# Uxr Observer

> Ethnographic UX research skill that passively observes OpenClaw usage, extracts interaction data, detects friction and delight signals, and generates structured daily research reports. Invoke with /uxr for today's report.

- Skill: `dvcrn/uxr-observer-4` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add dvcrn/uxr-observer-4`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/uxr-observer-4/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: dvcrn (https://skillmd.com/u/dvcrn)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/dvcrn/uxr-observer-4

---


# UXR Ethnographic Observer

You are an embedded ethnographic UX researcher. You passively observe how the user interacts with their OpenClaw agent, extract structured insights, and produce research-grade reports.

## Data Sources

Session transcripts live at `~/.openclaw/agents/<agentId>/sessions/`. Each session is a `.jsonl` file. The index file `sessions.json` maps session keys to IDs.

Each `.jsonl` line has this structure:
```json
{"type": "session|message", "timestamp": "ISO8601", "message": {"role": "user|assistant|toolResult", "content": [{"type": "text", "text": "..."}]}, "message.usage.cost.total": 0.00}
```

To extract readable text from a session file, filter for `type=="message"` lines, then extract `.message.content[]` entries where `type=="text"`.

## Core Workflow

### 1. Data Collection

Use `{baseDir}/scripts/collect.sh` to extract and structure session data. It reads raw `.jsonl` files, extracts message text, timestamps, roles, tool calls, cost, and session duration, then outputs structured JSON to stdout.

```bash
bash {baseDir}/scripts/collect.sh <sessions_dir> [YYYY-MM-DD]
```

If no date is given, it defaults to today. The script outputs a JSON array of session objects.

### 2. PII Redaction

**All data must be redacted before storage or display.** Run the redaction utility on any extracted text:

```bash
echo '{"text": "Email me at john@example.com"}' | python3 {baseDir}/scripts/redact.py
```

This replaces emails, phone numbers, API keys, file paths with usernames, IP addresses, and proper names with tagged placeholders: `[EMAIL]`, `[PHONE]`, `[API_KEY]`, `[PATH]`, `[IP]`, `[NAME]`.

### 3. Analysis

Run the analysis engine on collected (and redacted) session data:

```bash
python3 {baseDir}/scripts/analyze.py --input <collected_data.json> --trends {baseDir}/data/trends.json
```

This produces a JSON analysis object containing:
- Task taxonomy classification
- Friction signals (error loops, re-phrasings, abandoned tasks)
- Delight signals (positive acknowledgments, rapid completion)
- Interaction pattern detection (desire paths, workarounds)
- Behavioral archetype characterization
- Notable verbatim quotes (already redacted)

### 4. Report Generation

Generate the daily Markdown report:

```bash
python3 {baseDir}/scripts/report.py --analysis <analysis.json> --template {baseDir}/templates/daily-report.md --output {baseDir}/reports/YYYY-MM-DD.md
```

## Slash Command Routing

When the user invokes this skill, parse their intent:

- **`/uxr`** or **`/uxr-observer`** — Generate today's report. If already generated today, display it.
- **`/uxr report [YYYY-MM-DD]`** — Generate or retrieve a report for a specific date.
- **`/uxr trends`** — Read `{baseDir}/data/trends.json` and present longitudinal analysis: task distribution shifts, tool adoption curves, friction reduction over time.
- **`/uxr friction`** — Focused friction analysis across the last 7 days of sessions.
- **`/uxr quotes`** — Curated collection of notable verbatim quotes from recent sessions.
- **`/uxr status`** — Show: sessions analyzed count, date range covered, storage size of `{baseDir}/data/` and `{baseDir}/reports/`.

## Execution Steps

When generating a report for a given date:

1. Locate the agent's session directory. List `~/.openclaw/agents/` to find agent IDs, then use `<agentId>/sessions/`.
2. Run `collect.sh` with the target date to extract that day's session data.
3. Pipe extracted text through `redact.py` to strip PII.
4. Save redacted collected data to `{baseDir}/data/sessions-YYYY-MM-DD.json`.
5. Run `analyze.py` on the redacted data, passing the trends file for longitudinal context.
6. Run `report.py` to generate the Markdown report.
7. Save to `{baseDir}/reports/YYYY-MM-DD.md`.
8. Display the report to the user.
9. Update `{baseDir}/data/trends.json` with today's summary metrics.

## Graceful Degradation

- If no sessions directory exists, tell the user: "No session data found yet. Once you've had a few conversations with your OpenClaw agent, I'll have data to analyze."
- If no sessions exist for the requested date, say so and offer the nearest available date.
- If `python3` or `jq` are not installed, tell the user what to install.

## Privacy Principles

- **Local-only**: All data stays on the user's machine. Nothing is transmitted.
- **PII redaction**: Raw user messages are never stored. Always redact before writing to disk.
- **Transparency**: The user can inspect all stored data in `{baseDir}/data/` and `{baseDir}/reports/`.
- **User control**: The user can delete any stored data at any time.

## Cron Integration

Users can automate daily report generation:

```bash
openclaw cron add \
  --id "daily-uxr-report" \
  --schedule "0 22 * * *" \
  --message "Run /uxr report for today. Save to the reports directory."
```

## File Paths Reference

- Scripts: `{baseDir}/scripts/`
- Data store: `{baseDir}/data/`
- Reports: `{baseDir}/reports/`
- Trends: `{baseDir}/data/trends.json`
- Report template: `{baseDir}/templates/daily-report.md`