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total-recall

The only memory skill that watches on its own. No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.10/month. While other memory skills ask you to remember to remember, this one just pays attention.

gavdalf0 source installs☆ 0 Repository stars

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install gavdalf/total-recall

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

Total Recall — Autonomous Agent Memory

The only memory skill that watches on its own.

No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.10/month.

While other memory skills ask you to remember to remember, this one just pays attention.

Architecture

Layer 1: Observer (cron, every 15-30 min)
    ↓ compresses recent messages → observations.md
Layer 2: Reflector (auto-triggered when observations > 8000 words)
    ↓ consolidates, removes superseded info → 40-60% reduction
Layer 3: Session Recovery (runs on every /new or /reset)
    ↓ catches any session the Observer missed
Layer 4: Reactive Watcher (inotify daemon, Linux only)
    ↓ triggers Observer after 40+ new JSONL writes, 5-min cooldown
Layer 5: Pre-compaction hook (memoryFlush)
    ↓ emergency capture before OpenClaw compacts context

What It Does

  • Observer reads recent session transcripts (JSONL), sends them to an LLM, and appends compressed observations to observations.md with priority levels (high, medium, low)
  • Reflector kicks in when observations grow too large, consolidating related items and dropping stale low-priority entries
  • Session Recovery runs at session start, checks if the previous session was captured, and does an emergency observation if not
  • Reactive Watcher watches the session directory with inotify so high-activity periods get captured faster than the cron interval
  • Pre-compaction hook fires when OpenClaw is about to compact context, ensuring nothing is lost

Quick Start

1. Install the skill
clawdhub install total-recall
2. Set your API key

Add to your .env or OpenClaw config:

OPENROUTER_API_KEY=sk-or-v1-xxxxx
3. Run the setup script
bash skills/total-recall/scripts/setup.sh

This will:

  • Create the memory direc...

Source information

Source
github
Repository
openclaw/skills
Path
skills/gavdalf/total-recall
Version
—
Commit
728a29e40a67ce8eab08a24062412caf6af3c537
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

README

🧠 Total Recall — Autonomous Agent Memory

The only memory system that watches on its own.

No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.10/month.

While other memory skills ask you to remember to remember, this one just pays attention.

How It Works

Layer 1: Observer (cron, every 15 min)
    ↓ compresses recent messages → observations.md
Layer 2: Reflector (auto-triggered when observations > 8000 words)
    ↓ consolidates, removes superseded info → 40-60% reduction
Layer 3: Session Recovery (runs on every /new or /reset)
    ↓ catches any session the Observer missed
Layer 4: Reactive Watcher (inotify daemon, Linux only)
    ↓ triggers Observer after 40+ new JSONL writes, 5-min cooldown
Layer 5: Pre-compaction hook (memoryFlush)
    ↓ emergency capture before OpenClaw compacts context

Inspired by how human memory works during sleep — the hippocampus captures experiences, and during consolidation, important memories are strengthened while noise is discarded.

Install via ClawdHub

clawdhub install total-recall
bash skills/total-recall/scripts/setup.sh

Install from GitHub

git clone https://github.com/gavdalf/total-recall.git
cd total-recall
bash scripts/setup.sh

See SKILL.md for full documentation, configuration, and platform support.

What's Inside

Component Description
scripts/observer-agent.sh Compresses recent conversations via LLM
scripts/reflector-agent.sh Consolidates when observations grow large
scripts/session-recovery.sh Catches missed sessions on /new
scripts/observer-watcher.sh Reactive inotify trigger (Linux)
scripts/dream-cycle.sh Nightly memory consolidation (Dream Cycle)
scripts/staging-review.sh Review, approve, or reject Pa...

Extracted content

SKILL.md
# Total Recall — Autonomous Agent Memory

**The only memory skill that watches on its own.**

No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.10/month.

While other memory skills ask you to remember to remember, this one just pays attention.

## Architecture

```
Layer 1: Observer (cron, every 15-30 min)
    ↓ compresses recent messages → observations.md
Layer 2: Reflector (auto-triggered when observations > 8000 words)
    ↓ consolidates, removes superseded info → 40-60% reduction
Layer 3: Session Recovery (runs on every /new or /reset)
    ↓ catches any session the Observer missed
Layer 4: Reactive Watcher (inotify daemon, Linux only)
    ↓ triggers Observer after 40+ new JSONL writes, 5-min cooldown
Layer 5: Pre-compaction hook (memoryFlush)
    ↓ emergency capture before OpenClaw compacts context
```

## What It Does

- **Observer** reads recent session transcripts (JSONL), sends them to an LLM, and appends compressed observations to `observations.md` with priority levels (high, medium, low)
- **Reflector** kicks in when observations grow too large, consolidating related items and dropping stale low-priority entries
- **Session Recovery** runs at session start, checks if the previous session was captured, and does an emergency observation if not
- **Reactive Watcher** watches the session directory with inotify so high-activity periods get captured faster than the cron interval
- **Pre-compaction hook** fires when OpenClaw is about to compact context, ensuring nothing is lost

## Quick Start

### 1. Install the skill
```bash
clawdhub install total-recall
```

### 2. Set your API key
Add to your `.env` or OpenClaw config:
```bash
OPENROUTER_API_KEY=sk-or-v1-xxxxx
```

### 3. Run the setup script
```bash
bash skills/total-recall/scripts/setup.sh
```

This will:
- Create the memory direc...
README
# 🧠 Total Recall — Autonomous Agent Memory

**The only memory system that watches on its own.**

No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.10/month.

While other memory skills ask you to remember to remember, this one just pays attention.

## How It Works

```
Layer 1: Observer (cron, every 15 min)
    ↓ compresses recent messages → observations.md
Layer 2: Reflector (auto-triggered when observations > 8000 words)
    ↓ consolidates, removes superseded info → 40-60% reduction
Layer 3: Session Recovery (runs on every /new or /reset)
    ↓ catches any session the Observer missed
Layer 4: Reactive Watcher (inotify daemon, Linux only)
    ↓ triggers Observer after 40+ new JSONL writes, 5-min cooldown
Layer 5: Pre-compaction hook (memoryFlush)
    ↓ emergency capture before OpenClaw compacts context
```

Inspired by how human memory works during sleep — the hippocampus captures experiences, and during consolidation, important memories are strengthened while noise is discarded.

## Install via ClawdHub

```bash
clawdhub install total-recall
bash skills/total-recall/scripts/setup.sh
```

## Install from GitHub

```bash
git clone https://github.com/gavdalf/total-recall.git
cd total-recall
bash scripts/setup.sh
```

See [SKILL.md](SKILL.md) for full documentation, configuration, and platform support.

## What's Inside

| Component | Description |
|-----------|-------------|
| `scripts/observer-agent.sh` | Compresses recent conversations via LLM |
| `scripts/reflector-agent.sh` | Consolidates when observations grow large |
| `scripts/session-recovery.sh` | Catches missed sessions on /new |
| `scripts/observer-watcher.sh` | Reactive inotify trigger (Linux) |
| `scripts/dream-cycle.sh` | Nightly memory consolidation (Dream Cycle) |
| `scripts/staging-review.sh` | Review, approve, or reject Pa...

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