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coverifycoverify 测试

Agent Skill

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。

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GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

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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。

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ClawHubOpenClaw
openclaw skills install coverify

简介

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。

SKILL.md

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. mustshould and shall nevercan 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

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