Coverify
/install coverify
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.
- Patent pending: Serial No. 63/877,177
- DOI: https://zenodo.org/records/18792459
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 \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
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install coverify - 安装完成后,直接呼叫该 Skill 的名称或使用
/coverify触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
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。