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QMD Learning Loop
作者
Shaker Gilbert
· GitHub ↗
· v1.0.1
· MIT-0
118
总下载
0
收藏
0
当前安装
2
版本数
在 OpenClaw 中安装
/install qmd-learning-loop
功能描述
Capture corrections, errors, feature requests, and recurring best practices in a QMD-native way for markdown-first agent workspaces. Use when a user corrects...
安全使用建议
This skill is a set of editorial guidelines and templates for capturing and promoting learnings in your markdown workspace. It does not request credentials or install software. The only operational implication is that an agent using this skill will be reading and writing workspace markdown files — so: (1) ensure the agent's file-write permissions are limited to the intended workspace, (2) keep your workspace backed by version control so edits can be reviewed/reverted, (3) review any promoted/created documents for accidental inclusion of secrets or sensitive data, and (4) if you need stricter control, disable autonomous invocation or require manual approval before the agent edits durable docs.
功能分析
Type: OpenClaw Skill
Name: qmd-learning-loop
Version: 1.0.1
The qmd-learning-loop skill bundle establishes a framework for an AI agent to autonomously update its own documentation and behavioral guidelines (e.g., SOUL.md, AGENTS.md, TOOLS.md) based on user interactions and errors. While designed for organizational efficiency and 'durable memory,' the instructions in SKILL.md and references/promotion-targets.md create a high-risk mechanism for persistent prompt injection by allowing external input to be promoted into core operating instructions. There is no evidence of intentional malice, obfuscation, or data exfiltration, but the architectural capability for self-reprogramming based on untrusted user 'corrections' represents a significant security vulnerability.
能力评估
Purpose & Capability
Name/description, SKILL.md, and the five reference markdown files all describe and implement the same goal: classify events and route/promote them into workspace docs. There are no unrelated requirements (no env vars, binaries, or external endpoints) that would contradict the stated purpose.
Instruction Scope
Runtime instructions tell the agent to read, classify, and update workspace markdown files (logs, runbooks, decision memos, etc.). That scope is appropriate for a learning-loop skill. The SKILL.md explicitly prefers updating existing docs and keeping chronology separate from durable rules. There is nothing instructing the agent to read unrelated system files, fetch secrets, or transmit data externally.
Install Mechanism
This is an instruction-only skill with no install spec or code files to execute; nothing will be downloaded or written by an installer step. Risk from installation is minimal.
Credentials
No environment variables, credentials, or config paths are requested. The skill operates solely on workspace markdown documents, which is proportionate to its purpose.
Persistence & Privilege
The skill does not request always:true and uses platform defaults (agent-invocable and allowed to invoke autonomously). That is normal for a skill that will edit workspace files. It does not attempt to modify other skills' configs or system-wide settings.
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 qmd-learning-loop - 安装完成后，直接呼叫该 Skill 的名称或使用
/qmd-learning-loop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
v1.0.1
- Broadened skill description to support all markdown-first agent workspaces, not just Atlas/OpenClaw.
- Generalized documentation and routing guidance for greater compatibility with various workspace structures.
- Updated references from specific Atlas/OpenClaw file paths to more general file categories (e.g., "workflow guidance file" instead of "AGENTS.md").
- Simplified examples to avoid assuming the presence of a particular directory structure.
- No code or functional changes; documentation, terminology, and instructions only.
v1.0.0
Initial release of qmd-learning-loop — a QMD-native workflow for capturing and promoting operational learnings:
- Added core workflow and first-/second-pass routing rules for classifying and promoting corrections, errors, feature requests, and best practices.
- Provided specific guidelines for targeting and updating durable docs (e.g., AGENTS.md, TOOLS.md, SOUL.md) without duplicating policy.
- Included criteria for promoting recurring or policy-changing lessons versus logging one-off events in daily memory.
- Added references for routing, promotion, review loop, and starter templates to support the learning capture process.
元数据
常见问题
QMD Learning Loop 是什么？
Capture corrections, errors, feature requests, and recurring best practices in a QMD-native way for markdown-first agent workspaces. Use when a user corrects... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 118 次。
如何安装 QMD Learning Loop？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install qmd-learning-loop」即可一键安装，无需额外配置。
QMD Learning Loop 是免费的吗？
是的，QMD Learning Loop 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
QMD Learning Loop 支持哪些平台？
QMD Learning Loop 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 QMD Learning Loop？
由 Shaker Gilbert（@shakerg）开发并维护，当前版本 v1.0.1。
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