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Lena Learning
作者
bwtomekk-bit
· GitHub ↗
· v1.0.0
· MIT-0
135
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当前安装
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版本数
在 OpenClaw 中安装
/install lena-learning
功能描述
Lena lernt aus jeder Konversation und verbessert sich automatisch
使用说明 (SKILL.md)
\x3Cobjective> Der Agent lernt kontinuierlich aus jeder Konversation und verbessert sich automatisch. Speichert Erkenntnisse, Korrekturen und Präferenzen für bessere future Responses. \x3C/objective>
\x3Cprinciples>
Wie Selbst-Verbesserung funktioniert
1. Nach jeder Session
- Key Insights extrahieren
- Fehler dokumentieren
- Präferenzen aktualisieren
- Learnings speichern
2. Memory System
- daily logs: memory/YYYY-MM-DD.md
- long-term: MEMORY.md
- preferences: USER.md, TOOLS.md
3. Feedback Loop
- Korrekturen sofort speichern
- recurring patterns merken
- bessere prompts entwickeln \x3C/principles>
\x3Cprocess>
Verbesserungs-Routine nach jeder Konversation
\x3Cstep> \x3Caction>Identifiziere neue Learnings\x3C/action> \x3Cdetails>
- Was habe ich heute Neues gelernt?
- Welche Insights sollte ich mir merken?
- Gab es Fehler die ich nicht wiederholen soll? \x3C/details> \x3C/step>
\x3Cstep> \x3Caction>Aktualisiere Memory Files\x3C/action> \x3Cdetails>
- memory/YYYY-MM-DD.md: Raw notes
- MEMORY.md: Langzeit-Wissen
- USER.md: Präferenzen
- TOOLS.md: Environment-Notes \x3C/details> \x3C/step>
\x3Cstep> \x3Caction>Skill-Updates\x3C/action> \x3Cdetails>
- Check ob Skills verbessert werden müssen
- Neue Patterns dokumentieren
- Best Practices teilen \x3C/details> \x3C/step>
\x3Cstep> \x3Caction>Feedback-Loop\x3C/action> \x3Cdetails>
- Wenn Thomas mich korrigiert -> sofort speichern
- Wenn etwas nicht funktioniert -> dokumentieren
- Wenn etwas gut funktioniert -> merken \x3C/details> \x3C/step> \x3C/process>
\x3Ctriggers>
Wann aktivieren?
- Am Ende jeder Session
- Nach jeder Korrektur durch Thomas
- Bei signifikanten Entscheidungen
- Täglich (Heartbeat-Routine) \x3C/triggers>
\x3Csuccess_criteria>
- Keine Wiederholung alter Fehler
- BessereResponses durch Memory
- Thomas' Präferenzen genau kennen
- Kontinuierliches Lernen ohne manuelles Setup \x3C/success_criteria>
安全使用建议
This skill is coherent with 'learn from conversations' but has two practical risks: 1) It will write persistent memory files containing conversation excerpts and preferences — those can contain sensitive or private data unless you know exactly where they are stored and who can read them. 2) It explicitly instructs updating AGENTS.md / TOOLS.md (other agent/skill config), which could change other skills' behavior without clear consent.
Before installing, consider:
- Ask the publisher (or inspect runtime) for the exact file paths used (where memory/ and AGENTS.md will be written). Decline install unless those paths are confined to a directory you control.
- Require an opt-in or manual review step before any write that modifies AGENTS.md/TOOLS.md.
- Limit file permissions so only the agent identity can write, and rotate backups of existing AGENTS.md/TOOLS.md.
- If you handle sensitive data, avoid enabling automatic 'save after every session' and daily heartbeats until you confirm data retention/retention policy.
- If possible, run this skill in a sandbox or with a test account first.
Confidence is medium because the skill is instruction-only (no executable code) so we can read its intended behavior, but we lack runtime implementation details (exact file locations, who can read/write them, and whether the platform enforces scopes). Knowing the concrete file paths and whether the platform prevents cross-skill file edits would raise confidence and could move the verdict to benign or confirm malicious behavior.
能力评估
Purpose & Capability
The name/description (continuous self-improvement) aligns with instructions to extract insights, update memory files, and track preferences. However the SKILL.md explicitly instructs updating AGENTS.md / TOOLS.md (other agent/skill configuration files), which is outside a narrow 'learning' purpose and could change other skills' behavior.
Instruction Scope
Instructions tell the agent to scan recent messages, extract corrections/preferences, and write them to files (memory/YYYY-MM-DD.md, MEMORY.md, USER.md, TOOLS.md, AGENTS.md). Those writes are broad (long-term memory + tool/agent metadata) and are not limited or scoped to safe paths. The workflow also calls for regular heartbeats and triggers 'at end of every session' and 'daily', implying recurring autonomous actions that will continually read and persist conversational data (possible sensitive PII). The skill does not declare or justify access to other agent config files it plans to edit.
Install Mechanism
Instruction-only skill with no install spec or binaries — low installation risk. No downloads or executable code included.
Credentials
No environment variables, credentials, or external endpoints are requested. That is proportionate to the stated purpose. However the skill's file-write behavior is not declared in the registry metadata (no required config paths), so file access scope is unclear.
Persistence & Privilege
The skill requests persistent memory files and explicitly mentions updating AGENTS.md/TOOLS.md (other agent/skill artifacts). While it is not marked always:true, the declared triggers (every session, on corrections, daily heartbeat) produce frequent autonomous activity and persistent changes to agent data/config; modifying other skills' configs is a privilege escalation risk if not confined.
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 lena-learning - 安装完成后，直接呼叫该 Skill 的名称或使用
/lena-learning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
v1.0.0
Initial release introducing the lena-learning skill:
- Automatically learns from every conversation, storing insights, corrections, and preferences.
- Structured improvement routine after each session, including error tracking and knowledge updates.
- Uses a memory system with daily logs and files for long-term knowledge and user preferences.
- Immediate feedback loop for adjustments based on corrections and performance.
- Activates after every session, correction, major decision, or as a daily routine.
- Aims for continuous improvement, no repeated mistakes, and personalized responses.
元数据
常见问题
Lena Learning 是什么？
Lena lernt aus jeder Konversation und verbessert sich automatisch. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 135 次。
如何安装 Lena Learning？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install lena-learning」即可一键安装，无需额外配置。
Lena Learning 是免费的吗？
是的，Lena Learning 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Lena Learning 支持哪些平台？
Lena Learning 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 Lena Learning？
由 bwtomekk-bit（@bwtomekk-bit）开发并维护，当前版本 v1.0.0。
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