Oddsense

D 57 completed
Cli Tool
unknown / rust · tiny
36
Files
6,231
LOC
0
Frameworks
3
Languages

Pipeline State

completed
Run ID
#304149
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
40.93
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #48152
Member of a group with 1 similar repo(s) — canonical #94586 view group →
Top concepts (4)
Project DescriptionCLI ToolFactorySearch
Repobility · MCP-ready · https://repobility.com

AI Prompt

Create a command-line intelligence layer for prediction markets, similar to oddsense. It needs to be written in Rust and function as an agent-native CLI. The core functionality should allow users to search markets across multiple sources like Polymarket, Kalshi, and Metaculus. I also need commands to enrich queries with sentiment from news and Reddit, detect market divergence from sentiment, surface trending signals based on volume, and find cross-platform arbitrage opportunities. Make sure the output is structured JSON and that the CLI is designed to be composable via Unix pipes.
rust cli command-line prediction-markets finance intelligence agent-native json
Generated by gemma4:latest

Catalog Information

oddsense is an agent-native CLI intelligence layer for prediction markets, providing analysis, enrichment, signals, and cross-source intelligence.

Description

oddsense is a command-line interface (CLI) that aggregates data from existing CLIs like polymarket-cli, performs real-world sentiment analysis, divergence detection, and arbitrage discovery. It treats upstream data sources as input and focuses on providing analysis, enrichment, signals, and cross-source intelligence.

الوصف

هو CLI يجمع البيانات من CLIs موجودة مثل polymarket-cli، يقوم بتحليل الرأي العام، اكتشاف الفجوات، واكتشاف الفرص للاستثمار. يعتبر مصدر البيانات كمصدر للبيانات الداخلة في العملية.

Novelty

7/10

Tags

prediction-markets data-aggregation sentiment-analysis divergence-detection arbitrage-discovery

Technologies

serde tokio

Claude Models

claude-opus-4.6

Quality Score

D
57.4/100
Structure
53
Code Quality
70
Documentation
48
Testing
0
Practices
80
Security
100
Dependencies
80

Strengths

  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected
  • Properly licensed project

Weaknesses

  • No tests found \u2014 high risk of regressions
  • No CI/CD configuration \u2014 manual testing and deployment
  • 455 duplicate lines detected \u2014 consider DRY refactoring

Recommendations

  • Add a test suite \u2014 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

Security & Health

7.6h
Tech Debt (C)
Medium
DORA Rating
A
OWASP (100%)
Generated by Repobility's multi-pass static-analysis pipeline (https://repobility.com)
PASS
Quality Gate
A
Risk (3)
MIT
License
5.7%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

rust
77.1%
markdown
22.2%
toml
0.8%

Frameworks

None detected

Symbols

function124
struct44
extension17
constant9
enum4
trait2

Concepts (4)

Generated by the Repobility scanner · https://repobility.com
CategoryNameDescriptionConfidence
Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/
auto_descriptionProject DescriptionAgent-native CLI for prediction market intelligence.80%
auto_categoryCLI Toolcli70%
design_patternFactoryFound factory/create_ naming patterns60%
business_logicSearchDetected from 3 related files50%

Quality Timeline

1 quality score recorded.

View File Metrics
Repobility's GitHub App fixes findings like these · https://github.com/apps/repobility-bot

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