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在 OpenClaw 中安装
/install super-self-improving
功能描述
超级自我优化智能体 - 多模态记忆、反馈循环、元学习、置信度校准 / Super Self-Improving Agent - Multi-modal Memory, Feedback Loops, Meta-Learning, Confidence Calibration
安全使用建议
This skill conceptually fits a self‑improving memory agent, but there are mismatches you should resolve before installing or running it: 1) The SKILL.md expects persistent files in ~/.super-self-improving and CLI commands (e.g., super-self-improving) yet the package declares no config paths or binaries — ask the publisher which files will be created and inspect them. 2) Token monitoring and scheduling features imply access to usage/billing data or orchestration credentials; confirm what data sources it will read and whether any credentials are required. 3) The skill promises not to store sensitive data, but that is not enforceable — avoid letting it store any secrets, or run it in a sandboxed account. 4) Because no code is included, do not install third‑party code to satisfy referenced commands unless you review its source. If you decide to proceed, run in an isolated environment, inspect any created files under ~/.super-self-improving, and verify there are no unexpected network endpoints or credential reads.
功能分析
Type: OpenClaw Skill
Name: super-self-improving
Version: 1.1.0
The skill bundle defines a framework for an AI agent to implement self-improvement through local memory, feedback loops, and performance tracking. It instructs the agent to manage data within a local directory (~/.super-self-improving/) to store user preferences, error logs, and performance metrics. No malicious code, data exfiltration patterns, or harmful prompt-injection instructions were identified in SKILL.md or README.md.
能力评估
Purpose & Capability
The declared purpose (self‑improving agent with memory, feedback loops, meta‑learning and confidence calibration) is consistent with the prose in SKILL.md. However, the skill references persistent storage under ~/.super-self-improving and CLI commands (e.g., super-self-improving stats) while the registry metadata declares no required config paths or binaries. That mismatch is unexpected: a memory/monitoring skill legitimately needs a place to store data and potentially tooling, but those requirements are not declared.
Instruction Scope
SKILL.md instructs storing and reading memory files (hot.md, preferences.md, etc.) under the user's home directory and defines workflows that imply reading session/context and tracking token consumption and performance metrics. The instructions do not explicitly ask for unrelated system files or credentials, but they do assume persistent local file I/O and implicit access to usage/token metrics. The guidance 'do not store sensitive information' is advisory only — there is no enforcement or mechanism described to prevent sensitive data from being written or transmitted.
Install Mechanism
This is an instruction‑only skill with no install spec and no code files packaged. That minimizes immediate supply‑chain risk because nothing is downloaded or executed by an installer. However, the README references a python script (super_self_improving.py) and CLI commands which are not included; if a user later installs code from an external source to satisfy these commands, that introduces risk not captured here.
Credentials
The skill declares no required environment variables or credentials, yet it describes token monitoring, cost estimation, and agent scheduling/auto‑scaling features that in practice often require access to provider APIs, billing info, or orchestration credentials. The absence of declared env vars/config paths but the presence of features that normally need them is an inconsistency. Also, persistent memory in the user's home could unintentionally capture secrets even if the skill 'promises' not to.
Persistence & Privilege
The skill intends to persist data under ~/.super-self-improving (detailed directory layout and files). The registry metadata lists no required config paths, so this persistence is an implicit privilege not declared up front. The skill does not request always:true or attempt to change other skills/settings, which reduces the privilege concerns, but users should be aware it expects to create and maintain local persisted state.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install super-self-improving - 安装完成后，直接呼叫该 Skill 的名称或使用
/super-self-improving触发 - 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
v1.1.0
Version 1.1.0 introduces token monitoring and agent scheduling optimization.
- Added "Token Monitoring" section with real-time token usage tracking, alerts, trends, and cost estimation.
- Introduced "Agent Scheduling Optimization" with features like intelligent task allocation, load balancing, auto scaling, and performance optimization.
- Expanded metrics and rules for both token monitoring and scheduling strategies.
- No changes to core mechanisms or fundamental workflows.
v1.0.0
Initial release of the enhanced super-self-improving agent:
- Adds multi-modal memory (text, code, style, tool usage, metrics).
- Integrates explicit, implicit, and synthetic feedback loops.
- Implements meta-learning for strategy selection and dynamic adjustment.
- Introduces confidence calibration with prediction accuracy tracking.
- Expands error analysis: categorization, root cause, and prevention.
- Provides detailed directory structure, workflow, and performance metrics.
元数据
常见问题
Super Self Improving 是什么？
超级自我优化智能体 - 多模态记忆、反馈循环、元学习、置信度校准 / Super Self-Improving Agent - Multi-modal Memory, Feedback Loops, Meta-Learning, Confidence Calibration. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 645 次。
如何安装 Super Self Improving？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install super-self-improving」即可一键安装，无需额外配置。
Super Self Improving 是免费的吗？
是的，Super Self Improving 完全免费（开源免费），可自由下载、安装和使用。
Super Self Improving 支持哪些平台？
Super Self Improving 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 Super Self Improving？
由 BOMBFUOCK（@bombfuock）开发并维护，当前版本 v1.1.0。
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