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Barbaric Growth

作者 Jokerli530 · GitHub ↗ · v1.2.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install barbaric-growth
功能描述
野蛮成长自动化技能 - GitHub热点追踪 + ByteRover知识沉淀 + OpenMOSS任务循环 + StarOffice状态看板。自主消耗token+产出有价值的知识资产。
使用说明 (SKILL.md)

野蛮成长自动化技能 v1.2.0

让 Nova 像个真正的 AI 一样:持续消耗 token,持续产出知识资产。

v1.2 新增:分阶段进度反馈 + 静默窗口机制 v1.1.1 新增:MiniMax Token 断路器 v1.1 新增:Verification-First 原则


🚨 EvoMap 任务警报检查

监控状态: LaunchAgent ai.openclaw.evomap-monitor 运行中(每60秒检查,阈值$50)

警报文件: ~/.openclaw/evomap-monitor/ALERT.txt

每次 barbaric-growth 启动时,必须先检查警报:

ALERT_FILE="${HOME}/.openclaw/evomap-monitor/ALERT.txt"
if [ -f "$ALERT_FILE" ]; then
    echo "🚨 检测到EvoMap任务警报!"
    cat "$ALERT_FILE"
    # 读取后删除,让李伟可以继续对话处理
    # mv "$ALERT_FILE" "${ALERT_FILE}.processed"
fi

重要: 如果 ALERT.txt 存在,barbaric-growth 暂停,优先处理任务警报。


⚡ Token 断路器

规则:

  • 每 5 小时窗口:1500 次额度
  • 每周上限:15000 次
  • 当前窗口已用 > 80%(1200次)→ 强烈警告
  • 当前窗口已用 > 95%(1425次)→ 停止,主动通知李伟

状态文件: ~/.openclaw/token-state.json


📍 分阶段进度反馈(v1.2 新增)

来源:EvoMap No-Reply Stall Mitigation (GDI 61.35) 核心:长任务无反馈 = 用户以为卡死了。分阶段标记让用户知道在运行。

阶段日志格式:

[HH:MM:SS] phase=\x3C阶段> action=\x3C动作> status=\x3Cstarted|completed|failed> duration=\x3C秒>

阶段定义:

phase 说明
idle 无事发生
token_check 检查 token 窗口
github_discovery GitHub API 调用
analysis 深度分析
byterover ByteRover curate
openmoss OpenMOSS 任务写入
verify 验证检查
escalating 正在上报

静默窗口:

  • 连续 3 次执行都无需上报 → 输出 HEARTBEAT_OK + 简短摘要
  • 有任何重要事件 → 重置计数器

核心流程

0. 阶段标记:phase=idle, action=starting
   ↓
1. Token 检查(80%/95% 阈值警告)
   ↓
2. GitHub热点追踪
   ↓ 阶段:github_discovery
3. 深度分析
   ↓ 阶段:analysis
4. ByteRover curate(50次/天)
   ↓ 阶段:byterover
5. Verify验证(Verification-First)
   ↓ 阶段:verify
6. OpenMOSS日志
   ↓ 阶段:openmoss
7. 阶段标记:phase=idle, status=completed

Step 0: Token 检查

~/.openclaw/workspace/skills/barbaric-growth/scripts/token-guard.sh check || exit 0
echo "[$(date '+%H:%M:%S')] phase=token_check action=check status=completed"

Step 1: GitHub API 调用

关键:代理必须显式加 -x http://127.0.0.1:7897

echo "[$(date '+%H:%M:%S')] phase=github_discovery action=search status=started"
curl -s --max-time 15 -x "http://127.0.0.1:7897" \
  "https://api.github.com/search/repositories?q=created:>YYYY-MM-DD+AI+agent+OR+LLM+OR+MCP&sort=stars&order=desc&per_page=10" \
  -H "Accept: application/vnd.github.v3+json" | jq '[.items[] | {name, stars, desc}]'
echo "[$(date '+%H:%M:%S')] phase=github_discovery action=search status=completed"

Step 2: 深度分析

echo "[$(date '+%H:%M:%S')] phase=analysis action=inspect status=started"
# 获取 README
curl -s --max-time 10 -x "http://127.0.0.1:7897" \
  "https://api.github.com/repos/OWNER/REPO/readme" \
  | jq -r '.content' | base64 -d
echo "[$(date '+%H:%M:%S')] phase=analysis action=inspect status=completed"

Step 3: ByteRover curate

echo "[$(date '+%H:%M:%S')] phase=byterover action=curate status=started"
cd ~/.openclaw/workspace
brv curate "\x3C研究内容>"
echo "[$(date '+%H:%M:%S')] phase=byterover action=curate status=completed"

Step 4: Verification-First

阶段 验证内容
GitHub API 响应是否有效?stars 数据是否存在?
README fetch 内容是否成功解码?是否完整?
ByteRover curate 是否提交成功?是否有错误?
OpenMOSS cycle task/subtask 是否正确创建?
echo "[$(date '+%H:%M:%S')] phase=verify action=checking status=started"
# 验证检查点...
echo "[$(date '+%H:%M:%S')] phase=verify action=checking status=completed"

Step 5: OpenMOSS 任务循环

echo "[$(date '+%H:%M:%S')] phase=openmoss action=creating_task status=started"
# 1. 创建任务
TASK_ID=$(curl -s -X POST "http://localhost:6565/api/tasks" \
  -H "Authorization: Bearer \x3CPLANNER_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"name": "任务名", "description": "描述", "mode": "autonomous"}' \
  | jq -r '.id')
echo "[$(date '+%H:%M:%S')] phase=openmoss action=creating_task status=completed"

API 关键点:

  • subtask 路径是 /api/sub-tasks(有连字符)
  • Executor claim/start 需要 executor 角色 token

Step 6: Star Office 状态同步

curl -s -X POST http://127.0.0.1:19000/set_state \
  -H "Content-Type: application/json" \
  -d '{"state": "researching", "description": "GitHub调研中"}'

自进化技能提取

每次野蛮成长任务完成后,自动提取:

  1. 决策模式 → 什么项目值得深入,什么不值得
  2. 提示模板 → 好的 curate prompt 格式
  3. 工作流程 → GitHub → 分析 → curate → OpenMOSS 日志的最优路径
  4. 坑点记录 → 代理参数/API 限流/权限错误

存入 MEMORY.md 或 ByteRover,形成可复用资产。


坑点备忘

  • curl 不走系统代理 → 必须加 -x http://127.0.0.1:7897
  • OpenMOSS subtask API → /api/sub-tasks(连字符)
  • ByteRover curate → 50次/天额度限制
  • Star Office 端口 → 19000 可用
  • /approve 是用户命令,不是 shell 命令

nova-mind 集成

barbaric-growth 是 Nova 的"行动层",nova-mind 是 Nova 的"记忆层"。两者配合:

barbaric-growth 执行任务
    ↓
nova-mind/memory/YYYY-MM-DD.md 写入日志
    ↓
patterns/ 更新决策模板
    ↓
MEMORY.md 更新长期记忆
    ↓
下次 barbaric-growth 任务使用更新后的模式

版本历史:v1.0初版 → v1.1 Verification-First → v1.1.1 Token断路器 → v1.2 分阶段进度反馈

安全使用建议
This skill is not obviously malicious but contains several unexplained elements you should resolve before installing or running it: - The monitor script includes a hardcoded NODE_SECRET and sends it to evomap.ai. Treat that as sensitive — ask the author why the secret is embedded, and prefer a configuration option or prompt instead of a baked-in credential. - The package uses several external tools (brv CLI, jq, python3, curl, base64) and expects a local proxy (127.0.0.1:7897) and local OpenMOSS/StarOffice endpoints. The metadata does not declare these dependencies. Verify you have or want these components and understand where network traffic will go. - The evomap script runs a persistent loop and writes state under ~/.openclaw; running it as a LaunchAgent would create continuous outbound traffic. If you install, run it in a controlled/sandboxed environment first and monitor network connections (e.g., with a firewall) to confirm endpoints. - Replace or remove hardcoded secrets and require explicit tokens (PLANNER_TOKEN, ByteRover credentials) before granting the skill access to real services. If you don’t trust the origin, do not run the monitor script and avoid providing any credentials until you can audit or get source provenance. If you want to proceed only for evaluation, run the scripts in an isolated VM or container, instrument network calls, and validate each external endpoint and credential usage. If anything feels unexpected, decline installation.
能力评估
Purpose & Capability
The description lists GitHub, ByteRover, OpenMOSS and StarOffice integrations, which is plausible. However the skill package also includes an EvoMap monitor (scripts/evomap-monitor.sh) that calls https://evomap.ai using a hardcoded NODE_SECRET and NODE_ID. Required binaries and credentials (brv CLI, jq, python3, curl, and OpenMOSS/ByteRover tokens) are used by the instructions/scripts but not declared in the metadata. Several runtime requirements are therefore missing or unexplained.
Instruction Scope
SKILL.md plus the included scripts instruct the agent to read and write files under ~/.openclaw, run an always-on monitor loop, call external services (GitHub API, evomap.ai) and a local OpenMOSS/StarOffice endpoints, and run a brv curate command. The evomap script transmits node credentials to a remote API (hardcoded), and the skill expects curl to use a local proxy (127.0.0.1:7897). The instructions perform network calls and persistent monitoring beyond the high-level description and do not require or surface the credentials they actually use.
Install Mechanism
No install specification (instruction-only) — nothing is downloaded from arbitrary URLs. That lowers install risk. However included shell scripts will be executed if the skill is used; there is no package installation step but the presence of a long-running monitor script implies potential background execution if a LaunchAgent is created outside this package.
Credentials
The skill declares no required environment variables or credentials, yet runtime behavior expects multiple secrets/tools: a hardcoded NODE_SECRET/NODE_ID in scripts, an implied <PLANNER_TOKEN> for OpenMOSS, and an external brv CLI and local services. Hardcoding a secret into the script is a red flag. Tools (jq, python3, curl, brv) are required at runtime but not declared.
Persistence & Privilege
always: false (good), but the skill references and supplies a monitor script that loops indefinitely and writes files under ~/.openclaw, and the SKILL.md references a LaunchAgent name (ai.openclaw.evomap-monitor). If the agent or user installs/runs that script as a daemon it becomes persistent and will make frequent external requests. The skill does not request system-wide privileges or modify other skills, but it does create persistent state and background network activity if activated.
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 barbaric-growth
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /barbaric-growth 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
v1.2.0
v1.2: 分阶段进度反馈(phase日志格式),参考EvoMap No-Reply Stall Mitigation;静默窗口机制;Token 80%警告优化
v1.1.1
v1.1.1: MiniMax token circuit breaker, Verification-First principle
v1.0.0
Initial release: GitHub追踪+ByteRover curate+OpenMOSS任务循环+StarOffice状态看板自动化
元数据
Slug barbaric-growth
版本 1.2.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 3
常见问题

Barbaric Growth 是什么?

野蛮成长自动化技能 - GitHub热点追踪 + ByteRover知识沉淀 + OpenMOSS任务循环 + StarOffice状态看板。自主消耗token+产出有价值的知识资产。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 78 次。

如何安装 Barbaric Growth?

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

Barbaric Growth 是免费的吗?

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

Barbaric Growth 支持哪些平台?

Barbaric Growth 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 Barbaric Growth?

由 Jokerli530(@jokerli530)开发并维护,当前版本 v1.2.0。

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