Jevgrep

Jev Review is a local-first MCP plugin for continuous software-quality review by AI coding agents, powered by Jev. It runs as a local Node.js server over MCP stdio and exposes a focused `jev_review` tool: an agent submits a task, focused diff, selected files, or repository context, and Jev returns structured per-dimension scores and confidence values for areas such as correctness, complexity, readability, modularity, changeability, testing, reliability, security, and documentation. When a previous evaluation is supplied, the plugin reports metric deltas, improvements, regressions, and weak dimensions rather than producing a blended overall score or prose diagnosis; the coding agent remains responsible for identifying causes and changing the code. It supports Claude Code, Codex, Cursor, and OpenCode, and is distributed directly from its GitHub repository rather than npm. The process keeps the API key locally and sends only explicitly supplied review context directly to the configured Jev API; it has no hosted backend, database, telemetry service, or author-operated proxy. The repository is licensed under MIT.

Jev Search is a web search application built by Search1API that uses TypeSafe's Jev to interpret plain-language requests, select search terms, sources, and time ranges, and rank results. It queries general and vertical search engines concurrently, merges results by URL, engine agreement, original rank, and Jev relevance scores, and returns links and snippets with editable filters rather than generated answers. The application streams results as search lanes finish, reports failed sources separately, and can be installed as a browser app; it requires a network connection for searches. The open-source application is MIT licensed and can be self-hosted on Cloudflare Workers with Search1API and a Jev provider such as TypeSafe, Vercel AI Gateway, or Cloudflare Workers AI.

Jevry is an open-source, MIT-licensed desktop browser agent created by Michael Swissa. It is an Electron browser that combines a language model for planning and text or visual reasoning with TypeSafe's Jev model for selecting typed actions from controls observed on the current page. Chromium executes the selected operations through guarded native input rather than generated JavaScript, arbitrary selectors, or shell commands. Its loop observes page text and compatible controls, compiles supported operation-and-target choices such as clicking, typing, scrolling, or stopping, asks Jev to choose, validates the choice against the current document and target, executes it, records an input receipt, and checks the resulting evidence before continuing. It supports browser tasks such as forms, page-grounded questions, research and citation, documentation navigation, catalogs, and selected visual or numeric games. The project is an experimental developer preview for macOS and Windows; it requires a TypeSafe Jev connection plus a text-model connection, and some capabilities—including uploads, extensions, password-manager workflows, and certain cross-origin interactions—remain unsupported or unverified.

Jevmind is a Python decision layer for software agents. It turns agent decisions into typed answers such as yes/no results, choices, and scores with confidence values, then applies a code-defined gate: uncertain answers escalate, trusted positive answers may act, and trusted negative answers are held. Its decisions are recorded in an append-only, hash-chained ledger, graded against later outcomes, and optionally recalibrated with Platt scaling. It includes nine skills for compacting tool output, navigating repositories, reviewing diffs, routing tasks to model tiers, checking completion claims, curating training data, walking linked markdown vaults, guarding commands, and running a structured-state control-loop demo. The default local brain uses readable rules, reflexes, and BM25 retrieval without a language model; an optional Jev HTTP brain can answer the same typed questions, while replay mode uses recorded answers. Jevmind runs as a command-line tool and MCP server, includes a Claude Code hook for shell-command screening, supports Python 3.10+, has no runtime dependencies, and is distributed under the MIT license.

geiger is a read-only command-line scanner from Atomburst that inventories AI agents, harnesses, MCP servers, plugins, skills, hooks, browser extensions, desktop AI applications, and related IDE configurations on a machine. It reads known configuration files and directories, identifies each finding's origin and evidence path, and labels capabilities such as code execution, secret storage, broad filesystem access, broad web access, and network access. Credential-shaped values are reported by key name, file, and secret type without exposing value contents; the scanner performs no telemetry and writes only an explicitly requested report file. It can emit terminal, HTML, or versioned JSON reports, scan specified project or home directories, enforce a strict exit status for findings that can execute code or hold secrets, and compare JSON snapshots to identify newly appeared, removed, or escalated findings. It runs through npx with Node.js 18 or newer, has no runtime dependencies or account requirement, and is licensed under MIT. The project notes that it reads configuration rather than runtime behavior, covers known locations, and does not determine whether a package is malicious.