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ssa-revolution社会主义革命

Agent Skill

ssa-revolution 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

3,990

周安装

163

GitHub Stars

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下载量

1,291
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:ssa-revolution(社会主义革命)
来源仓库:https://github.com/cjboy007/ssa-revolution
安装命令:
openclaw skills install ssa-revolution
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 ssa-revolution

简介

多代理自动进化系统,采用审核-执行-审核循环架构。

  • 包含协调者、执行者与双审核者四角色协作机制。
  • 适用于复杂任务分解与智能体协同优化场景。ssa-revolution 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 安装命令:openclaw skills install ssa-revolution。
  • 使用前应评估任务复杂度与资源分配合理性。

SKILL.md

name
auto-evolution
description
Multi-agent auto-evolution system — orchestrate review-execute-audit loops with 4 roles (Coordinator, Reviewer, Executor, Auditor). A single coordinator agent drives the loop by spawning sub-agents for review, execution, and audit. Break goals into subtasks, auto-iterate with dual quality gates, and auto-package results. Use when: user wants autonomous task execution with built-in quality assurance.

auto-evolution

Category: Agent Orchestration / Meta-Skill Version: 0.6.0


Description

Multi-agent auto-evolution system — a coordinator agent drives an autonomous review → execute → audit loop by spawning specialized sub-agents for each role.

This is a meta-skill: it doesn't handle business logic. It orchestrates the loop so complex tasks get completed autonomously with dual quality gates (pre-execution review + post-execution audit).

Architecture (4 Roles)

RoleResponsibilityWhen SpawnedRecommended Model
CoordinatorDrives the loop, updates task state, spawns sub-agentsAlways (heartbeat/cron)Any (cost-efficient)
ReviewerPre-execution review, generates detailed instructionsBefore each subtaskStrong (Sonnet/GPT-4o)
ExecutorImplements one subtask, runs verificationAfter review approvesCost-effective (Qwen/Haiku)
AuditorPost-execution audit, decides pass/retryAfter execution completesStrong (Sonnet/GPT-4o)

Why 4 roles?

  • Reviewer and Auditor are both quality gates but serve different purposes
  • Reviewer ensures the plan is sound before work starts
  • Auditor verifies the result matches the plan after work completes
  • Executor is pure labor — follows instructions, no judgment needed

Cost control: Only Reviewer and Auditor need strong models. Coordinator and Executor can use cheap models.


Core Modules

FilePurpose
scripts/heartbeat-coordinator.jsCoordinator: scan tasks → spawn Reviewer/Executor/Auditor
scripts/monitor.jsMonitor: detect stuck tasks, clean orphaned locks
scripts/pack-skill.jsPackage completed tasks → skill directories
config/task-schema.jsonTask file JSON Schema

Setup

1. Initialize workspace

mkdir -p evolution/tasks evolution/archive evolution/test-results

2. Create a task

cp skills/auto-evolution/references/task-example.json evolution/tasks/task-001.json
# Edit with your goal and subtasks

3. Configure the coordinator

Option A: Heartbeat (recommended — in your agent's HEARTBEAT.md)

## Evolution Loop
1. Run `node skills/auto-evolution/scripts/heartbeat-coordinator.js`
2. Parse output: if phase=review → spawn Reviewer sub-agent
3. Apply review → if phase=execute → spawn Executor sub-agent
4. Apply execution → if phase=audit → spawn Auditor sub-agent
5. Apply audit → done for this tick

Option B: Cron

openclaw cron add --agent <your-agent> \
  --name "evolution-coordinator" \
  --every 5m \
  --session isolated \
  --timeout-seconds 300 \
  --message "Evolution heartbeat: scan and process tasks."

4. (Optional) Configure the monitor

openclaw cron add --agent <any-agent> \
  --name "evolution-monitor" \
  --every 10m \
  --session isolated \
  --timeout-seconds 120 \
  --message "Run: node skills/auto-evolution/scripts/monitor.js"

5. Environment variables (optional)

export OPENCLAW_WORKSPACE=/path/to/workspace
export EVOLUTION_TASKS_DIR=/path/to/tasks

How It Works

Full Loop

Coordinator heartbeat
  → finds task (priority: reviewed > executing > pending)
  → if pending: spawn Reviewer → reviewed
  → if reviewed: spawn Executor → executing
  → if executing: spawn Auditor → pending (next) or completed ✅

State Machine

pending → reviewed → executing → pending (next subtask)
                         → completed (all done)
                         → packaged ✅

Key Rules

  • One subtask per iteration — keeps cycles fast and reviewable
  • Dual quality gates — Reviewer (before) + Auditor (after)
  • Only mark completed when all subtasks done
  • If Reviewer/Auditor API fails → wait and retry next heartbeat
  • Monitor auto-resets tasks stuck > 10 minutes

Task File Format

See references/task-example.json for a complete example.

Required fields:

{
  "task_id": "task-001",
  "status": "pending",
  "goal": "What to build",
  "current_iteration": 0,
  "max_iterations": 10,
  "context": {
    "subtasks": ["Step 1", "Step 2", "Step 3"]
  },
  "history": []
}

CLI Usage

# Scan and output next phase prompt
node scripts/heartbeat-coordinator.js

# Apply review result
node scripts/heartbeat-coordinator.js apply-review task-001.json review.txt

# Apply execution result
node scripts/heartbeat-coordinator.js apply-exec task-001.json exec.txt

# Apply audit result
node scripts/heartbeat-coordinator.js apply-audit task-001.json audit.txt

# Run monitor
node scripts/monitor.js

# Package completed tasks
node scripts/pack-skill.js

Design Philosophy

  • 4-role architecture — Coordinator drives, Reviewer/Executor/Auditor specialize
  • Dual quality gates — Review before, audit after — never skip either
  • Model-agnostic — swap any model for any role
  • One subtask per tick — predictable, reviewable, won't timeout
  • Self-healing — monitor detects and fixes stuck states
  • Cost-efficient — strong models only where judgment matters (Reviewer, Auditor)

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

71.17%
按下载量换算919

安全审计

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通过

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权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

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来源信息

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