Overview
- Skill Key
- dkistenev/inner-life-evolve
- Author
- dkistenev
- Source Repo
- openclaw/skills
- Version
- 1.0.4
- Source Path
- skills/dkistenev/inner-life-evolve
- Latest Commit SHA
- 97baf8ae0573246ed3a93b97ad007ec5dd2fe277
Official OpenClaw
rules
36%
inner-life-evolve
Your agent does the same things the same way forever. inner-life-evolve analyzes patterns, challenges assumptions, and proposes improvements — writing proposals to the task queue for user approval. Never auto-executes. Evolution with a safety net.
Stars
0
Installs
0
Status
ACTIVE
Visibility
PUBLIC
安装方式
直接复制以下提示词，发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店，若未安装，请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店，然后安装 inner-life-evolve 技能。 若已安装，则直接安装 inner-life-evolve 技能。
Extracted Content
SKILL.md excerpt
# inner-life-evolve > Evolution is not optional. But it requires permission. Requires: **inner-life-core** ## Prerequisites Check Before using this skill, verify that inner-life-core has been initialized: 1. Check that `memory/inner-state.json` exists 2. Check that `BRAIN.md` exists 3. Check that `tasks/QUEUE.md` exists If any are missing, tell the user: *"inner-life-core is not initialized. Install it with `clawhub install inner-life-core` and run `bash skills/inner-life-core/scripts/init.sh`."* Do not proceed without these files. ## What This Solves Without evolution, agents plateau. They find a way that works and repeat it forever — even as the world changes. inner-life-evolve analyzes your agent's patterns, challenges its assumptions, and writes concrete improvement proposals. But it never auto-executes — you approve first. ## How It Works ### Step 1: Deep Context Read (Context Level 4) Read everything: - AGENTS.md, TOOLS.md, BRAIN.md, SELF.md - `memory/week-digest.md` (NOT individual diaries — use digest) - `memory/habits.json` — habits + user patterns - `memory/drive.json` — seeking, avoidance - `memory/relationship.json` — trust, lessons - `memory/inner-state.json` — emotions, frustrations ### Step 2: Challenge Assumptions For each potential improvement, structure thinking: ``` Assumption: [what we currently believe/do] Is it true? [evidence for/against] What if false? [alternative approach] New proposal: [concrete change] ``` Look for: - **Recurring frustrations** → systemic solutions (not patches) - **Stale habits** → habits with declining strength or unused for weeks - **Trust dynamics** → areas where trust has grown but behavior hasn't adapted - **Seeking themes** → research interests that could become capabilities - **Avoidance patterns** → things the agent avoids that might be valuable ### Step 3: Write Proposals to QUEUE Write proposals to `tasks/QUEUE.md` under the Ready section: ```markdown - [EVOLVER] Description of proposed change...
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