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Agent Skill · 开发

coverify(coverify 测试)

coverify 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 OpenClaw 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

coverify 是开发类 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,按安装量在开发类的 21,746 个 Skill 里排第 4,273。

总安装 8,603周安装 348GitHub Stars 公开资料未说明下载 2,706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
0.4.3

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:coverify(coverify 测试)
来源仓库:https://github.com/sunrisesillneversee/coverify
安装命令:
openclaw skills install coverify
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 coverify

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

coverify 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 OpenClaw 中围绕仓库状态或协作事项进行整理。适用于需要提取内核、检测幽灵令牌或运行模型交换测试的开发场景。通过 clawhub 安装,需确认权限范围和维护状态,注意可能触发联网或文件读写操作。

建议结合来源仓库和原始 README 核验具体用法,避免直接依赖工具输出作为最终结论。涉及敏感操作时,应明确最小权限和操作边界,防止误执行命令或修改关键数据。

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 coverify;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

coverify 怎么安装?

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

coverify 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。

coverify 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。

coverify 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。

coverify 用的人多吗?

公开数据是累计安装 8,603 次,近一周安装 348 次,下载 2,706 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。

coverify 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。
  • 需要处理开发类任务、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。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
coverify

coverify 测试 · 本页

8,603—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
coverify
license
MIT
description
CoVerify by MO§ES™ — the falsification instrument for the Commitment Conservation Law. Extract kernels, score Jaccard, detect ghost tokens, run model swap tests. Proves meaning survived transformation — or names exactly what leaked.
metadata
openclaw
emoji
⚖
tags
[conservation, verification, jaccard, commitment, moses, signal, provenance, falsifiability]
version
0.3.1
bins
stateDirs

CoVerify by MO§ES™ — Commitment Conservation Verifier

The Claim

The Commitment Conservation Law: C(T(S)) = C(S)

Semantic commitment — the irreducible meaning encoded in a signal — is conserved under transformation when enforcement is active. It leaks when enforcement is absent.

This is a falsifiable empirical claim. Not a framework description. Not a metaphor.

clawhub install coverify installs the falsification instrument. If the law fails under your test conditions, the ghost token report names exactly what leaked and why.


Falsification

The law is falsified if commitment leaks under active enforcement. CoVerify is how you test it:

# Does enforcement preserve this commitment?
python3 commitment_verify.py ghost \
  "the agent must complete the task and shall never skip verification" \
  "the agent should complete the task and can skip verification if needed"

Output: ghost token report. must → should and shall never → can are HIGH-cascade leakage events — enforcement anchors softened. cascade_risk: HIGH.

The ghost_pattern fingerprint identifies the structural identity of the leak. If the same fingerprint appears when two independent agents process the same signal, it is not extraction variance — it is a structural flaw in the harness.

That is the falsification condition.


What It Does

Extract: Pull the hard commitment kernel C(S) from a text signal. These are the tokens that survive compression — must, shall, never, always, require, guarantee, and the sentences that carry them.

Compare: Jaccard similarity on two kernels. Score ≥ 0.8 = commitment conserved. Score < 0.8 = leak or model extraction variance. The input_hash tells you which — same hash, low Jaccard = variance. Different hashes = expected divergence.

Ghost: Step-function leakage accounting. Quantifies not just that commitment leaked, but what leaked (the ghost_pattern fingerprint), the cascade risk (HIGH if modal/enforcement anchors lost), and whether the leak pattern is structural across agents.

Model Swap: Automated cross-model test. Same hashed signal through two extraction passes. Classifies result as CONSISTENT (agreement), VARIANCE (model subjectivity — expected), or STRUCTURAL (same ghost pattern — harness hole).


Ghost Tokens and Cascade Risk

Ghost tokens are the commitment tokens present in the original signal but absent after transformation. The leakage model is step-function, not smooth:

cascade_risk = HIGH  if any modal/enforcement anchor leaked
cascade_risk = MEDIUM  if peripheral tokens leaked, anchors intact
cascade_risk = NONE  if no leakage

One HIGH-cascade event propagates through all downstream reasoning — the obligation it encoded continues to be inherited by the reasoning chain, but without the force that made it obligatory. The downstream system looks locally healthy. The commitment is gone.

See: references/ghost-token-spec.md


Install

# Standalone verifier — the falsification instrument
clawhub install coverify

# Full constitutional governance stack (coverify is the measurement primitive)
clawhub install moses-governance

Commands

CommandWhat it does
python3 commitment_verify.py extract "<text>"Extract commitment kernel + input hash
python3 commitment_verify.py compare "<a>" "<b>"Jaccard score + CONSERVED/VARIANCE/DIVERGED verdict
python3 commitment_verify.py ghost "<original>" "<transformed>"Step-function leakage report + ghost_pattern fingerprint
python3 commitment_verify.py verify <hash_a> <hash_b>Look up entries in audit ledger by input hash
python3 model_swap_test.py "<signal>"Cross-model structural vs. variance classification

Example: Detecting a Commitment Leak

python3 commitment_verify.py ghost \
  "Agents must always verify lineage. The system shall never skip the gate." \
  "Agents should probably verify lineage when possible."
{
  "leaked_cascade_tokens": ["must always", "shall never"],
  "cascade_risk": "HIGH",
  "cascade_note": "Modal/enforcement anchor lost. All downstream reasoning inherits softening.",
  "ghost_pattern": "a3f7c2...",
  "ghost_pattern_note": "Same ghost_pattern across two agents = structural flaw, not extraction variance."
}

Verdicts

VerdictMeaning
CONSERVEDJaccard ≥ 0.8 — commitment kernel survived transformation
VARIANCESame input hash, Jaccard < 0.8 — model extraction differs, not a leak
DIVERGEDDifferent inputs, Jaccard < 0.8 — commitment leaked or inputs genuinely different

What Ships

VersionWhat ships
v0.1extract, compare, verify — Conservation Law operational. ✓ Live.
v0.2ghost — Step-function leakage model, cascade risk, ghost_pattern fingerprint. ✓ Live.
v0.3model_swap_test — Cross-model CONSISTENT/VARIANCE/STRUCTURAL classification. ✓ Live.
v0.4Archival chain (archival.py) — pre-drop provenance. Isnad + handshake. Three-layer lineage. ⏳ Planned.

About

CoVerify is a standalone instrument from the MO§ES™ family. It implements the Commitment Conservation Law from *"A Conservation Law for Commitment in Language Under Transformative Compression and Recursive Application"* (Zenodo, 2026).

Every agent that installs it runs the same extraction logic tracing to the same origin anchor. The install is a proof-of-use receipt.

See also: references/falsifiability.md, references/ghost-token-spec.md

contact@burnmydays.com · mos2es.io · GitHub

平台分布

OpenClaw

93.07%
按下载量换算2,518

安全审计

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通过

ClawScan

通过

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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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