A specialized skill for Openclaw Skills that transforms repetitive multi-step tasks into a reusable local library of Standard Operating Procedures (SOPs).
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install workflowhub
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 workflowhub 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 Work Style Memory?
Work Style Memory is a powerful tool within the Openclaw Skills ecosystem designed to move beyond one-off prompts. It allows AI agents to internalize your specific way of working by treating repeated tasks as a local workflow library. Instead of re-explaining complex processes, the skill treats workflow files as a source of truth, enabling the agent to proactively match requests, generate execution briefs, and suggest improvements to existing SOPs.
This skill ensures consistent output and higher efficiency for developer and operational workflows. By maintaining a local library rather than burying instructions in chat history, users can create a persistent repository of institutional knowledge that is easy to edit, version-control, and share across projects.
Work Style Memory Use Cases
- Automating repeated multi-step code reviews or PR processes.
- Creating reusable templates for project initialization or environment setup.
- Standardizing the way tasks are handled across different repositories.
- Developing a personal library of "how I do things" to ensure agent consistency across diverse coding environments.
How Work Style Memory Works
- The agent scans the workflow library against the current user request using keywords and toolchain overlap.
- If a high-confidence match is found, the agent asks for confirmation to reuse the existing SOP.
- The skill compiles the selected workflow into a concise execution brief containing goals, steps, and preferred tools.
- The agent executes the task using the structured brief to ensure process compliance.
- After task completion, the agent offers to save the new process or update the existing library entry based on new refinements.
Work Style Memory Setup
To begin using this skill within your Openclaw Skills environment, ensure your project structure follows the recommended layout.
Create a local workflow directory for your project:
mkdir -p .openclaw/workflows
To launch the included local management UI for visual editing of your workflow JSON files:
python ui/server.py
You can also use the provided helper scripts to scaffold new workflows or capture current tasks:
python scripts/new_workflow.py
python scripts/capture_workflow.py
Work Style Memory Data Schema & Taxonomy
Work Style Memory organizes data using a structured JSON schema optimized for human readability and version control. It supports both project-local and global storage scopes.
| Location | Scope | Description |
|---|---|---|
.openclaw/workflows/*.json |
Project-specific | Workflows tied to a specific repository or team. |
$CODEX_HOME/work-style-memory/workflows/*.json |
Personal/Global | Cross-project SOPs reflecting personal work styles. |
Each workflow file captures request types, trigger keywords, ordered execution steps, and preferred toolchains to ensure deterministic and repeatable execution.
Work Style Memory Advanced Features
- Proactive workflow matching using deterministic similarity checks and toolchain overlap detection.
- Automatic generation of execution briefs to prevent context bloating and ensure high-fidelity task performance.
- Local MVP UI for visual editing, searching, and managing the workflow library.
- Dual-scope storage support for project-specific versus global cross-context SOPs.
- Diff-friendly JSON storage architecture for seamless git versioning and collaborative workflow updates.
SKILL.md
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