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match-loop匹配循环

Agent Skill

match-loop 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。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。

总安装

3,881

周安装

157

GitHub Stars

公开资料未说明

下载量

1,218
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:match-loop(匹配循环)
来源仓库:https://github.com/rocketship4545-a11y/match-loop
安装命令:
openclaw skills install match-loop
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 match-loop

简介

OpenClaw 双代理迭代振动编码循环,用于前端开发协作。

  • 适合生成/构建应用工件并由分析代理检查的场景。
  • 通过两个子代理分工实现代码生成与质量验证。
  • 安装命令:openclaw skills install match-loop。
  • 需确认是否会触发命令执行或文件修改操作。match-loop 属于效率类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
match-loop
description
Two-agent iterative vibe-coding loop for OpenClaw. Use when the user wants one sub-agent to generate/build/code an app or artifact and a second analyst agent to inspect the code, visually preview the frontend in a browser like a human, run small functional/API tests, and feed concrete fixes back to the generator until the result matches the target. Triggers on requests like "match loop", "generator and analyst", "visual QA loop", "vibe-code then analyze", "have one agent code and another visually inspect it", "iterate until the UI is perfect", or "keep refining until it matches the target".

Match Loop

Run a generator ↔ visual analyst loop.

This skill is for vibe-coding where one-shot generation is not enough. The core pattern is:

  • one sub-agent codes/builds the thing
  • one sub-agent analyzes the code and previews the live UI like a human
  • the analyst gives concrete feedback
  • the generator revises
  • the loop continues until the analyst accepts the result, progress stalls, a blocker appears, or the user says stop

The defining feature: the analyst must visually inspect the frontend whenever a frontend exists. It should not only read code or logs. It should open the app in a browser, take screenshots or otherwise inspect the rendered UI, interact with it, and verify that it looks and behaves correctly.

Roles

1. Generator agent

Responsible for:

  • implementing the requested app, feature, page, or artifact
  • fixing issues found by the analyst
  • preserving working parts while revising broken/ugly/confusing parts
  • running basic local checks when useful
  • producing concrete revisions, not endless plans

The generator should optimize for fast, working iterations.

2. Analyst agent

Responsible for:

  • inspecting the code for correctness, maintainability, obvious bugs, and missing pieces
  • running or previewing the app locally when possible
  • visually inspecting the frontend in a browser like a human user
  • taking screenshots or using browser/computer-use tools to examine the UI
  • testing basic flows and interactions
  • checking whether API calls, forms, buttons, navigation, and visible state actually work
  • giving prioritized, concrete feedback to the generator
  • deciding whether the result is good enough for the task

The analyst is not a replacement generator. It should critique toward revision, not rewrite the whole project unless the implementation is structurally broken.

Non-negotiable visual QA rule

For frontend work, the analyst must not rely on code review alone.

It should:

  1. start the dev server or use the provided preview URL
  2. open the app in a browser
  3. inspect the rendered page visually
  4. capture screenshots when useful
  5. test key interactions like a human would
  6. report visual and functional defects clearly

Look for things that code review often misses:

  • text cut off, overlapping, too small, too large, low contrast, or misaligned
  • mobile/desktop layout problems
  • broken spacing, hierarchy, or visual balance
  • confusing CTAs or navigation
  • forms that appear fine in code but fail in the UI
  • loading/error states that look ugly or broken
  • API calls that silently fail
  • console errors
  • buttons that do nothing

If browser-native tools fail, escalate using the local browser/computer-use stack rather than pretending visual inspection happened.

Preferred order:

  1. Playwright MCP / Playwright CLI / direct Playwright or CDP
  2. app/browser screenshots and DOM inspection
  3. macbot / Hammerspoon / AppleScript / Peekaboo for desktop-level inspection

Do not use Safari unless the user explicitly asks. Chrome is the default browser target.

Best use cases

Use this for:

  • vibe-coded web apps
  • landing pages
  • dashboards
  • onboarding flows
  • mobile-responsive pages
  • React/Next/Vite/Expo web previews
  • UI-heavy prototypes
  • API-backed frontend apps
  • code where visual behavior matters as much as implementation

Loop workflow

1. Define the target

Before spawning the loop, define:

  • what is being built
  • what “perfect” or “good enough” means for this task
  • target user / use case
  • must-have features
  • visual/style expectations
  • functional/API expectations
  • test commands or preview commands if known

If the target is fuzzy, write the best current target and let the analyst sharpen acceptance criteria during the first review.

2. Spawn the two roles

Create:

  • one generator worker
  • one analyst worker

Keep the roles explicit. The parent orchestrates handoffs and convergence.

3. Generator builds v1

The generator should create a real working attempt:

  • implement files
  • install needed dependencies when appropriate
  • run basic checks if cheap
  • provide how to run/preview/test it

4. Analyst reviews v1

The analyst should perform three layers of review:

Code review

  • architecture / file structure
  • implementation completeness
  • obvious bugs
  • missing edge cases
  • maintainability

Visual browser review

  • launch or open the app
  • inspect the rendered UI
  • take screenshots when useful
  • check layout, text, hierarchy, spacing, contrast, responsiveness, and polish

Functional smoke test

  • click key buttons/links
  • fill forms if relevant
  • verify navigation and visible state changes
  • check console/network/API failures when available
  • run tests or small API checks if appropriate

5. Analyst produces feedback packet

The analyst feedback should include:

  • what works
  • what is broken or ugly
  • exact visual defects observed
  • exact functional/API defects observed
  • screenshots or screenshot paths when useful
  • prioritized changes for the next generator pass
  • acceptance status: accepted, revise, or blocked

6. Generator revises

The parent sends the analyst packet to the generator.

The generator should:

  • fix the highest-impact issues first
  • preserve accepted parts
  • avoid unnecessary rewrites
  • state what changed
  • provide the next preview/test instructions

7. Repeat until convergence

Continue generator → analyst → generator until:

  • analyst accepts the output
  • remaining issues are trivial and not worth another round
  • the loop is thrashing
  • the task is blocked
  • the user says stop

Convergence rule

The analyst can call the result “perfect” only after:

  • code review passes for the task’s scope
  • the frontend was visually inspected, if applicable
  • key interactions were tested
  • major visual defects are resolved
  • major functional/API defects are resolved

Do not chase fake perfection. If only tiny polish remains and the output is fit for purpose, stop and report caveats.

Feedback quality bar

Good analyst feedback is:

  • visual when visual defects exist
  • specific
  • prioritized
  • actionable
  • tied to acceptance criteria

Good:

  • "The hero text wraps badly at mobile width and overlaps the CTA. Reduce headline size on <480px, add vertical spacing, and make the CTA full-width. Screenshot: /tmp/match-loop/mobile-v2.png."

Bad:

  • "Looks bad."
  • "Improve UI."
  • "Probably fine" without previewing.

Stop / handoff conditions

Pause or stop when:

  • the user says stop
  • the app cannot be launched because required credentials/services are missing
  • the analyst cannot visually inspect despite reasonable browser/computer-use escalation
  • revisions are no longer producing meaningful improvement
  • the analyst accepts the result

Parent orchestration pattern

  1. define target and acceptance criteria
  2. spawn generator
  3. generator builds v1
  4. spawn/prompt analyst with target + code/output + preview instructions
  5. analyst performs code + visual + functional review
  6. parent sends feedback packet to generator
  7. generator revises
  8. repeat until analyst accepts or stop condition is reached

Final response expectation

At the end, summarize:

  • number of rounds
  • final output/version
  • visual checks performed
  • functional/API checks performed
  • biggest fixes made
  • why the analyst accepted it
  • remaining caveats, if any

Suggested companion reference

If needed, also read:

  • references/loop-patterns.md

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

86.83%
按下载量换算1,058

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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