jev-scout
Zero-hallucination open-source repo and crate discovery powered by TypeSafe AI Jev.
Author: Akash Priyadarshi
Why • Quickstart • How it works • CLI Flags • Architecture • Non-Goals • Ecosystem
Why
When developers and AI coding agents ask LLMs for open-source libraries, general-purpose models routinely hallucinate non-existent package names or recommend abandoned six-year-old repositories. GitHub's native search relies on rigid keyword matching that fails on conceptual queries.
jev-scout solves this by decoupling discovery from decision:
- Grounding first: Queries real package registries (GitHub REST API, crates.io) to fetch actual live metadata.
- System One scoring: Uses TypeSafe AI's
jev-latestin a single speculative fan-out call (~120ms) to score architectural fit, license suitability, and maintenance freshness. - Zero hallucinated packages: You only get verified, installable repositories with exact stars, licenses, and clone commands.
- Dual surface: Works as an interactive terminal CLI for developers and as a stdio MCP server for autonomous coding agents (Claude Code, Antigravity).
Quickstart
Installation
Or build from source:
Setup API Key
jev-scout requires a TypeSafe AI API key:
Usage
Search for repositories matching natural language concepts:
# Search crates and repositories
# Filter by ecosystem
# Output raw JSON for scripts and agents
How it works
User Query: "fast sqlite tui in rust"
│
├─► 1. Grounded Search (GitHub REST API + crates.io)
│ Pulls top candidate repos with stars, licenses, and commit dates.
│
├─► 2. Speculative Fan-out Call (POST https://api.typesafe.ai/v1/systemone)
│ Evaluates all candidates in 120ms with typed primitives:
│ - Score(fit): 1 (unrelated) to 4 (exact architectural fit)
│ - Noul(modern): Calibrated probability of active maintenance
│ - Choice(best_match): Single top candidate
│
└─► 3. Deterministic Ranking & Output
Sorts by confidence-weighted score (score * confidence).
Renders terminal cards or JSON.
CLI Flags
| Flag | Short | Default | Description |
|---|---|---|---|
--ecosystem |
-e |
all |
Target ecosystem (all, github, crates) |
--limit |
-n |
5 |
Maximum number of ranked results to return |
--json |
-j |
false |
Output machine-readable JSON to stdout |
--mcp |
false |
Start as a stdio Model Context Protocol (MCP) server | |
--help |
-h |
Print help information | |
--version |
-v |
Print version |
Architecture
jev-scout/
├── Cargo.toml # Rust dependencies: ureq, lexopt, serde
├── README.md # Documentation
├── CLAUDE.md # Development rules
├── AGENTS.md # Agent directives
├── STATE.md # Active project state
├── CHANGELOG.md # Version history
├── src/
│ ├── main.rs # Lexopt argument parsing and terminal display
│ ├── jev.rs # TypeSafe API client (Choice, Score, Noul)
│ ├── search.rs # Candidate retriever (GitHub REST + crates.io)
│ └── mcp.rs # Stdio JSON-RPC 2.0 MCP server handler
└── tests/
└── mock_test.rs # Offline unit tests using recorded fixtures
Non-Goals
- No text generation:
jev-scoutdoes not write code summaries or essays. It returns verified repo metadata and typed scores. - No heavy local databases: Zero SQLite, Postgres, or caching daemons required.
- No browser automation: No Chromium, Playwright, or web scraping dependencies.
- No paid search engines: Relies on official GitHub and crates.io APIs rather than paid search proxies.
Ecosystem
- design-genius - Design systems and UI craft documentation.
- akash-design-engineering - Advanced front-end architecture and animations.
- tdlib-android - Precompiled TDLib for Android with zero-dependency builds.
- kharcha - India-first offline UPI expense tracker.
Author
Akash Priyadarshi
Patna, Bihar, India
- GitHub: AkashPriyadarshii
- Portfolio: akashpriyadarshi.vercel.app
- LinkedIn: akash-priyadarshi-1aa51b37a
- Resume: akashpriyadarshii.github.io/Resume
Social
- X / Twitter: @Akash__ydv001
- Threads: @akash.priyadarshii
- Instagram: @akash.priyadarshii
- Reddit: u/DragonfruitWeak2801
Zero hallucinations, verified repositories, sub-second decisions.