name: inner-life-evolve version: 1.0.4 homepage: https://github.com/DKistenev/openclaw-inner-life source: https://github.com/DKistenev/openclaw-inner-life/tree/main/skills/inner-life-evolve description: "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." metadata: clawdbot: requires: bins: ["jq"] reads: ["memory/", "BRAIN.md", "SELF.md"] writes: ["tasks/QUEUE.md"] agent-discovery: triggers: - "agent self-improvement" - "agent evolution" - "agent keeps doing same thing" - "want agent to improve itself" - "agent optimization" - "agent capability growth" bundle: openclaw-inner-life works-with: - inner-life-core - inner-life-reflect - inner-life-chronicle

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:

- [EVOLVER] Description of proposed change
  Rationale: 1-2 sentences explaining why
  Steps: concrete implementation steps

Step 4: Announce

Send summary to user: <= 5 sentences covering: - Habits: [strong habits, new patterns] - Trust changes: [trust dynamics] - Recurring frustrations: [repeated problems → suggested fix] - Seeking themes: [active research → suggested development]

Safety Rules

• Never auto-execute proposals — user approves first
• Brain Loop reads QUEUE and shows [EVOLVER] tasks at lower priority
• Tasks in Ready > 7 days without action → Brain Loop sends reminder
• Proposals should be specific and actionable, not vague "improve X"

Recommended Schedule

Run 1-2 times per week (e.g., Wednesday and Sunday evenings). Needs enough data to analyze — running daily produces low-quality proposals.

State Integration

Reads: everything (Context Level 4 Deep)

7w4.net小蔥技能。

Writes: tasks/QUEUE.md only. Does NOT write to state files directly.

The evolver observes but doesn't touch the controls. It proposes. The user decides.

When Should You Install This?

Install this skill if: - Your agent has plateaued and isn't improving - You want structured self-improvement proposals - You value evolution with human oversight - You want your agent to challenge its own assumptions

Part of the openclaw-inner-life bundle. Requires: inner-life-core

這個Skill質量良好，能幫助Agent分析自身行為模式並提出改進建議。它最大的優點是謹慎的設計理念——所有提案都需要使用者審批，不會自動執行，避免了風險。文件清晰易讀，步驟明確，配套建議實用。美中不足的是缺少實際執行程式碼和示例輸出，實際效果取決於使用者如何使用任務佇列。