Tracelight

D 55 completed
Other
containerized / json · tiny
23
Files
5,032
LOC
0
Frameworks
5
Languages

Pipeline State

completed
Run ID
#1545754
Phase
done
Progress
0%
Started
2026-04-16 23:32:25
Finished
2026-04-16 23:32:25
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
28.02
Framework unique
Isolation
Last stage change
2026-05-10 03:34:51
Deduplication group #47389
Member of a group with 182 similar repo(s) — canonical #1447986 view group →
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AI Prompt

Create a web application, similar to Tracelight, that visualizes hidden offshore connections between world leaders using real ICIJ data. The frontend should feature a 3D force-directed network graph built with Three.js, allowing users to select "Power Players" from a grid of politically exposed persons. The core logic needs a backend API that uses a PathFinder walker agent to trace connections through shell companies and intermediaries. This agent should leverage a Python SQLite database for pathfinding and use an LLM call, specifically a `by llm()` function, to generate a structural narrative summary based only on the discovered graph facts. The system should expose REST endpoints for searching and investigating connections.
javascript three.js python web-app graph-visualization llm api sqlite data-analysis network-graph
Generated by gemma4:latest

Catalog Information

Create a web application, similar to Tracelight, that visualizes hidden offshore connections between world leaders using real ICIJ data. The frontend should feature a 3D force-directed network graph built with Three.js, allowing users to select "Power Players" from a grid of politically exposed persons. The core logic needs a backend API that uses a PathFinder walker agent to trace connections through shell companies and intermediaries. This agent should leverage a Python SQLite database for pat

Tags

javascript three.js python web-app graph-visualization llm api sqlite data-analysis network-graph

Quality Score

D
55.3/100
Structure
46
Code Quality
75
Documentation
51
Testing
0
Practices
64
Security
100
Dependencies
80

Strengths

  • Consistent naming conventions (snake_case)
  • Good security practices — no major issues detected
  • Containerized deployment (Docker)
  • Properly licensed project

Weaknesses

  • No tests found — high risk of regressions
  • No CI/CD configuration — manual testing and deployment
  • 175 duplicate lines detected — consider DRY refactoring
  • 1 'god files' with >500 LOC need decomposition

Recommendations

  • Add a test suite — start with critical path integration tests
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a linter configuration to enforce code style consistency

Languages

json
47.1%
python
27.1%
html
22.5%
markdown
3.0%
toml
0.3%

Frameworks

None detected

Symbols

function33
constant17
variable3

Quality Timeline

1 quality score recorded.

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