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OpenClaw

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.

dkistenev0 installations selon la source☆ 0 Étoiles du dépôtv1.0.4

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install dkistenev/inner-life-evolve

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

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...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/dkistenev/inner-life-evolve
Version
1.0.4
Commit
97baf8ae0573246ed3a93b97ad007ec5dd2fe277
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

SKILL.md
# 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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