A lightweight workflow system for managing multi-phase, interruptible AI agent tasks with robust state recovery.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install spec-steering-workflow
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install spec-steering-workflow using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Spec Steering Workflow?
The Spec Steering Workflow is designed to handle complex, high-stakes tasks that require more than a single interaction to complete. By implementing a lightweight spec and steering framework, this skill allows AI agents to manage projects that are long-running, interruptible, or require staged execution. It is a vital component of the Openclaw Skills ecosystem for users who need their AI agents to maintain state and context across multiple sessions.
This skill moves beyond simple command execution by introducing a structured approach to project management. It ensures that an agent can research, plan, and execute while providing regular progress updates. By utilizing a dedicated steering directory and specific checkpoint types, the workflow guarantees that work is never lost and can be resumed accurately even after significant interruptions.
Spec Steering Workflow Use Cases
- Managing tasks expected to exceed 20 minutes of processing time.
- Projects involving multiple deliverables that require a staged rollout.
- Scenarios where an agent must perform deep research before starting the execution phase.
- Ensuring task continuity and state recovery across different chat sessions.
- Workflows where the user requires a formal plan for approval before the agent begins work.
How Spec Steering Workflow Works
- Preliminary Configuration: The agent begins by reading global steering rules and user preferences to align with established constraints.
- Specification Initialization: For new tasks, the agent uses the specctl.py tool to initialize a unique specification ID and title.
- Task Batching: The project is broken down into manageable batches, focusing only on the current phase to maintain high quality.
- Periodic Checkpointing: Every 20-30 minutes, or upon batch completion, the agent records a status (done, blocked, or failed) to secure progress.
- Continuity Management: Before concluding a session, the agent updates handoff documentation and metadata to facilitate seamless resumption in the next session.
Spec Steering Workflow Setup
To integrate this skill into your environment, ensure the utility scripts are available and follow these steps:
# Initialize a new specification
python scripts/specctl.py init <spec-id> --title "Project Title" --kind <kind>
# Check the status of current tasks
python scripts/specctl.py status <spec-id>
# Run a system diagnostic to ensure all references are correctly mapped
python scripts/specctl.py doctor
This setup allows you to leverage the full suite of Openclaw Skills for professional-grade project steering.
Spec Steering Workflow Data Schema & Taxonomy
The skill organizes data within a specific directory hierarchy to maintain a clean workspace:
| Location | File/Folder | Purpose |
|---|---|---|
specs/active/ |
<spec-id>/ |
Active project state and task tracking. |
steering/ |
workflow.md |
Core operational rules and logic. |
steering/ |
preferences.md |
User-defined stylistic or technical preferences. |
workspace/ |
handoff.md |
Summary of work completed and next steps for recovery. |
workspace/ |
meta.json |
Technical state metadata for automated parsing. |
Spec Steering Workflow Advanced Features
- Multi-session recovery using the resume command to pick up exactly where the agent left off.
- Automated validation of specification structures to ensure compliance with file contracts.
- Integrated handoff protocols that utilize meta.json for machine-readable state transitions.
- Support for custom templates located in the assets directory to standardize project documentation.
- Advanced archiving capabilities to clean up the active workspace while preserving project history.
SKILL.md
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