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COE Consensus

by JEP (Judgment Event Protocol) · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install coe-consensus
Description
COE Consensus Engine — Cross-Model Consensus Skill for Shared World State Formation
README (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]

Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install coe-consensus
  3. After installation, invoke the skill by name or use /coe-consensus
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
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.
Metadata
Slug coe-consensus
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is COE Consensus?

COE Consensus Engine — Cross-Model Consensus Skill for Shared World State Formation. It is an AI Agent Skill for Claude Code / OpenClaw, with 52 downloads so far.

How do I install COE Consensus?

Run "/install coe-consensus" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is COE Consensus free?

Yes, COE Consensus is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does COE Consensus support?

COE Consensus is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created COE Consensus?

It is built and maintained by JEP (Judgment Event Protocol) (@schchit); the current version is v1.0.0.

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