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COE Consensus

作者 JEP (Judgment Event Protocol) · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install coe-consensus
功能描述
COE Consensus Engine — Cross-Model Consensus Skill for Shared World State Formation
使用说明 (SKILL.md)

COE Consensus Skill

Cross-Model Consensus Engine

Algorithm implementation based on the COE (Cognition-Oriented Emergence) Protocol (Wang, 2026).

Core Problem

When multiple agents (humans, AI models, robots) observe the same physical space, how do they reach a verifiable consensus on "what the world is"?

Consensus Policies

Policy Use Case Rule
Simple Majority Small equal-trust groups Confirmations exceed 50% of all verifications received
Weighted Trust Heterogeneous agents with different reliability Sum of (trust_weight * confidence) exceeds threshold
BFT High-security with potential malicious agents More than f+1 confirmations out of at least 2f+1 total verifications

Shared World State (SWS)

Whenever consensus is reached, the engine produces an SWS record containing:

  • subject / predicate / value — the agreed-upon fact
  • confidence — aggregated confidence score
  • based_on — event IDs of the underlying J/V events
  • consensus_policy — policy used to reach agreement
  • confirmations — number of confirming verifications

Usage Example

Request

{
  "session_id": "warehouse-001",
  "target": "warehouse-zone-3",
  "policy": "weighted_trust",
  "events": [
    {
      "event_id": "evt-1",
      "primitive": "J",
      "issuer": "robot-A",
      "timestamp": "2026-04-19T10:30:00Z",
      "target": "warehouse-zone-3",
      "assertion": {"subject": "door_01", "predicate": "status", "value": "open"},
      "confidence": 0.95
    },
    {
      "event_id": "evt-2",
      "primitive": "V",
      "issuer": "robot-B",
      "timestamp": "2026-04-19T10:30:05Z",
      "target": "warehouse-zone-3",
      "verify_of": ["evt-1"],
      "verification_result": "confirmed",
      "confidence": 0.9
    }
  ],
  "trust_weights": {"robot-A": 0.9, "robot-B": 0.8},
  "weighted_threshold": 1.5
}

Response

{
  "session_id": "warehouse-001",
  "resolved": true,
  "policy": "weighted_trust",
  "sws": {
    "sws_id": "...",
    "target": "warehouse-zone-3",
    "timestamp": "2026-04-19T10:30:05Z",
    "assertions": [
      {
        "subject": "door_01",
        "predicate": "status",
        "value": "open",
        "confidence": 1.0,
        "based_on": ["evt-1"],
        "consensus_policy": "weighted_trust",
        "confirmations": 1
      }
    ]
  },
  "conflicts": [],
  "message": "Consensus complete. 1 assertions resolved, 0 conflicts remain.",
  "events_processed": 2,
  "events_by_issuer": {"robot-A": 1, "robot-B": 1}
}

Relationship with JEP

  • COE answers "what the world is" — cognitive consensus, ex-ante / in-situ collaboration.
  • JEP answers "who is responsible" — accountability tracing, post-hoc audit.
  • COE events may be referenced by JEP as evidence. Together they form a complete cognition-accountability dual-loop.

Cognitive Emergence Lab
[email protected]

安全使用建议
This skill appears to do what it says: a local consensus engine with a FastAPI interface. Before installing, consider: 1) Network exposure — the README example binds to 0.0.0.0; run behind a firewall or bind to localhost and add authentication if you will accept untrusted inputs. 2) Dependency supply chain — pip will install fastapi/uvicorn/pydantic; pin versions and install from trusted registries. 3) Input validation & access control — the service accepts arbitrary event payloads; if used in production, require authentication, rate limiting, and sanitize inputs. 4) Signature/hash fields are present in payload schema but no signing key management is implemented; if you rely on cryptographic provenance, extend the code to verify signatures. 5) Author provenance — the package lists an email but no homepage; if provenance matters for your environment, verify the source (contact author or review repository history). Running tests (pytest) and reviewing the example scripts locally in an isolated environment are recommended before deploying on a network-exposed host.
功能分析
Type: OpenClaw Skill Name: coe-consensus Version: 1.0.0 The coe-consensus skill bundle is a legitimate implementation of a consensus engine for multi-agent systems based on the Cognition-Oriented Emergence (COE) protocol. The code (skill/core.py, skill/api.py) contains standard logic for processing state assertions using algorithms like Simple Majority and Byzantine Fault Tolerance without any suspicious file system access, network exfiltration, or shell execution. The documentation in SKILL.md and README.md is consistent with the code's functionality and contains no prompt-injection or malicious instructions.
能力评估
Purpose & Capability
Name/description match the code and SKILL.md. The package implements a COE consensus engine (simple_majority, weighted_trust, BFT), provides a FastAPI HTTP interface, examples, and unit tests. Required dependencies (fastapi/uvicorn/pydantic) are coherent with an HTTP API skill.
Instruction Scope
SKILL.md and README describe running a FastAPI server (example uses `uvicorn --host 0.0.0.0 --port 8000`) and POSTing events to /consensus. The instructions do not ask the agent to read unrelated files or credentials. Note: the example binds to 0.0.0.0 — running this server as-is would expose an open HTTP endpoint unless firewalling/authentication is added.
Install Mechanism
No explicit install spec; code is included and manifest/requirements.txt list PyPI dependencies (fastapi, uvicorn, pydantic). There are no downloads from arbitrary URLs or extract operations. Installing will involve standard pip installation of well-known packages (pinning recommended).
Credentials
The skill requires no environment variables, credentials, or config paths. The code optionally accepts hash/signature fields in event payloads but does not request or depend on any signing keys or external secret material — the declared requirements are minimal and proportionate.
Persistence & Privilege
always is false and the skill does not claim to modify other skills or system-wide settings. It runs as an API service and may be invoked autonomously (default behavior) which is expected for an API skill; no elevated privileges are requested.
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 coe-consensus
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /coe-consensus 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
v1.0.0
Changelog All notable changes to the COE Consensus Skill will be documented in this file. [1.0.0] - 2026-04-26 Added Initial release of the COE Consensus Skill. Implemented three consensus policies from COE Protocol Section 4.2: Simple Majority: confirmations exceed 50% threshold. Weighted Trust: weighted confirmation score exceeds configurable threshold. Byzantine Fault Tolerance (BFT): lightweight BFT variant requiring >f+1 confirmations out of >=2f+1 total. Core engine supporting J (Judge), V (Verify), and T (Terminate) event processing. Conflict detection for contradictory assertions on the same subject-predicate pair. Shared World State (SWS) generation with full provenance and consensus policy annotation. Termination handling: T events invalidate prior J assertions before re-consensus. FastAPI HTTP interface (/consensus, /health) compatible with MCP Skill calling conventions. Full protocol reproduction example (Appendix A robot collaboration) in examples/robot_consensus.py. Unit tests covering simple majority, weighted trust, BFT, termination, and unresolved scenarios. Standard Clawhub SKILL.md manifest with YAML frontmatter and JSON schemas. Features Neutral Consensus Layer: Heterogeneous world models (JEPA, Dreamer, GPT, Claude, local models) emit COE events via adapters; consensus engine produces a unified SWS. Policy-Pluggable: Consensus policy configurable per session without code changes. Audit-Ready: All SWS records carry based_on event provenance for downstream JEP accountability tracing. References Wang, Y. (2026). COE: Cognition-Oriented Emergence. IETF Internet-Draft. Wang, Y. (2026). JEP: Judgment Event Protocol. IETF Internet-Draft. Wang, Y. (2026). Target Determinability under Partial Causal Observation. Cognitive Emergence Lab.
元数据
Slug coe-consensus
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

COE Consensus 是什么?

COE Consensus Engine — Cross-Model Consensus Skill for Shared World State Formation. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 52 次。

如何安装 COE Consensus?

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

COE Consensus 是免费的吗?

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

COE Consensus 支持哪些平台?

COE Consensus 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 COE Consensus?

由 JEP (Judgment Event Protocol)(@schchit)开发并维护,当前版本 v1.0.0。

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