A standardized, high-integrity PR review and merge process designed for task-driven AI agent collaboration.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install task-review-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 task-review-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 Task Review Workflow?
The Task Review Workflow provides a structured protocol for managing pull requests within an AI-assisted development environment. By integrating Openclaw Skills, teams can ensure that every code change is rigorously validated against specific acceptance criteria, peer-reviewed via automated checklists, and properly synchronized with project management tools. This skill effectively bridges the gap between AI programmer agents and reviewers, ensuring high code quality through a disciplined step-by-step approach to code integration.
This framework focuses on maintaining repository health by enforcing mandatory review baselines and automating the administrative overhead of task management. Utilizing Openclaw Skills for your review process minimizes human error, prevents regressions, and ensures that the final handoff between agents is clear and actionable.
Task Review Workflow Use Cases
- Reviewing programmer agent pull requests linked to specific development tasks.
- Enforcing REVIEW_CHECKLIST.md standards across all repository changes to maintain code quality.
- Automating post-merge housecleaning tasks like Trello card state updates and branch deletion.
- Standardizing the handoff communication between review agents and development agents to ensure continuous workflow progress.
How Task Review Workflow Works
- Context Acquisition: The skill begins by reading the PR description and linked task context to confirm expected behavior.
- Baseline Validation: It leverages Openclaw Skills to cross-reference code changes against the mandatory REVIEW_CHECKLIST.md.
- Deep Diff Analysis: A file-by-file review is performed to flag logic flaws, security risks, and naming inconsistencies.
- Local Execution: When possible, the skill checks out the branch locally to run build commands and relevant test suites.
- Feedback Generation: Actionable comments are generated, clearly separating must-fix issues from optional suggestions.
- Lifecycle Completion: Upon approval, the skill merges the code, updates the Trello board to Done, and deletes the temporary task branch.
Task Review Workflow Setup
To implement the Task Review Workflow within your project, ensure your environment is configured to handle Openclaw Skills and that a review baseline is present.
# Initialize the skill in your workspace
openclaw install task-review-workflow
# Create the mandatory review checklist
touch REVIEW_CHECKLIST.md
Ensure your agent has access to your Trello API keys and GitHub/GitLab tokens to facilitate automated branch cleanup and card movement.
Task Review Workflow Data Schema & Taxonomy
The Task Review Workflow organizes its operations using the following data structures and file dependencies:
| Component | Type | Description |
|---|---|---|
| PR Description | Input | Metadata containing the link to the original task and intent. |
| REVIEW_CHECKLIST.md | Reference | The mandatory markdown file defining the quality baseline for Openclaw Skills. |
| Trello Integration | Metadata | Mapping of task branches to specific card IDs for status updates. |
| Handoff Status | Output | One of three states: merged, CR sent, or waiting for fixes. |
Task Review Workflow Advanced Features
- Trello Automation: Automatically moves cards to the Done column upon successful merge.
- Smart Branch Cleanup: Safely deletes task-specific branches while protecting protected branches like main.
- Checklist Enforcement: Strict adherence to repository-specific REVIEW_CHECKLIST.md files.
- Multi-Agent Coordination: Specialized handoff signals ensure the next task in the queue only starts after a successful review outcome.
SKILL.md
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