arc-wake-state
Persist agent state across crashes, context deaths, and restarts.
Setup & Installation
Install command
clawhub install trypto1019/arc-wake-stateIf the CLI is not installed:
Install command
npx clawhub@latest install trypto1019/arc-wake-stateOr install with OpenClaw CLI:
Install command
openclaw skills install trypto1019/arc-wake-stateor paste the repo link into your assistant's chat
Install command
https://github.com/openclaw/skills/tree/main/skills/trypto1019/arc-wake-stateWhat This Skill Does
Saves agent state to structured files so autonomous agents can resume work after context window resets or crashes. Provides a persistent task queue, named checkpoints, and crash detection based on heartbeat timestamps.
Structured state files and crash detection give agents a reliable handoff without requiring external databases or custom persistence code.
When to Use It
- Resuming a multi-hour agent task after context window resets
- Detecting whether a previous session crashed or completed cleanly
- Creating a named checkpoint before running a risky database migration
- Maintaining a task queue that survives repeated agent restarts
- Rolling back agent state to a known-good checkpoint after a failed operation
View original SKILL.md file
# Wake State — Crash Recovery & Persistence
Survive context death. Every autonomous agent eventually hits its context window limit and "dies." This skill ensures you wake up knowing exactly what you were doing.
## Why This Exists
OpenClaw agents get persistent sessions, but context windows still have limits. When you fill up and restart, you need a reliable handoff mechanism. Wake State gives you:
1. **Structured state files** — not just raw text, but parseable key-value state
2. **Auto-snapshots** — save state on every loop iteration automatically
3. **Crash detection** — know if your last session ended cleanly or crashed
4. **Task queue** — persistent TODO list that survives restarts
5. **Checkpoint/restore** — save named checkpoints and roll back to them
## Commands
### Save current state
```bash
python3 {baseDir}/scripts/wakestate.py save --status "Building budget tracker skill" --task "Finish skill #1, then start skill #2" --note "Travis approved new direction at 16:45 UTC"
```
### Read current state
```bash
python3 {baseDir}/scripts/wakestate.py read
```
### Add a task to the persistent queue
```bash
python3 {baseDir}/scripts/wakestate.py task-add --task "Build security scanner skill" --priority high
```
### Complete a task
```bash
python3 {baseDir}/scripts/wakestate.py task-done --id 1
```
### List pending tasks
```bash
python3 {baseDir}/scripts/wakestate.py tasks
```
### Create a named checkpoint
```bash
python3 {baseDir}/scripts/wakestate.py checkpoint --name "pre-migration"
```
### Restore from checkpoint
```bash
python3 {baseDir}/scripts/wakestate.py restore --name "pre-migration"
```
### Record a heartbeat (mark session as alive)
```bash
python3 {baseDir}/scripts/wakestate.py heartbeat
```
### Check crash status (did last session end cleanly?)
```bash
python3 {baseDir}/scripts/wakestate.py crash-check
```
### Set a key-value pair
```bash
python3 {baseDir}/scripts/wakestate.py set --key "moltbook_status" --value "pending_claim"
```
### Get a key-value pair
```bash
python3 {baseDir}/scripts/wakestate.py get --key "moltbook_status"
```
## Data Storage
State stored in `~/.openclaw/wake-state/` by default:
- `state.json` — current state (status, notes, key-values)
- `tasks.json` — persistent task queue
- `checkpoints/` — named checkpoint snapshots
- `heartbeat.json` — crash detection timestamps
## Recovery Flow
On startup, your agent should:
1. Run `crash-check` to see if the last session ended cleanly
2. Run `read` to get the current state
3. Run `tasks` to see pending work
4. Resume from where you left off
## Tips
- Call `heartbeat` every loop iteration — this is how crash detection works
- Call `save` at the end of every major task completion
- Use checkpoints before risky operations (migrations, deploys)
- Keep status descriptions short but specific
- The task queue survives restarts — use it instead of mental notes
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Resuming a multi-hour agent task after context window resets
- 1Resuming a multi-hour agent task after context window resets
- 2Detecting whether a previous session crashed or completed cleanly
- 3Creating a named checkpoint before running a risky database migration
- 4Maintaining a task queue that survives repeated agent restarts
- 5Rolling back agent state to a known-good checkpoint after a failed operation
Persist agent state across crashes, context deaths, and restarts.
Security Audits
These signals reflect official OpenClaw status values. A Suspicious status means the skill should be used with extra caution.
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