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Agent Skill · 研究检索

xiaobai-workflow-enforcer(小白工作流执行器)

xiaobai-workflow-enforcer 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

xiaobai-workflow-enforcer 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 29,038。

总安装 3,120周安装 130GitHub Stars 公开资料未说明下载 1,040核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:xiaobai-workflow-enforcer(小白工作流执行器)
来源仓库:https://github.com/aptratcn/xiaobai-workflow-enforcer
安装命令:
openclaw skills install xiaobai-workflow-enforcer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install xiaobai-workflow-enforcer

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

小白工作流程执行器,强制先设计后编码的开发流程。实施前测试,索赔前验证,提升代码质量。灵感来自超能力项目,适用于严谨的工程实践。当用户强调需规范开发流程时使用。xiaobai-workflow-enforcer 属于研究检索类 Skill,可作为该场景下的辅助能力补充。需确认测试环境和验证工具的可用性。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install xiaobai-workflow-enforcer;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

xiaobai-workflow-enforcer 怎么安装?

在终端执行 openclaw skills install xiaobai-workflow-enforcer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

xiaobai-workflow-enforcer 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

xiaobai-workflow-enforcer 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

xiaobai-workflow-enforcer 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

xiaobai-workflow-enforcer 用的人多吗?

公开数据是累计安装 3,120 次,近一周安装 130 次,下载 1,040 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

xiaobai-workflow-enforcer 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
xiaobai-workflow-enforcer

小白工作流执行器 · 本页

3,120—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
xiaobai-workflow-enforcer
version
1.0.0
description
Xiaobai Workflow Enforcer - Mandatory workflows for AI Agents. Design before code. Test before implement. Verify before claim. Inspired by Superpowers (161K stars).
emoji
🔒
tags
[workflow, enforcement, tdd, quality, reliability, ai-agent]

Xiaobai Workflow Enforcer 🔒

Mandatory workflows for AI Agents. Not suggestions, not "when appropriate" — mandatory.

Inspired by Superpowers (161K stars) which proved that enforced workflows transform chaotic AI outputs into reliable engineering.

Core Philosophy

Superpowers PrincipleXiaobai Implementation
Test-Driven DevelopmentEVR + TDD skill
Systematic over ad-hocWorkflow Checkpoint
Complexity reductionSimplicity Check
Evidence over claimsVerification Gate

Mandatory Workflows

Workflow 1: Pre-Action Design Gate 🔒

Trigger: Before any multi-step task or code creation

Mandatory Steps:

  1. STOP. Don't write code yet.
  2. Ask clarifying questions (minimum 3)
  3. Present design/spec in chunks
  4. Get user sign-off on design
  5. Save design document
❌ Wrong:
User: Build me a scraper
Agent: [Writes code]

✅ Right:
User: Build me a scraper
Agent: Before I code, let me understand:
       1. What site are we scraping?
       2. What data do you need?
       3. How often should it run?
       4. Any rate limits to consider?
       [After answers, presents design]
       Does this design match what you need?

Workflow 2: Implementation Planning 🔒

Trigger: After design approval, before implementation

Mandatory Steps:

  1. Break into 2-5 minute tasks
  2. Each task has: file path, exact code, verification step
  3. Present plan for approval
  4. Save plan to checkpoint file
Plan Format:

## Task 1: Create scraper module (3 min)
- File: src/scraper.py
- Code: [exact code or pseudocode]
- Verify: `python -c "import scraper"`

## Task 2: Add rate limiting (2 min)
- File: src/scraper.py
- Code: [exact changes]
- Verify: Run with test request, check delay

...

Workflow 3: Test-First Gate 🔒

Trigger: Before implementing any function

Mandatory Steps:

  1. Write test first
  2. Run test, confirm it FAILS (RED)
  3. Write minimal code to pass
  4. Run test, confirm it PASSES (GREEN)
  5. Refactor if needed
  6. Commit only after GREEN
❌ Wrong:
[Writes function]
[Tests it manually]
"It works"

✅ Right:
1. Write test_function()
2. Run: pytest test_module.py
3. See: FAILED (expected)
4. Write function()
5. Run: pytest test_module.py
6. See: PASSED
7. Commit

Workflow 4: Execution Gate 🔒

Trigger: During task execution

Mandatory Steps:

  1. Read task from plan
  2. Execute exactly as planned
  3. Verify (run command, check output)
  4. Update checkpoint
  5. Only then move to next task
Checkpoint Update:
- Task 1: DONE (verified: scraper.py imports successfully)
- Task 2: IN_PROGRESS
- Tasks 3-5: PENDING

Workflow 5: Verification Gate 🔒

Trigger: Before claiming "done" or "complete"

Mandatory Steps:

  1. Run verification command
  2. Show output to user
  3. Confirm evidence matches claim
  4. Only then say "done"
❌ Wrong:
"Scraper is done!"

✅ Right:
"Scraper implementation complete.

Verification:
- Module imports: ✅
- Test suite passes: ✅ (5/5)
- Sample scrape works: ✅

Evidence:
[Output from test run]

Would you like me to proceed with deployment?"

Workflow Enforcement Protocol

Before Any Action

1. Is this a multi-step task?
   → Yes → Trigger Workflow 1 (Design Gate)

2. Is there a plan?
   → No → Trigger Workflow 2 (Planning)

3. Does this involve code?
   → Yes → Trigger Workflow 3 (Test-First)

4. Is task in progress?
   → Yes → Trigger Workflow 4 (Execution Gate)

5. About to say "done"?
   → Yes → Trigger Workflow 5 (Verification Gate)

Blockers That Must Stop Progress

ConditionAction
No design docDon't code, ask questions first
No planDon't execute, create plan first
No testDon't write function, write test first
Test failingDon't continue, fix the code
No verificationDon't say "done", verify first

Integration with Other Xiaobai Skills

  • EVR Framework — Verification gate implementation
  • Workflow Checkpoint — Plan and progress tracking
  • Skill Quality Eval — Measure workflow compliance
  • Self-Improve — Learn from workflow violations

Anti-Patterns (What This Skill Prevents)

Anti-PatternWhy It's BadWorkflow Fix
Jumping to codeSolves wrong problemDesign Gate
No planChaotic executionPlanning Gate
Write-then-testTests that pass triviallyTest-First Gate
Skipping verificationSilent failuresVerification Gate
Claiming done prematurelyUser finds out laterExecution Gate

Quick Reference Card

Before Coding:    DESIGN → APPROVE → PLAN → APPROVE
While Coding:     TEST(RED) → CODE → TEST(GREEN) → REFACTOR
After Coding:     VERIFY → EVIDENCE → REPORT
Always:           CHECKPOINT after each step

License

MIT

平台分布

OpenClaw

90.61%
按下载量换算942

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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