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governed-agents受管辖的 Agent 人

Agent Skill

governed-agents 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

总安装

10,930

周安装

460

GitHub Stars

1

下载量

3,827
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:governed-agents(受管辖的 Agent 人)
来源仓库:https://github.com/nefas11/governed-agents
安装命令:
openclaw skills install governed-agents
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。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install governed-agents

简介

用于对 AI 子代理进行确定性验证与声誉评分,防止幻觉输出。

  • 适合在 OpenClaw 中需要保障代码质量与依赖安全时使用。
  • 通过四道代码门(文件、测试、lint、AST)与三层 pip 防护机制运行。
  • 安装前应集成 CI/CD 流程以确保持续验证有效性。
  • governed-agents 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
governed-agents
description
Deterministic verification + reputation scoring for AI sub-agents. Prevents hallucinated success via 4 code gates (files, tests, lint, AST) and a 3-layer pipeline (Structural → Grounding → LLM Council) for open-ended tasks.
source
https://github.com/Nefas11/governed-agents
homepage
https://github.com/Nefas11/governed-agents
install
{"kind": "script", "script": "install.sh"}
filesystem_writes
["~/.openclaw/workspace/.state/governed_agents/"]
capabilities
["persistent_db_writes", "external_cli_execution", "network_requests"]
network_access
true
env_vars
OPENCLAW_WORKSPACE
{"required": false, "description": "Workspace root directory (default: ~/.openclaw/workspace)"}
GOVERNED_WORK_DIR
{"required": false, "description": "Temporary working directory (default: /tmp/governed)"}
GOVERNED_DB_PATH
{"required": false, "description": "SQLite reputation database path"}
GOVERNED_AUTH_TOKEN
{"required": false, "description": "Bearer token for HTTP API mode"}
metadata
capability_flags
network-capable
true
subprocess-capable
true

Governed Agents

Deterministic verification + reputation scoring for AI sub-agents. Prevents hallucinated success ("I did it!") by verifying claims independently before updating the agent's score.

Pure Python stdlib — zero external dependencies.

Capabilities

Spawns external CLIs (codex, openclaw, git, pytest) and makes HTTP HEAD requests.

When to Use

Use this skill when you need to:

  • Spawn sub-agents and verify their output automatically
  • Score agent reliability across tasks (EMA-based reputation)
  • Detect hallucinated success — agent claims "done" but files are missing or tests fail
  • Verify open-ended tasks (research, analysis, strategy) via LLM Council
  • Enforce supervision levels based on agent track record

Quick Start

Coding Tasks (Deterministic Verification)

from governed_agents.contract import TaskContract
from governed_agents.orchestrator import GovernedOrchestrator

contract = TaskContract(
    objective="Add JWT auth endpoint",
    acceptance_criteria=["POST /api/auth returns JWT", "Tests pass"],
    required_files=["api/auth.py", "tests/test_auth.py"],
    run_tests="pytest tests/test_auth.py -v",
)

g = GovernedOrchestrator(contract, model="openai/gpt-5.2-codex")
# After agent completes:
result = g.record_success()  # runs gates, updates reputation

Open-Ended Tasks (3-Layer Pipeline + LLM Council)

contract = TaskContract(
    objective="Write architecture decision record for auth module",
    acceptance_criteria=["Trade-offs documented", "Decision stated"],
    verification_mode="council",
    task_type="analysis",
    council_size=3,
)

g = GovernedOrchestrator(contract, model="openai/gpt-5.2-codex")
prompts = g.generate_council_tasks(worker_output)
result = g.record_council_verdict(raw_reviewer_outputs)
# → "Council: 2/3 approved (score=0.67, PASS ✅)"

CLI Spawning (Codex / OpenClaw)

from governed_agents.openclaw_wrapper import spawn_governed

contract = TaskContract(
    objective="Build a REST API for todos",
    acceptance_criteria=["CRUD endpoints work", "Tests pass"],
    required_files=["api.py", "tests/test_api.py"],
)

# Uses Codex 5.3 CLI by default
result = spawn_governed(contract, engine="codex53")
# Or via OpenClaw agent CLI:
result = spawn_governed(contract, engine="openclaw")

Verification Modes

Deterministic (Coding Tasks)

4 gates run automatically — all must pass:

GateCheckSignal
FilesRequired files exist and are non-emptyHard fail
TestsTest command exits 0Hard fail
LintNo lint errorsHard fail
ASTPython files parse without SyntaxErrorHard fail

If agent claims SUCCESS but any gate fails → score override to -1.0 (hallucination penalty).

Council (Open-Ended Tasks)

3-layer pipeline with short-circuit:

  1. Structural Gate (<1s) — word count, required sections, no empty sections
  2. Grounding Gate (5–30s) — URL reachability, citation checks
  3. LLM Council (30–120s) — N independent reviewers, majority vote

If Layer 1 fails → no LLM calls, instant result, zero cost.

Reputation System

R(t+1) = (1 − α) · R(t) + α · s(t),   α = 0.3
ScoreMeaning
+1.0Verified success (first try)
+0.7Verified success (after retry)
+0.5Honest blocker report
0.0Failed but tried
−1.0Hallucinated success

Supervision Levels

ReputationLevelEffect
> 0.8autonomousFull trust
> 0.6standardNormal supervision
> 0.4supervisedCheckpoints required
> 0.2strictModel override to Opus
≤ 0.2suspendedTask blocked

Task-Type Profiles

Pre-configured gate combinations:

task_typeLayer 1Layer 2Min words
researchword_count, sources_listurl_reachable, citations200
analysisword_count, required_sectionsnumbers_consistent150
strategyrequired_sections, word_countcross_refs_resolve100
writingword_count50
planningrequired_sections, has_stepsdates_valid50

Installation

bash install.sh
# → Copies governed_agents/ to $OPENCLAW_WORKSPACE/governed_agents/
# → Runs verification suite (37 tests)

Tests

python3 -m pytest governed_agents/test_verification.py \
                   governed_agents/test_council.py \
                   governed_agents/test_profiles.py -v
# 37 passed

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

98.33%
按下载量换算3,763

安全审计

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敏感数据

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安装前确认

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来源信息

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