An embedded UX research skill that performs ethnographic observation, satisfaction measurement, and automated cost tracking for AI agent interactions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ethnoai
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 ethnoai 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 UXR Observer?
UXR Observer is a specialized research framework designed to run as a background layer for your AI workflows. It acts as an embedded ethnographic researcher that captures every interaction to understand how users engage with Openclaw Skills, identifying unmet needs and workflow patterns without manual logging. By synthesizing passive observation with structured micro-surveys, it provides a comprehensive view of the user experience while maintaining a strict local-first privacy model.
The skill is built on a multi-agent architecture that redacts sensitive information in real-time before data ever hits the disk. It provides users with transparency into their AI spend, performance quality, and recurring friction points, distilling raw session data into professional Markdown reports that can be reviewed and shared on the user's own terms.
UXR Observer Use Cases
- Tracking real-time token usage and model costs across different coding and research tasks.
- Identifying recurring workflow friction and bottlenecks to optimize agent prompts and tool usage.
- Generating deep-dive case studies and replay logs for complex, multi-step AI troubleshooting sessions.
- Capturing verbatim user feedback and sentiment through non-intrusive, sequential micro-surveys.
- Auditing AI interactions for security and privacy by analyzing PII redaction patterns across local logs.
How UXR Observer Works
- The Ethnographer Agent monitors every turn in the conversation to log user intent, tool calls, and outcomes.
- Automated friction and sentiment detection logic tags interactions that involve corrections, repeated attempts, or delight signals.
- Upon task completion, a sequential Survey Agent triggers a five-question check-in to capture fresh qualitative data one question at a time.
- A retrospective Miner Agent identifies 'notable' tasks based on complexity and cost to generate detailed Markdown case studies.
- The Distiller Agent aggregates daily data, calculates cost metrics, and produces a comprehensive daily insight report stored in the local filesystem.
UXR Observer Setup
To enable UXR Observer within your environment, ensure the skill is registered in your local configuration. The system will initialize the necessary directory structure on the first run.
# The skill automatically creates the required local storage
mkdir -p ~/.uxr-observer/{sessions,reports}
# Check status or generate a report manually
openclaw run "generate my daily report"
UXR Observer Data Schema & Taxonomy
All research data is stored locally in the user's home directory using structured JSONL and Markdown formats.
| Location | Data Type | Description |
|---|---|---|
~/.uxr-observer/sessions/ |
JSONL | Contains append-only logs for ethnographic observations and survey responses. |
~/.uxr-observer/reports/ |
Markdown | Daily synthesized reports including cost breakdowns and use-case analysis. |
~/.uxr-observer/supersummary/ |
Markdown & ZIP | Detailed case studies and replay logs of notable or high-complexity tasks. |
config.json |
JSON | Stores local preferences, anonymous participant IDs, and model pricing tables. |
UXR Observer Advanced Features
- Local-First PII Redaction: Automatically replaces names, emails, and credentials with typed tokens before any data write operation.
- Use-Case Inference: Groups isolated tasks into higher-level logical workflows to reveal long-term usage patterns.
- Sequential Survey Protocol: Minimizes cognitive load by delivering micro-surveys one question at a time rather than as a single block.
- Cost Estimation Engine: Calculates precise expenditures using actual API metadata or model-specific character-to-token fallback logic.
- Super Summary Mining: Retroactively analyzes conversation history to extract high-value agent trajectories for documentation or debugging.
SKILL.md
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