CLI Reference

slopit provides a command-line interface for running analysis without writing Python code.

Installation

The CLI is installed automatically with the slopit package:

pip install slopit

Verify installation:

slopit --version

Commands

slopit analyze

Analyze sessions for AI-assisted responses.

slopit analyze INPUT_PATH [OPTIONS]

Arguments:

• INPUT_PATH: Path to a session file or directory of files

Options:

Option	Short	Default	Description
--output	-o	None	Output file for JSON results
--analyzers	-a	keystroke,focus,timing,paste	Comma-separated list of analyzers
--aggregation		weighted	Flag aggregation strategy
--summary	-s	False	Print summary table only
--csv		None	Export results to CSV file

Examples:

# Analyze all files in a directory
slopit analyze data/

# Print summary only
slopit analyze data/ --summary

# Save results to JSON
slopit analyze data/ --output results.json

# Export to CSV
slopit analyze data/ --csv results.csv

# Use specific analyzers
slopit analyze data/ --analyzers keystroke,focus

# Change aggregation strategy
slopit analyze data/ --aggregation majority

# Combine options
slopit analyze data/ \
  --analyzers keystroke,focus,timing \
  --aggregation weighted \
  --output results.json \
  --csv summary.csv

Output:

Without --summary, prints a full report:

============================================================
slopit Analysis Report
============================================================

Sessions Analyzed: 100
  Flagged:    12 (12.0%)
  Suspicious: 8 (8.0%)
  Clean:      80 (80.0%)

------------------------------------------------------------
Flagged Sessions
------------------------------------------------------------

Session: session_042
  Status: flagged (confidence: 0.85)
  Flags:
    - [keystroke] low_iki_variance: Keystroke timing unusually consistent
    - [focus] blur_paste_pattern: Paste event detected shortly after tab switch

With --summary, prints a compact table:

         Analysis Summary
┏━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
┃ Status     ┃ Count ┃ Percentage ┃
┡━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
│ Flagged    │    12 │      12.0% │
│ Suspicious │     8 │       8.0% │
│ Clean      │    80 │      80.0% │
└────────────┴───────┴────────────┘

slopit validate

Validate session files against the slopit schema.

slopit validate INPUT_PATH

Arguments:

• INPUT_PATH: Path to a session file or directory of files

Examples:

# Validate a single file
slopit validate data/session.json

# Validate all files in a directory
slopit validate data/

Output:

✓ data/session_001.json: 1 session(s) valid
✓ data/session_002.json: 1 session(s) valid
✗ data/session_003.json: Invalid schemaVersion: expected "1.0"

Summary: 2 valid, 1 invalid

slopit report

Generate a report from previously saved analysis results.

slopit report RESULTS_PATH [OPTIONS]

Arguments:

• RESULTS_PATH: Path to a JSON results file from slopit analyze --output

Options:

Option	Short	Default	Description
--format	-f	text	Output format: text or csv
--output	-o	None	Output file path

Examples:

# Print text report to console
slopit report results.json

# Save text report to file
slopit report results.json --output report.txt

# Export to CSV (requires --output)
slopit report results.json --format csv --output summary.csv

slopit dashboard

Start the slopit analytics dashboard server.

slopit dashboard [OPTIONS]

Options:

Option	Default	Description
--host	127.0.0.1	Host address to bind to
--port	8000	Port number to listen on
--reload	False	Enable auto-reload for development
--data-dir	./data	Directory for session data storage

Examples:

# Start on default port (localhost:8000)
slopit dashboard

# Start on all interfaces, custom port
slopit dashboard --host 0.0.0.0 --port 8080

# Development mode with auto-reload
slopit dashboard --reload

# Custom data directory
slopit dashboard --data-dir ./sessions

# Combined options
slopit dashboard --host 0.0.0.0 --port 8080 --reload --data-dir ./sessions

Requirements:

The dashboard requires optional dependencies. Install with:

pip install slopit[dashboard]

Features:

When running, the dashboard provides:

• REST API at http://localhost:8000/api/v1/
• WebSocket at ws://localhost:8000/ws for real-time updates
• Web UI at http://localhost:8000/ (if React frontend is built)

API Endpoints:

Endpoint	Description
/api/v1/sessions/	List and manage sessions
/api/v1/trials/	Access trial data
/api/v1/analysis/	Verdicts, flags, batch analysis
/api/v1/export/	CSV and JSON exports
/api/v1/jatos/	JATOS synchronization
/api/v1/prolific/	Prolific submission management

Environment Variables:

For JATOS and Prolific integration, set API tokens via environment variables or pass them in API requests:

export JATOS_URL="https://jatos.example.com"
export JATOS_TOKEN="your-jatos-api-token"
export PROLIFIC_TOKEN="your-prolific-api-token"

slopit dashboard

---

Analyzers

Available analyzers for the --analyzers option:

Name	Description
keystroke	Keystroke dynamics analysis (IKI variance, revision patterns)
focus	Focus and visibility analysis (tab switching, hidden periods)
timing	Response timing analysis (fast responses, consistent timing)
paste	Paste event analysis (large pastes, paste without typing)

Default: All four analyzers are used.

Aggregation Strategies

Available strategies for the --aggregation option:

Strategy	Description
any	Flag if any analyzer produces a flag (most sensitive)
majority	Flag if majority of analyzers produce flags
weighted	Use confidence-weighted voting (recommended)

Default: weighted

Exit Codes

Code	Meaning
0	Success
1	Error (file not found, invalid format, etc.)

Environment Variables

The dashboard command can use environment variables for API integration:

Variable	Description
JATOS_URL	JATOS server URL for data synchronization
JATOS_TOKEN	JATOS API authentication token
PROLIFIC_TOKEN	Prolific API authentication token

These are optional; credentials can also be passed via the REST API.

Examples

Basic Workflow

# 1. Validate data files
slopit validate data/

# 2. Run analysis
slopit analyze data/ --output results.json

# 3. Generate report
slopit report results.json

Screening Large Datasets

# Quick summary of flagged sessions
slopit analyze data/ --summary

# Export for further analysis
slopit analyze data/ --csv results.csv

Focused Analysis

# Only keystroke analysis
slopit analyze data/ --analyzers keystroke

# Keystroke and focus only
slopit analyze data/ --analyzers keystroke,focus

High Sensitivity Mode

# Use "any" aggregation to catch more potential issues
slopit analyze data/ --aggregation any --summary

Batch Processing

Process multiple directories:

for dir in batch1 batch2 batch3; do
  slopit analyze "data/$dir" --output "results_$dir.json" --csv "results_$dir.csv"
done

Integration with Other Tools

# Pipe to jq for JSON processing
slopit analyze data/ --output /dev/stdout | jq '.verdicts | to_entries | map(select(.value.status == "flagged"))'

# Extract flagged session IDs
slopit analyze data/ --output results.json
jq -r '.verdicts | to_entries[] | select(.value.status == "flagged") | .key' results.json