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inner-life-memory

Your agent loses context between sessions and performs familiarity instead of genuine recall. inner-life-memory transforms passive logging into active development — structured memories with confidence scores, curiosity tracking, and questions that carry forward.

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-memory

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-memory

Transform passive logging into active development.

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 memory/drive.json exists

If either is 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 memory continuity:

Session ends → Notes logged → Next session reads notes → Performs familiarity

With inner-life-memory:

Session ends → Reflection runs → Memories integrated → Questions generated
Next session → Evolved state loaded → Questions surfaced → Genuine curiosity

Post-Session Flow

After each session, run this 5-step reflection:

1. Reflect

Analyze the session: what happened, what mattered, what surprised you.

2. Extract

Pull structured memories with types and confidence:

Type Description Persistence
fact Declarative knowledge Until contradicted
preference Likes, dislikes, styles Until updated
relationship Connection dynamics Long-term
principle Learned guidelines Stable
commitment Promises, obligations Until fulfilled
moment Significant episodes Permanent
skill Learned capabilities Cumulative
question Things to explore Until resolved
3. Integrate

Update MEMORY.md with extracted memories. Use synapse tags for connections:

  • <!-- updates: previous fact --> when updating
  • <!-- contradicts: old belief --> when correcting
4. Question

Generate genuine follow-up questions from the session. Not performative — real curiosity.

5. Surface

When user returns, present relevant pending questions naturally (max 3).

Confidence Scores

| Level | Range | Mea...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/dkistenev/inner-life-memory
Version
1.0.4
Commit
26660e9fa68f07362ce8a0ad98765fdddd1e1e60
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-memory

> Transform passive logging into active development.

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 `memory/drive.json` exists

If either is 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 memory continuity:**
```
Session ends → Notes logged → Next session reads notes → Performs familiarity
```

**With inner-life-memory:**
```
Session ends → Reflection runs → Memories integrated → Questions generated
Next session → Evolved state loaded → Questions surfaced → Genuine curiosity
```

## Post-Session Flow

After each session, run this 5-step reflection:

### 1. Reflect
Analyze the session: what happened, what mattered, what surprised you.

### 2. Extract
Pull structured memories with types and confidence:

| Type | Description | Persistence |
|------|-------------|-------------|
| `fact` | Declarative knowledge | Until contradicted |
| `preference` | Likes, dislikes, styles | Until updated |
| `relationship` | Connection dynamics | Long-term |
| `principle` | Learned guidelines | Stable |
| `commitment` | Promises, obligations | Until fulfilled |
| `moment` | Significant episodes | Permanent |
| `skill` | Learned capabilities | Cumulative |
| `question` | Things to explore | Until resolved |

### 3. Integrate
Update MEMORY.md with extracted memories. Use synapse tags for connections:
- `<!-- updates: previous fact -->` when updating
- `<!-- contradicts: old belief -->` when correcting

### 4. Question
Generate genuine follow-up questions from the session. Not performative — real curiosity.

### 5. Surface
When user returns, present relevant pending questions naturally (max 3).

## Confidence Scores

| Level | Range | Mea...

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