A situational awareness skill that monitors upstream dependencies and downstream consumers for changes that could break your current work.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install peripheral-vision
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 peripheral-vision 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 Peripheral Vision?
Peripheral Vision is a sophisticated situational awareness skill designed for AI coding agents. It addresses a common developer pain point: code breaks not because of the file currently being edited, but because of changes in the surrounding ecosystem. By establishing an awareness model based on human vision zones (Foveal, Parafoveal, and Peripheral), this addition to your Openclaw Skills library monitors imports, exports, and transitive dependencies to alert you to drift before it causes a crash.
The skill creates a protective field around your current work context. It continuously scans for interface changes, behavioral shifts, and schema tremors in the files you aren't looking at but rely on. By integrating this into your workflow, you ensure that your code remains compatible with the evolving state of the larger repository, significantly reducing the time spent debugging unexpected integration issues.
Peripheral Vision Use Cases
- Establishing a baseline awareness field when starting a new feature branch to identify potential conflicts.
- Running a pre-commit check to ensure that parallel changes in sibling modules haven't invalidated your logic.
- Investigating unexpected CI/CD failures by identifying peripheral changes that occurred during your development cycle.
- Monitoring database schema migrations or API contract updates that impact your current module in real-time.
How Peripheral Vision Works
- Context Capture: The skill identifies the foveal zone by looking at currently modified files and traces the parafoveal and peripheral zones through dependency mapping.
- Change Detection: It scans six distinct channels—including upstream drift and downstream pressure—detecting changes in Git commits, schemas, and configurations.
- Alert Triage: Detected changes are scored by impact probability and categorized by urgency (STOP, REVIEW, NOTE, or EMERGING).
- Continuous Monitoring: The awareness field is updated periodically as the developer's focus shifts, ensuring the situational model remains accurate.
Peripheral Vision Setup
To add this capability to your agent, use the following command within your environment configured for Openclaw Skills:
# Install the peripheral-vision skill
openclaw install peripheral-vision
# Initialize the awareness field for your current project
openclaw run peripheral-vision --init
Peripheral Vision Data Schema & Taxonomy
The skill organizes its monitoring data into several logical zones and channels to provide a structured view of the system's state:
| Zone | Scope | Monitoring Focus |
|---|---|---|
| Foveal | Active Files | Current modifications and local logic |
| Parafoveal | Direct Dependencies | Immediate imports, exports, and calls |
| Peripheral | Transitive Deps | Shared resources, upstream providers, and consumers |
| Blind Spot | Implicit Connections | Shared databases, environment variables, and co-change history |
It maintains a metadata taxonomy for alerts, classifying them by source (e.g., [Schema], [Upstream], [Temporal]) and providing actionable instructions for resolution.
Peripheral Vision Advanced Features
- Temporal Neighbor Analysis: Uses co-change analysis to identify files that historically change together, even without explicit code dependencies.
- Six-Channel Scanning: Monitors upstream drift, downstream pressure, sibling mutations, schema tremors, environmental shifts, and temporal neighbors.
- Impact Probability Scoring: Filters out noise by only alerting you to changes with a high likelihood of affecting your current work.
- Multi-Layered Alerting: Provides contextual stop/review/note statuses with specific implications for your code, such as 'your INSERT will fail' or 'update your imports'.
SKILL.md
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