/docs/zh-CN/skills/prompt-optimizer .claude/skills/prompt-optimizer && 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 "prompt-optimizer" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/zh-CN/skills/prompt-optimizer into .claude/skills/prompt-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-optimizer", 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/affaan-m/ECC/tree/main/docs/zh-CN/skills/prompt-optimizerType 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 affaan-m/ECC --skill prompt-optimizer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install affaan-m/ECC prompt-optimizer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .agents/skills && cp -r skills-src/docs/zh-CN/skills/prompt-optimizer .agents/skills/prompt-optimizer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "prompt-optimizer" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/zh-CN/skills/prompt-optimizer into .agents/skills/prompt-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-optimizer", 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 affaan-m/ECC --skill prompt-optimizer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install affaan-m/ECC prompt-optimizer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/docs/zh-CN/skills/prompt-optimizer .cursor/skills/prompt-optimizer && 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 "prompt-optimizer" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/zh-CN/skills/prompt-optimizer into .cursor/skills/prompt-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-optimizer", 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/affaan-m/ECC.git --path docs/zh-CN/skills/prompt-optimizer--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 affaan-m/ECC --skill prompt-optimizer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install affaan-m/ECC prompt-optimizer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/docs/zh-CN/skills/prompt-optimizer .gemini/skills/prompt-optimizer && 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 "prompt-optimizer" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/zh-CN/skills/prompt-optimizer into .gemini/skills/prompt-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-optimizer", 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 affaan-m/ECC prompt-optimizerInstalls 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 affaan-m/ECC --skill prompt-optimizer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .github/skills && cp -r skills-src/docs/zh-CN/skills/prompt-optimizer .github/skills/prompt-optimizer && 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 "prompt-optimizer" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/zh-CN/skills/prompt-optimizer into .github/skills/prompt-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-optimizer", 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 affaan-m/ECC --skill prompt-optimizer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install affaan-m/ECC prompt-optimizer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/docs/zh-CN/skills/prompt-optimizer .opencode/skills/prompt-optimizer && 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 "prompt-optimizer" agent skill from https://github.com/affaan-m/ECC/tree/main/docs/zh-CN/skills/prompt-optimizer into .opencode/skills/prompt-optimizer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-optimizer", 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
prompt-optimizer- GitHub stars
- 274k
- Used in
- 2 other repos
- Token cost
- ~2.4k tokens
- SKILL.md length
- 677 words
- Files
- 1
- Skills in repo
- 657
- Repo updated
- First seen
- Licence
- MIT
At a glance
分析原始提示，识别意图和差距，匹配ECC组件（技能/命令/代理/钩子），并输出一个可直接粘贴的优化提示。仅提供咨询角色——绝不自行执行任务。触发时机：当用户说“优化提示”、“改进我的提示”、“如何编写提示”、“帮我优化这个指令”或明确要求提高提示质量时。中文等效表达同样触发：“优化prompt”、“改进prompt”、“怎么写prompt”、“帮我优化这个指令”。不触发时机：当用户希望直接执行任…
- Works in 3 steps: 检查工作目录中是否存在 CLAUDE.md——读取它以了解项目惯例 → 从项目文件中检测技术栈 → 记录检测到的技术栈，用于阶段 3 和阶段 4
- Tasks that involve Prompt engineering
- SKILL.md covers 何时使用, 工作原理, 输出格式 and 示例, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Prompt Optimizer is an agent skill from affaan-m/ECC. 分析原始提示，识别意图和差距，匹配ECC组件（技能/命令/代理/钩子），并输出一个可直接粘贴的优化提示。仅提供咨询角色——绝不自行执行任务。触发时机：当用户说“优化提示”、“改进我的提示”、“如何编写提示”、“帮我优化这个指令”或明确要求提高提示质量时。中文等效表达同样触发：“优化prompt”、“改进prompt”、“怎么写prompt”、“帮我优化这个指令”。不触发时机：当用户希望直接执行任务，或说“直接做”时。不触发时机：当用户说“优化代码”、“优化性能”、“optimize performance”、“optimize this code”时——这些是重构/性能优化任务，而非提示优化。
Its SKILL.md is about 2.4k 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 Development, covering Prompt engineering and Performance optimization. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.
When your agent uses it
- Tasks that involve Prompt engineering
- Tasks that involve Performance optimization
Example prompts
- “改进我的提示”
- “如何编写提示”
- “帮我优化这个指令”
- “/prompt-optimizer”
Requirements
- Python 3
- Node.js
- Docker
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- 检查工作目录中是否存在 CLAUDE.md——读取它以了解项目惯例
- 从项目文件中检测技术栈
- 记录检测到的技术栈，用于阶段 3 和阶段 4
What it can do on your machine
Read from SKILL.md and the folder at commit ef648e0. 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
Prompt Optimizer loads about 2.4k tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 677 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 affaan-m/ECC at commit ef648e0, republished under its MIT licence (© affaan-m). 677 words, ~2,433 tokens.
Download SKILL.mdSave it as
.claude/skills/prompt-optimizer/SKILL.md (or your agent's skills folder).- name
- prompt-optimizer
- description
- 分析原始提示，识别意图和差距，匹配ECC组件（技能/命令/代理/钩子），并输出一个可直接粘贴的优化提示。仅提供咨询角色——绝不自行执行任务。触发时机：当用户说“优化提示”、“改进我的提示”、“如何编写提示”、“帮我优化这个指令”或明确要求提高提示质量时。中文等效表达同样触发：“优化prompt”、“改进prompt”、“怎么写prompt”、“帮我优化这个指令”。不触发时机：当用户希望直接执行任务，或说“直接做”时。不触发时机：当用户说“优化代码”、“优化性能”、“optimize performance”、“optimize this code”时——这些是重构/性能优化任务，而非提示优化。
- origin
- community
- metadata.author
- YannJY02
- metadata.version
- 1.0.0
Prompt 优化器
分析一个草稿提示，对其进行评估，匹配到 ECC 生态系统组件，并输出一个完整的优化提示供用户复制粘贴并运行。
何时使用
- 用户说“优化这个提示”、“改进我的提示”、“重写这个提示”
- 用户说“帮我写一个更好的提示来...”
- 用户说“询问 Claude Code 的...最佳方式是什么？”
- 用户说“优化prompt”、“改进prompt”、“怎么写prompt”、“帮我优化这个指令”
- 用户粘贴一个草稿提示并要求反馈或改进
- 用户说“我不知道如何为此编写提示”
- 用户说“我应该如何使用 ECC 来...”
- 用户明确调用
/prompt-optimize
不要用于
- 用户希望直接执行任务（直接执行即可）
- 用户说“优化代码”、“优化性能”、“optimize this code”、“optimize performance”——这些是重构任务，不是提示优化
- 用户询问 ECC 配置（改用
configure-ecc） - 用户想要技能清单（改用
skill-stocktake） - 用户说“直接做”或“just do it”
工作原理
仅提供建议——不要执行用户的任务。
不要编写代码、创建文件、运行命令或采取任何实现行动。你的唯一输出是分析加上一个优化后的提示。
如果用户说“直接做”、“just do it”或“不要优化，直接执行”，不要在此技能内切换到实现模式。告诉用户此技能只生成优化提示，并指示他们如果要执行任务，请提出正常的任务请求。
按顺序运行这个 6 阶段流程。使用下面的输出格式呈现结果。
分析流程
阶段 0：项目检测
在分析提示之前，检测当前项目上下文：
- 检查工作目录中是否存在
CLAUDE.md——读取它以了解项目惯例 - 从项目文件中检测技术栈：
package.json→ Node.js / TypeScript / React / Next.jsgo.mod→ Gopyproject.toml/requirements.txt→ PythonCargo.toml→ Rustbuild.gradle/pom.xml→ Java / Kotlin（然后检查构建文件中的quarkus→ Quarkus，或spring-boot→ Spring Boot）Package.swift→ SwiftGemfile→ Rubycomposer.json→ PHP*.csproj/*.sln→ .NETMakefile/CMakeLists.txt→ C / C++cpanfile/Makefile.PL→ Perl
- 记录检测到的技术栈，用于阶段 3 和阶段 4
如果未找到项目文件（例如，提示是抽象的或用于新项目），则跳过检测并在阶段 4 标记“技术栈未知”。
阶段 1：意图检测
将用户的任务分类为一个或多个类别：
| 类别 | 信号词 | 示例 |
|---|---|---|
| 新功能 | build, create, add, implement, 创建, 实现, 添加 | "Build a login page" |
| 错误修复 | fix, broken, not working, error, 修复, 报错 | "Fix the auth flow" |
| 重构 | refactor, clean up, restructure, 重构, 整理 | "Refactor the API layer" |
| 研究 | how to, what is, explore, investigate, 怎么, 如何 | "How to add SSO" |
| 测试 | test, coverage, verify, 测试, 覆盖率 | "Add tests for the cart" |
| 审查 | review, audit, check, 审查, 检查 | "Review my PR" |
| 文档 | document, update docs, 文档 | "Update the API docs" |
| 基础设施 | deploy, CI, docker, database, 部署, 数据库 | "Set up CI/CD pipeline" |
| 设计 | design, architecture, plan, 设计, 架构 | "Design the data model" |
阶段 2：范围评估
如果阶段 0 检测到项目，则使用代码库大小作为信号。否则，仅根据提示描述进行估算，并将估算标记为不确定。
| 范围 | 启发式判断 | 编排 |
|---|---|---|
| 微小 | 单个文件，< 50 行 | 直接执行 |
| 低 | 单个组件或模块 | 单个命令或技能 |
| 中 | 多个组件，同一领域 | 命令链 + /verify |
| 高 | 跨领域，5+ 个文件 | 先使用 /plan，然后分阶段执行 |
| 史诗级 | 多会话，多 PR，架构性变更 | 使用蓝图技能制定多会话计划 |
阶段 3：ECC 组件匹配
将意图 + 范围 + 技术栈（来自阶段 0）映射到特定的 ECC 组件。
按意图类型
| 意图 | 命令 | 技能 | 代理 |
|---|---|---|---|
| 新功能 | /plan, /tdd, /code-review, /verify | tdd-workflow, verification-loop | planner, tdd-guide, code-reviewer |
| 错误修复 | /tdd, /build-fix, /verify | tdd-workflow | tdd-guide, build-error-resolver |
| 重构 | /refactor-clean, /code-review, /verify | verification-loop | refactor-cleaner, code-reviewer |
| 研究 | /plan | search-first, iterative-retrieval | — |
| 测试 | /tdd, /e2e, /test-coverage | tdd-workflow, e2e-testing | tdd-guide, e2e-runner |
| 审查 | /code-review | security-review | code-reviewer, security-reviewer |
| 文档 | /update-docs, /update-codemaps | — | doc-updater |
| 基础设施 | /plan, /verify | docker-patterns, deployment-patterns, database-migrations | architect |
| 设计 (中-高) | /plan | — | planner, architect |
| 设计 (史诗级) | — | blueprint (作为技能调用) | planner, architect |
按技术栈
| 技术栈 | 要添加的技能 | 代理 |
|---|---|---|
| Python / Django | django-patterns, django-tdd, django-security, django-verification, python-patterns, python-testing | python-reviewer |
| Go | golang-patterns, golang-testing | go-reviewer, go-build-resolver |
| Spring Boot / Java | springboot-patterns, springboot-tdd, springboot-security, springboot-verification, java-coding-standards, jpa-patterns | java-reviewer |
| Quarkus / Java | quarkus-patterns, quarkus-tdd, quarkus-security, quarkus-verification, java-coding-standards, jpa-patterns | java-reviewer |
| Kotlin / Android | kotlin-coroutines-flows, compose-multiplatform-patterns, android-clean-architecture | kotlin-reviewer |
| TypeScript / React | frontend-patterns, backend-patterns, coding-standards | code-reviewer |
| Swift / iOS | swiftui-patterns, swift-concurrency-6-2, swift-actor-persistence, swift-protocol-di-testing | code-reviewer |
| PostgreSQL | postgres-patterns, database-migrations | database-reviewer |
| Perl | perl-patterns, perl-testing, perl-security | code-reviewer |
| C++ | cpp-coding-standards, cpp-testing | code-reviewer |
| 其他 / 未列出 | coding-standards (通用) | code-reviewer |
阶段 4：缺失上下文检测
扫描提示中缺失的关键信息。检查每个项目，并标记是阶段 0 自动检测到的还是用户必须提供的：
- [ ] 技术栈 —— 阶段 0 检测到的，还是用户必须指定？
- [ ] 目标范围 —— 提到了文件、目录或模块吗？
- [ ] 验收标准 —— 如何知道任务已完成？
- [ ] 错误处理 —— 是否考虑了边界情况和故障模式？
- [ ] 安全要求 —— 身份验证、输入验证、密钥？
- [ ] 测试期望 —— 单元测试、集成测试、E2E？
- [ ] 性能约束 —— 负载、延迟、资源限制？
- [ ] UI/UX 要求 —— 设计规范、响应式、无障碍访问？（如果是前端）
- [ ] 数据库变更 —— 模式、迁移、索引？（如果是数据层）
- [ ] 现有模式 —— 要遵循的参考文件或惯例？
- [ ] 范围边界 —— 什么不要做？
如果缺少 3 个以上关键项目，则在生成优化提示之前询问用户最多 3 个澄清问题。然后将答案纳入优化提示中。
Show full SKILL.md (262 more words)Show less
阶段 5：工作流和模型推荐
确定此提示在开发生命周期中的位置：
Research → Plan → Implement (TDD) → Review → Verify → Commit对于中等级别及以上的任务，始终以 /plan 开始。对于史诗级任务，使用蓝图技能。
模型推荐（包含在输出中）：
| 范围 | 推荐模型 | 理由 |
|---|---|---|
| 微小-低 | Sonnet 5 | 快速、成本效益高，适合简单任务 |
| 中 | Sonnet 5 | 标准工作的最佳编码模型 |
| 高 | Sonnet 5 (主) + Opus 5 (规划) | Opus 用于架构，Sonnet 用于实现 |
| 史诗级 | Opus 5 (蓝图) + Sonnet 5 (执行) | 深度推理用于多会话规划 |
多提示拆分（针对高/史诗级范围）：
对于超出单个会话的任务，拆分为顺序提示：
- 提示 1：研究 + 计划（使用 search-first 技能，然后 /plan）
- 提示 2-N：每个提示实现一个阶段（每个阶段以 /verify 结束）
- 最终提示：集成测试 + 跨所有阶段的 /code-review
- 使用 /save-session 和 /resume-session 在会话之间保存上下文
输出格式
按照此确切结构呈现你的分析。使用与用户输入相同的语言进行回应。
第 1 部分：提示诊断
优点： 列出原始提示做得好的地方。
问题：
| 问题 | 影响 | 建议的修复方法 |
|---|---|---|
| (问题) | (后果) | (如何修复) |
需要澄清： 用户应回答的问题编号列表。如果阶段 0 自动检测到答案，请陈述该答案而不是提问。
第 2 部分：推荐的 ECC 组件
| 类型 | 组件 | 目的 |
|---|---|---|
| 命令 | /plan | 编码前规划架构 |
| 技能 | tdd-workflow | TDD 方法指导 |
| 代理 | code-reviewer | 实施后审查 |
| 模型 | Sonnet 5 | 针对此范围的推荐模型 |
第 3 部分：优化提示 —— 完整版本
在单个围栏代码块内呈现完整的优化提示。该提示必须是自包含的，可以复制粘贴。包括：
- 清晰的任务描述和上下文
- 技术栈（检测到的或指定的）
- 在正确工作流阶段调用的 /command
- 验收标准
- 验证步骤
- 范围边界（什么不要做）
对于引用蓝图的项目，写成：“使用蓝图技能来...”（而不是 /blueprint，因为蓝图是技能，不是命令）。
第 4 部分：优化提示 —— 快速版本
为有经验的 ECC 用户提供的紧凑版本。根据意图类型而变化：
| 意图 | 快速模式 |
|---|---|
| 新功能 | /plan [feature]. /tdd to implement. /code-review. /verify. |
| 错误修复 | /tdd — write failing test for [bug]. Fix to green. /verify. |
| 重构 | /refactor-clean [scope]. /code-review. /verify. |
| 研究 | Use search-first skill for [topic]. /plan based on findings. |
| 测试 | /tdd [module]. /e2e for critical flows. /test-coverage. |
| 审查 | /code-review. Then use security-reviewer agent. |
| 文档 | /update-docs. /update-codemaps. |
| 史诗级 | Use blueprint skill for "[objective]". Execute phases with /verify gates. |
第 5 部分：改进理由
| 改进 | 理由 |
|---|---|
| (添加了什么) | (为什么重要) |
页脚
不符合你的需求？告诉我需要调整什么，或者如果你想执行任务而不是优化提示，请提出正常的任务请求。
示例
触发示例
- "Optimize this prompt for ECC"
- "Rewrite this prompt so Claude Code uses the right commands"
- "帮我优化这个指令"
- "How should I prompt ECC for this task?"
示例 1：模糊的中文提示（检测到项目）
用户输入：
帮我写一个用户登录页面阶段 0 检测到： package.json，使用 Next.js 15, TypeScript, Tailwind CSS
优化提示（完整）：
使用项目现有技术栈（Next.js 15 + TypeScript + Tailwind CSS）实现用户登录页面。
技术要求：
- 沿用项目现有的组件结构和路由约定
- 表单验证使用项目中已有的验证方案（检查是否已用 Zod/Yup/其他）
- 认证方式：沿用项目现有认证方案（如无，默认 JWT）
- 包含：邮箱/密码登录表单、表单验证、错误提示、加载状态、响应式布局
工作流：
1. /plan 先规划组件结构和认证流程，参考现有页面的模式
2. /tdd 测试先行：编写登录表单的单元测试和认证流程的集成测试
3. 实现登录页面和认证逻辑
4. /code-review 审查实现
5. /verify 验证所有测试通过且页面正常渲染
安全要求：
- 密码不明文传输
- 防止暴力破解（rate limiting）
- XSS 防护
- CSRF token
验收标准：
- 所有测试通过，覆盖率 80%+
- 页面在移动端和桌面端正常渲染
- 登录成功跳转到 dashboard，失败显示错误信息
不要做：
- 不要实现注册页面
- 不要实现忘记密码功能
- 不要修改现有的路由结构示例 2：中等英文提示
用户输入：
添加一个用于用户资料更新的REST API端点，并包含验证功能。阶段 0 检测到： go.mod，使用 Go 1.22, Chi router
优化提示（完整）：
添加用于用户资料更新的 REST API 端点（PATCH /api/users/:id）。
技术栈：Go 1.22 + Chi 路由器（根据项目检测得出）
要求：
- PATCH /api/users/:id — 部分更新用户资料
- 对字段进行输入验证：name、email、avatar_url、bio
- 身份验证：需要有效令牌，用户只能更新自己的资料
- 成功时返回 200 及更新后的用户信息
- 输入无效时返回 400 及验证错误信息
- 身份验证失败时返回 401/403
- 遵循代码库中现有的 API 模式
工作流程：
1. /plan 端点结构、中间件链和验证逻辑
2. /tdd — 为成功、验证失败、身份验证失败、未找到等情况编写表格驱动测试
3. 按照现有处理器模式实现
4. /go-review
5. /verify — 运行完整的测试套件，确认没有回归问题
禁止事项：
- 修改现有端点
- 更改数据库模式（使用现有的 user 表）
- 在未先检查现有依赖项的情况下添加新依赖（使用搜索优先技能）示例 3：史诗级项目
用户输入：
将我们的单体架构迁移到微服务优化提示（完整）：
使用蓝图技能规划：“将单体架构迁移至微服务架构”
在执行前，请在蓝图中回答以下问题：
1. 当前单体架构中存在哪些领域边界？
2. 应该首先提取哪个服务（耦合度最低）？
3. 通信模式：REST API、gRPC 还是事件驱动（Kafka/RabbitMQ）？
4. 数据库策略：初期使用共享数据库，还是一开始就采用“每个服务一个数据库”？
5. 部署目标：Kubernetes、Docker Compose 还是无服务器？
蓝图应生成如下阶段：
- 阶段 1：识别服务边界并创建领域映射
- 阶段 2：搭建基础设施（API 网关、服务网格、每个服务的 CI/CD）
- 阶段 3：提取第一个服务（采用绞杀者模式）
- 阶段 4：通过集成测试验证，然后提取下一个服务
- 阶段 N：停用单体架构
每个阶段 = 1 个 PR，阶段之间设置 /verify 检查点。
阶段之间使用 /save-session。使用 /resume-session 继续。
在依赖关系允许时，使用 git worktrees 进行并行服务提取。
推荐：使用 Opus 5 进行蓝图规划，使用 Sonnet 5 执行各阶段。相关组件
| 组件 | 何时引用 |
|---|---|
configure-ecc | 用户尚未设置 ECC |
skill-stocktake | 审计安装了哪些组件（使用它而不是硬编码的目录） |
search-first | 优化提示中的研究阶段 |
blueprint | 史诗级范围的优化提示（作为技能调用，而非命令） |
strategic-compact | 长会话上下文管理 |
cost-aware-llm-pipeline | Token 优化推荐 |
© affaan-m, 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 docs/zh-CN/skills/prompt-optimizer of affaan-m/ECC.
Open the folder on GitHubat commit ef648e0
Used in 2 other repositories
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Prompt Optimizer 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 |
|---|---|---|---|---|---|---|
| Prompt Optimizer this skillaffaan-m/ECC | 274k | 2 repos | ~2.4k | Automated safety check: Pass | MIT | |
| LLM Torch Profiler Analysissgl-project/sglang | 37k | 2 repos | ~6.4k | Automated safety check: Pass | Apache-2.0 | |
| ExecuTorch Binary Size Reductionpytorch/executorch | 5.1k | — | ~793 | Automated safety check: Pass | Custom licence | |
| Keybase RPC Log Analysiskeybase/client | 9.3k | — | ~3k | Automated safety check: Pass | BSD-3-Clause | |
| The Art of Debuggingstas00/the-art-of-debugging | 1.7k | — | ~6.1k | Automated safety check: Notes | CC-BY-SA-4.0 | |
| Performance CheckZeroDeng01/sublinkPro | 1.7k | — | ~1.8k | Automated safety check: Pass | MIT |
Similar skills
ExecuTorch Binary Size Reduction
pytorch/executorch
Measures and shrinks the ExecuTorch runtime binary by building a size test, analyzing it with bloaty and landing each reduction as its own pull request.
Keybase RPC Log Analysis
keybase/client
Captures a clean Keybase service log and analyzes it for redundant, duplicated or looping RPCs, then checks whether a caching fix reduced the calls.
The Art of Debugging
stas00/the-art-of-debugging
Condensed debugging method and tool recipes for Unix, Python and PyTorch programs: crashes, hangs, segfaults, wrong output, CUDA OOM, NaN values and slowness.
Performance Check
ZeroDeng01/sublinkPro
Checklist for reviewing code changes that touch queries, APIs, rendering, caching or algorithms for performance, scalability and resource-usage problems.
Cuda
technillogue/ptx-isa-markdown
CUDA kernel development, debugging, and performance optimization for Claude Code.
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Related
Questions about Prompt Optimizer
What does Prompt Optimizer do?
分析原始提示，识别意图和差距，匹配ECC组件（技能/命令/代理/钩子），并输出一个可直接粘贴的优化提示。仅提供咨询角色——绝不自行执行任务。触发时机：当用户说“优化提示”、“改进我的提示”、“如何编写提示”、“帮我优化这个指令”或明确要求提高提示质量时。中文等效表达同样触发：“优化prompt”、“改进prompt”、“怎么写prompt”、“帮我优化这个指令”。不触发时机：当用户希望直接执行任…. Prompt Optimizer is an agent skill from affaan-m/ECC.
When should I use Prompt Optimizer?
Prompt Optimizer fits situations like: tasks that involve Prompt engineering; tasks that involve Performance optimization.
How do I install Prompt Optimizer in Claude Code?
Run `npx skills add affaan-m/ECC --skill prompt-optimizer -a claude-code`. Or copy the skill folder (docs/zh-CN/skills/prompt-optimizer in affaan-m/ECC) into .claude/skills/prompt-optimizer in your project. Claude Code loads it when a task matches its description.
How do I install Prompt Optimizer in Codex?
Run `npx skills add affaan-m/ECC --skill prompt-optimizer -a codex`. Or copy the skill folder (docs/zh-CN/skills/prompt-optimizer in affaan-m/ECC) into .agents/skills/prompt-optimizer in your project. Codex loads it when a task matches its description.
Can I use Prompt Optimizer 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 affaan-m/ECC --skill prompt-optimizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-optimizer, .gemini/skills/prompt-optimizer, .github/skills/prompt-optimizer and .opencode/skills/prompt-optimizer in your project.
What does Prompt Optimizer need to run?
SKILL.md names no scripts, command-line tools or credentials: Prompt Optimizer is instructions for the agent only. Our summary lists: Python 3; Node.js; Docker.
Does Prompt Optimizer 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 Prompt Optimizer 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 Prompt Optimizer use?
Prompt Optimizer 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 Prompt Optimizer use?
About 2.4k tokens (SKILL.md is roughly 9.7k 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 Prompt Optimizer?
Skills that share tags, products or a category with Prompt Optimizer: LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), ExecuTorch Binary Size Reduction (pytorch/executorch, 5.1k stars), Keybase RPC Log Analysis (keybase/client, 9.3k stars) and The Art of Debugging (stas00/the-art-of-debugging, 1.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Prompt Optimizer?
affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 274,360 GitHub stars. The repository holds 657 skills in this directory. The repository was last updated on October 5, 2026.
Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.