Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Five composable primitives (typed memory, task files, project grouping, template schemas, heartbeat loops) for building self-directing agent workflows. Agents read their own task queue, execute work, record lessons, and loop without human prompting. Works with ClawVault as the storage layer.
Using typed memory directories and YAML-frontmatter task files keeps agent state queryable and human-readable without a separate database or sync layer.
When to use it
- Setting up a 30-minute cron loop that picks and runs the highest-priority task
- Tracking agent-discovered work as structured task files with priority and due dates
- Grouping tasks by client project so an agent stays scoped to one workstream
- Recording lessons during execution so the same mistake isn't repeated next run
- Building a shared vault where multiple agents coordinate without direct messaging
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: wire up my agent to work through its task backlog every 30 minutes
- 1Run `clawvault init` and create a project with `clawvault project add`
- 2Add tasks with owner, priority, and project fields via `clawvault task add`
- 3Add heartbeat instructions to HEARTBEAT.md: list open tasks, sort by priority and due date, pick top task
- 4Agent executes the task, then calls `clawvault task done <slug> --reason`
- 5If a lesson is learned during execution, agent calls `clawvault remember lesson`
Agent runs on schedule, drains its task queue in priority order, and builds a growing memory of decisions and lessons across cycles
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.