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Agent Skill · 研究检索

architecture-consistency-guardian(架构一致性守护者)

architecture-consistency-guardian 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。

architecture-consistency-guardian 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;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 21,898。

总安装 4,725周安装 193GitHub Stars 公开资料未说明下载 1,529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
2.0.1

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:architecture-consistency-guardian(架构一致性守护者)
来源仓库:https://github.com/upsightx/architecture-consistency-guardian
安装命令:
openclaw skills install architecture-consistency-guardian
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 architecture-consistency-guardian

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。

在代码变更前强制执行系统级一致性规则。architecture-consistency-guardian 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适用于跨文件重构与变量命名标准化任务。可自动检测并提示参数或字段命名冲突问题。

安装命令:openclaw skills install architecture-consistency-guardian。建议在受控环境中测试后再应用于关键项目。

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 architecture-consistency-guardian;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

architecture-consistency-guardian 怎么安装?

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

architecture-consistency-guardian 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。

architecture-consistency-guardian 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

architecture-consistency-guardian 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。

architecture-consistency-guardian 用的人多吗?

公开数据是累计安装 4,725 次,近一周安装 193 次,下载 1,529 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。

architecture-consistency-guardian 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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
architecture-consistency-guardian

架构一致性守护者 · 本页

4,725—只读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
architecture-consistency-guardian
description
>

Architecture Consistency Guardian

Purpose

Force a global-first posture for every code change that touches shared contracts: names, states, paths, configs, schemas, entry points, fallbacks, or documentation.

The default agent behavior is to patch the immediate error site. This skill overrides that instinct: scan globally, identify the single source of truth, modify as a group, audit for residue, then verify.

Mandatory Workflow (8 phases)

Every task under this skill MUST follow these phases in order. Skip a phase only if you can explicitly justify why it does not apply.

Phase 1 — Classify the Task

Determine which consistency category applies:

CategoryExamples
namingVariable, field, parameter, event name unification
state-machineStatus values, transition rules, write-back entry
config-pathDB path, env var, runtime config, deploy assumption
entry-pointConsolidating orchestrator / service / manager as sole entry
schemaTable name, column name, migration, compatibility layer
legacy-cleanupRemoving old fallback, old table writes, old function names
doc-alignmentSyncing SKILL.md, README, comments, contract docs with code
cross-moduleMulti-repo or multi-directory coordinated change

If multiple categories apply, lead with the one closest to the source of truth.

Phase 2 — Identify the Single Source of Truth

Answer explicitly before writing any code:

  1. What is the canonical file/module for this contract?
  2. Are there competing sources? List them.
  3. Which one survives, which ones get retired?

If no architecture contract document exists, produce a temporary contract summary (inline in your plan) covering: canonical state field, canonical write entry, canonical config source, and legacy items to retire. Read references/contract_template.md for the template when creating a persistent contract file.

Phase 3 — Global Reference Scan

Search the entire relevant scope (not just the current file) for:

  • Old and new variable/field names
  • Old and new status values
  • Hardcoded paths or env vars that should come from config
  • Old table names or column names
  • Old function/method names
  • Fallback branches that route to retired logic
  • Documentation still describing old flows

Use scripts/grep_legacy.py when available:

python3 <skill_dir>/scripts/grep_legacy.py <directory> <pattern1> <pattern2> ...

Use scripts/scan_contract_drift.py for multi-source detection:

python3 <skill_dir>/scripts/scan_contract_drift.py <directory> [--pattern-file <file>] [--mode default|lite|strict]

Mode guidance:

  • default: best general-purpose mode; ignores reference/template/test-only mentions as evidence
  • lite: same filtering as default, but down-ranks lower-risk categories for quick triage
  • strict: count every matching file, including references and tests, for forensic audits

Lite Mode

For small consistency fixes touching only 2-3 files with one clear source of truth, you may run a lite variant of the workflow:

  1. Classify the task
  2. Identify the source of truth
  3. Run a scoped global reference scan
  4. Output a short modification plan
  5. Edit all affected files in one pass
  6. Run a residue search for the retired names/paths
  7. Verify with at least one focused check

Use full 8-phase mode for state machines, schema changes, config paths, entry-point consolidation, or any task where multiple competing truths may exist.

Phase 4 — Produce a Modification Plan

Before touching code, output a concise plan:

  1. Source of truth — the canonical file
  2. Affected files — full list
  3. Changes per file — what gets renamed/removed/updated
  4. Compatibility layers to remove or retain (with justification if retained)
  5. Regression strategy — how you will verify

Do NOT proceed to edits without this plan.

Phase 5 — Execute Grouped Modifications

Recommended order: source of truth → callers → config layer → compatibility layer → tests → docs.

Rules:

  • Modify all references in a single logical pass; do not leave half-renamed states.
  • If a legacy item must be temporarily retained, mark it with a comment:

# COMPAT: <reason> — remove by <date or condition>

  • Never silently keep old logic alive.

Phase 6 — Residue Audit

After all edits, actively search for:

  1. Old variable/field names still present
  2. Old status values still present
  3. Old paths or env vars still present
  4. Old fallback branches still present
  5. Docs, comments, or SKILL files still referencing old flow

Use scripts/grep_legacy.py again with the retired names. If any residue is found, either fix it or explicitly document why it remains.

Phase 7 — Regression Verification

Execute at least one of:

  • Run existing test suite
  • Run a minimal script verifying the changed contract
  • Search for old names (zero hits expected)
  • Validate schema against code references
  • Confirm config resolution in the real directory layout

Phase 8 — Structured Report

Every task MUST end with a report containing these sections:

SectionRequiredContent
Classification✅Which consistency category
Source of truth✅The canonical location
Scope✅Files/modules affected
Changes made✅Concrete list of modifications
Residual compat✅What old logic remains and why
Verification✅What checks were performed
Follow-up risksRecommendedNext steps or remaining debt

Read references/output_template.md for the full report template.

Hard Rules

These are non-negotiable constraints:

  1. Never modify only the error site when the root cause is a contract mismatch.
  2. Never rename a symbol in one file without searching all files for the old name.
  3. Never delete a legacy path without confirming no external module depends on it.
  4. Never keep a fallback silently — if it stays, it gets a COMPAT comment and a reason.
  5. Never report "fixed" without stating what residue remains.
  6. Never skip doc/comment updates when code behavior changes.
  7. Always expand scope when the change touches: config vars, env vars, DB paths,

table/column names, status fields, status values, event names, service entry points, fallback logic, or documented main paths.

When to Suggest an Architecture Contract File

If you observe any of these recurring in a project, recommend creating an ARCHITECTURE_CONTRACT.md (template in references/contract_template.md):

  • Multiple status fields coexisting for the same entity
  • Multiple config entry points for the same value
  • Multiple write paths for the same data store
  • Docs and code chronically out of sync
  • Repeated legacy/new path confusion across tasks

References

  • references/workflow.md — Detailed workflow with decision branches and edge cases
  • references/output_template.md — Structured report template
  • references/risk_patterns.md — Common consistency risk patterns with examples
  • references/contract_template.md — Architecture contract template for projects lacking one

Scripts

  • scripts/grep_legacy.py — Scan directories for legacy name/path/status residue
  • scripts/scan_contract_drift.py — Detect multiple competing sources of truth; supports --mode default|lite|strict
  • scripts/summarize_impacts.py — Aggregate scan results into an impact summary

平台分布

OpenClaw

76.33%
按下载量换算1,167

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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