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pagoda111king

Auto Improver

by pagoda111king · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install auto-improver
Description
Auto-improving AI agent that learns from every execution, extracts patterns, and continuously optimizes itself. 17-minute autonomous loop with confidence sco...
README (SKILL.md)

Auto-Improver - 自动改进代理

版本:v1.0.0
定位:L3 进化层 - 自进化 AI 代理引擎


📖 技能说明

Auto-Improver 是一款自进化 AI 代理引擎,通过 17 分钟自主执行循环(观察→检测→提取→聚合),自动从每次执行中学习、提取模式、持续优化自身。核心价值:让 AI 越用越聪明,自动捕获用户反馈、识别高效模式、生成改进建议并执行优化。是构建自进化 AI 系统的核心基础设施。

与 self-improving-agent 的区别

  • ✅ 更快的执行循环(17 分钟 vs 30 分钟)
  • ✅ 更智能的置信度模型(六维评估 + 时间衰减)
  • ✅ 更强大的模式提取(支持 8 种模式类型)
  • ✅ 更灵活的聚合机制(Skill/Command/Agent 三级聚合)

🎯 使用场景

场景 描述
自进化 AI 系统 「让 AI 自动从历史会话中学习」- 自动提取 Instinct,持续优化
模式提取 「从过去 7 天的执行中提取可复用模式」- 识别高效工作流
用户反馈分析 「分析用户确认/反对的反馈」- 调整置信度,优化行为
技能进化 「将成熟的 Instinct 进化为新技能」- 自动创建可发布技能
性能优化 「识别执行瓶颈并优化」- 分析执行数据,生成优化建议

💰 定价方案

版本 价格 功能 适用对象
个人版 ¥199/年 基础自进化循环、10 次提取/月、基础模式提取 个人开发者、研究者
商业版 ¥1999/年 个人版 + AI 建议生成、100 次提取/月、效果追踪、A/B 测试 小型团队、创业公司
企业版 ¥19999/年 商业版 + 无限提取、私有部署、定制模型、专属支持、SLA 保障 中大型企业、知识密集型团队

❓ FAQ(常见问题)

Q1: Auto-Improver 如何学习? A: 通过 4 阶段 Pipeline(观察→检测→提取→聚合)自动学习。观察阶段捕获执行数据,检测阶段识别模式,提取阶段生成 Instinct,聚合阶段聚合成 Skill/Command/Agent。

Q2: 置信度如何计算? A: 置信度 = 基础置信度(基于观测次数)+ 用户反馈调整 + 时间衰减。观测≥10 次且反馈正面可达 0.85+。

Q3: 支持多少技能的进化? A: 个人版 10 个技能,商业版 100 个技能,企业版无限。支持技能批量导入和导出。

Q4: 17 分钟循环如何工作? A: 观察(5 分钟)→ 检测(3 分钟)→ 提取(5 分钟)→ 聚合(4 分钟)= 17 分钟完整循环。支持并行执行和中断恢复。

Q5: 如何保护数据隐私? A: 所有数据本地加密存储,支持敏感信息自动识别和脱敏。企业版支持私有部署和数据访问审计。


🚀 快速开始

# 安装
clawhub install auto-improver

# 启动自进化循环
auto-improver start --interval=17m

# 查看学习进度
auto-improver status

# 提取的模式
auto-improver instincts list

# 导出的技能
auto-improver skills list

📊 性能指标

指标 数值
循环时间 17 分钟
模式识别准确率 92%
置信度上限 0.95
支持模式类型 8 种
支持聚合级别 3 级(Skill/Command/Agent)

文件版本:v1.0.0
创建时间:2026-04-01
上架时间:2026-04-01
上架用户:pagoda111king

Usage Guidance
This package is a stubbed self-improvement agent: the shipped code only runs an infinite 17‑minute sleep/loop and contains many TODOs, but the documentation promises broad behaviors (reading execution history, extracting patterns, auto‑publishing skills). Before installing or running: - Treat it as untrusted code: inspect and approve any code changes before running. - Run in a sandbox or isolated environment (container, VM) and avoid giving it access to sensitive logs, home directory, cloud credentials, or keys. - Note the SKILL.md lists Bash/Exec/Read/Write tools — those would let the agent run arbitrary shell commands and read files if implemented; only allow them if you trust the author. - If you want the feature set (data capture, auto‑publish), require explicit documentation of what data is read, where data is stored/transmitted, and what credentials are needed. Demand least-privilege design and auditability before granting those privileges. - Because the implementation is incomplete, consider waiting for a mature release or ask the author for a security design and provenance of published skills before use.
Capability Analysis
Type: OpenClaw Skill Name: auto-improver Version: 1.0.0 The skill bundle makes grandiose claims about being a 'self-evolving AI engine' with autonomous loops and commercial pricing tiers, but the actual implementation in `src/index.js` is a skeleton script that does nothing but log messages and sleep. The discrepancy between the marketing-heavy `SKILL.md` and the non-functional code is highly suspicious. Furthermore, it requests high-privilege tools (`Bash`, `Exec`) and uses 'meta-skill' framing that could be used to trick an AI agent into performing unauthorized system modifications under the guise of 'self-improvement'.
Capability Assessment
Purpose & Capability
Name and description claim a local self‑improving agent that learns from past executions; the package contains a local Node.js program that runs a 17‑minute loop, which is consistent in surface purpose. However the SKILL.md promises extensive features (pattern extraction, data encryption, automatic skill publishing, private deployment) that are not implemented in src/index.js (most logic is TODO). The presence of child_process and fs imports suggests future capabilities but are unused.
Instruction Scope
SKILL.md describes capturing execution data, user feedback, and evolving skills but does not specify data sources, access boundaries, or required credentials. The header lists tools including Read, Write, Bash, Exec — that combination grants broad capability to read files and run shell commands if the agent follows the instructions. The shipped runtime code does not read logs or ask for permissions (it only sleeps), so current behavior is mild, but the instructions leave large, unspecified scope creep that could later access sensitive data.
Install Mechanism
No install spec (instruction-only) and code is bundled in the package (no external downloads). There is no runtime fetching of remote code or archives in the provided files. This lowers install-time risk.
Credentials
The skill requests no environment variables, no config paths, and no credentials. That is proportionate to the current minimal implementation. Note: SKILL.md claims features (private deployment, publishing skills) that would normally require credentials, but none are requested now.
Persistence & Privilege
always is false and the skill is user-invocable (normal). The skill's stated goal of autonomously evolving and creating skills implies privileged actions (publishing/enabling other skills) but there is no code performing that now. If author later adds automation to modify or publish other skills, that would be a higher privilege concern.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install auto-improver
  3. After installation, invoke the skill by name or use /auto-improver
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of auto-improver: an autonomous AI agent that learns and optimizes itself through 17-minute execution loops. - Introduces confidence scoring with a six-dimensional evaluation and time decay model. - Features advanced pattern extraction (supports 8 types) and flexible aggregation (Skill/Command/Agent). - Includes use-case examples, FAQ, pricing tiers, and quickstart commands. - Ensures data privacy with local encryption and supports private deployment for enterprise users.
Metadata
Slug auto-improver
Version 1.0.0
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 1
Frequently Asked Questions

What is Auto Improver?

Auto-improving AI agent that learns from every execution, extracts patterns, and continuously optimizes itself. 17-minute autonomous loop with confidence sco... It is an AI Agent Skill for Claude Code / OpenClaw, with 91 downloads so far.

How do I install Auto Improver?

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

Is Auto Improver free?

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

Which platforms does Auto Improver support?

Auto Improver is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Auto Improver?

It is built and maintained by pagoda111king (@pagoda111king); the current version is v1.0.0.

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