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Coverify

作者 burnmydays · GitHub ↗ · v0.4.3 · MIT-0
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在 OpenClaw 中安装
/install coverify
功能描述
CoVerify by MO§ES™ — the falsification instrument for the Commitment Conservation Law. Extract kernels, score Jaccard, detect ghost tokens, run model swap te...
使用说明 (SKILL.md)

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 \x3C 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

Command What it does
python3 commitment_verify.py extract "\x3Ctext>" Extract commitment kernel + input hash
python3 commitment_verify.py compare "\x3Ca>" "\x3Cb>" Jaccard score + CONSERVED/VARIANCE/DIVERGED verdict
python3 commitment_verify.py ghost "\x3Coriginal>" "\x3Ctransformed>" Step-function leakage report + ghost_pattern fingerprint
python3 commitment_verify.py verify \x3Chash_a> \x3Chash_b> Look up entries in audit ledger by input hash
python3 model_swap_test.py "\x3Csignal>" 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

Verdict Meaning
CONSERVED Jaccard ≥ 0.8 — commitment kernel survived transformation
VARIANCE Same input hash, Jaccard \x3C 0.8 — model extraction differs, not a leak
DIVERGED Different inputs, Jaccard \x3C 0.8 — commitment leaked or inputs genuinely different

What Ships

Version What ships
v0.1 extract, compare, verify — Conservation Law operational. ✓ Live.
v0.2 ghost — Step-function leakage model, cascade risk, ghost_pattern fingerprint. ✓ Live.
v0.3 model_swap_test — Cross-model CONSISTENT/VARIANCE/STRUCTURAL classification. ✓ Live.
v0.4 Archival 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

[email protected] · mos2es.io · GitHub

安全使用建议
This skill appears internally consistent and self-contained: it performs local text analysis, requires no credentials, and writes outputs under ~/.openclaw. Before installing or running it: 1) review/fix the truncated code in scripts/commitment_verify.py (the file ends mid-assignment and will likely crash the ghost command); 2) be aware the tool prints and saves extracted kernel sentences and reports (these can contain sensitive text) to ~/.openclaw — do not run it on sensitive secrets unless you accept local persistence; 3) model_swap_test.py expects either a provided JSON extract or a file and suggests recording patterns in a pattern_registry which is not included — evaluate whether you need that registry or to add it; 4) test the scripts in a sandboxed environment to confirm behavior and output locations before integrating them into any governance automation. There are no remote endpoints or secret requests in the package.
功能分析
Type: OpenClaw Skill Name: coverify Version: 0.4.3 The 'coverify' skill is a specialized NLP utility designed to measure semantic drift and 'commitment conservation' in text signals using Jaccard similarity and regex-based extraction. The Python scripts (commitment_verify.py and model_swap_test.py) implement transparent logic for identifying modal verbs and enforcement anchors without any network activity, obfuscation, or unauthorized file access. All file operations are restricted to the skill's declared state directories, and no evidence of malicious intent or prompt injection was found.
能力评估
Purpose & Capability
Name/description match the included scripts and SKILL.md: the Python scripts implement extraction, Jaccard comparison, ghost-token reporting, and a model-swap test harness. There are no unrelated environment variables, binaries, or external credentials requested.
Instruction Scope
SKILL.md instructs the agent to run the included Python scripts and to use a local audit ledger at ~/.openclaw/audits/moses/audit_ledger.jsonl; that is consistent with a verification tool. However, the included commitment_verify.py source appears truncated near the end (an unfinished assignment to hashlib.sha2) which will likely produce a runtime error for the ghost command as shipped. model_swap_test.py references pattern_registry.py (for recording ghost patterns) that is not included. Also note the tool prints/stores extracted kernels and may write results and saved reports under ~/.openclaw, so inputs (or kernel excerpts) may be persisted in plaintext.
Install Mechanism
There is no install spec (instruction-only with bundled scripts); nothing is downloaded from the network or installed automatically. This is lower risk. The SKILL.md suggests 'clawhub install coverify' but the package contains only local scripts (the platform's install step is not present here).
Credentials
The skill declares no required environment variables, no credentials, and no config paths beyond per-user directories under the home (~/.openclaw/*). Those file accesses are proportional to an audit/ledger-style verification tool. No network endpoints, tokens, or secrets are requested.
Persistence & Privilege
The skill does create and read files under the user's home directory (audit ledger, model-swap results), which is expected for an audit tool. It does not request elevated privileges, does not set always:true, and does not modify other skills' configs. Autonomous invocation is allowed by default (platform behavior) but not combined with any broad credential access here.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install coverify
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /coverify 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.4.3
Auto-publish from commit 7ce401c1a1daf43bc36435de47966fa3382ee2d1
v0.4.2
Auto-publish from commit e52f7b4cd7a2d63854c4bee1eddd84b9adb9b762
v0.4.1
No changes detected in this version. - No file or documentation updates. - Version number and functionality remain unchanged.
v0.4.0
- Major update: v0.4.0 introduces step-function leakage detection and cross-model testing, expanding commitment conservation verification to falsifiability. - Adds the new ghost command to quantify what commitment was lost, issue cascade risk ratings, and fingerprint “ghost patterns” for structural leak detection. - Model swap testing enables automated CONSISTENT/VARIANCE/STRUCTURAL verdicts by comparing extractions from multiple models. - Broadens support for provenance and archival lineage (isnad, handshake, multi-layer chain). - Documentation rewritten for clarity, emphasizing falsifiability, empirical testing, and structural leak identification.
v0.1.0
coverify v0.1.0 – Initial release - Introduces a commitment conservation verifier for text signals. - Extracts commitment kernels from text, scores Jaccard similarity across transformations, and verifies input provenance via cryptographic hashes. - Provides CLI commands: extract, compare, and verify. - Integrates with moses-governance audit logs for end-to-end constitutional enforcement. - Stands alone as a measurement instrument for provenance and commitment tracking.
元数据
Slug coverify
版本 0.4.3
许可证 MIT-0
累计安装 2
当前安装数 1
历史版本数 5
常见问题

Coverify 是什么?

CoVerify by MO§ES™ — the falsification instrument for the Commitment Conservation Law. Extract kernels, score Jaccard, detect ghost tokens, run model swap te... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 278 次。

如何安装 Coverify?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install coverify」即可一键安装,无需额外配置。

Coverify 是免费的吗?

是的,Coverify 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Coverify 支持哪些平台?

Coverify 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Coverify?

由 burnmydays(@sunrisesillneversee)开发并维护,当前版本 v0.4.3。

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