Quickstart
Python API
import scitex_clew as clew
# Check verification status
status = clew.status()
# List tracked runs
runs = clew.list_runs(limit=10)
# Verify a specific session
result = clew.run("SESSION_ID")
print(f"Verified: {result.is_verified}")
# Trace provenance chain
chain = clew.chain("/path/to/output.csv")
# Full DAG verification
dag = clew.dag(["/path/to/target.csv"])
Five Node Classes
Every node in the DAG is classified into one of five semantic roles:
Class |
Role |
Examples |
|---|---|---|
Source |
Data acquisition scripts |
|
Input |
Raw data and configuration |
|
Processing |
Transform and analysis scripts |
|
Output |
Intermediate and final data products |
|
Claim |
Manuscript assertions tied to evidence |
|
The key operation is backpropagation from claims to sources: starting from a manuscript assertion (claim), Clew traces backward through outputs, processing scripts, and inputs to the original raw data — verifying every hash along the way.
Three Verification Modes
Mode |
Scope |
API |
Description |
|---|---|---|---|
Project |
Entire pipeline |
|
Verifies every session recorded in the database in topological order. A navigation map for ongoing project monitoring. “Is the whole project intact?” |
Files |
Specific outputs |
|
Traces backward from target files through their dependency chain. “Can I trust this specific file?” |
Claims |
Manuscript assertions |
|
Verifies individual claims linked to source sessions. “Is this figure still backed by the data?” |
CLI
# Overview
clew status
# List runs
clew list --limit 10
# Verify a session
clew verify SESSION_ID
# Generate Mermaid diagram
clew mermaid
# List Python APIs
clew list-python-apis -v
# List MCP tools
clew mcp list-tools -v
MCP Server
# Start standalone MCP server
clew mcp start
# Check MCP health
clew mcp doctor