Macroscope

mintocode is an AI coding-agent context tool that compiles a repository into plain-text cards describing files, functions, modules, dependencies, flows, aliases, and likely questions. For each question, it selects relevant paragraphs from those notes, follows a symbol-level link graph to referenced definitions, callers, and tests, and combines the selected notes with current source spans into a compact context pack rather than repeatedly sending whole files. The compiled data is stored in .micode/ and can be updated incrementally; citations are checked against a parsed symbol table and unresolved citations are flagged. The mic command supports one-call questions and pack generation, while integrations provide hooks, MCP tools, commands, and read guards for Claude Code, Codex, ZCode, and OpenCode. It is distributed as a Python package requiring Python 3.10 or later with no dependencies, uses existing Claude, Anthropic, OpenAI-compatible, or Codex model access, and is licensed under MIT.

Scopey is a lightweight Rust command-line tool for keeping Claude Code, Codex, Grok, Pi, and OpenCode coding-agent sessions aligned with the user's current intent. It turns prompts into a current scope, evaluates agent tool activity against that scope, and surfaces sessions that need attention when work drifts. It can inject a correction into an active session and send notifications, while ignoring subagent sessions by default. Scopey is pre-1.0 software and is distributed as a Homebrew package or Rust source installation.

Open Code Review is an open-source, AI-powered command-line code-review tool developed from Alibaba Group’s internal review assistant. It reads Git diffs and sends changed files to a configurable LLM through an agent with tool-use capabilities; the agent can read complete files, search the repository, and inspect other changed files for context before producing structured comments tied to source lines. Its hybrid architecture combines deterministic engineering with dynamic agent decisions: deterministic stages select and bundle files, match rules to file characteristics, and apply independent comment-positioning and reflection modules, while scenario-specific prompts and tools guide context retrieval and review. The `ocr scan` command audits complete files or directories without requiring a meaningful diff, and the project provides multilingual rules for issues including null-pointer exceptions, thread safety, cross-site scripting, and SQL injection. It supports configurable model endpoints, including OpenAI- and Anthropic-compatible endpoints, and requires Git 2.41 or newer.

Code Review Graph is an open-source, local-first code intelligence tool that provides a command-line interface and MCP server. It builds a persistent structural map of a codebase with Tree-sitter, representing relationships such as functions, classes, imports, calls, inheritance, and test coverage, while tracking changes incrementally. The graph supplies focused repository context to AI coding tools so they can read relevant code instead of repeatedly scanning large portions of a project. It also supports code-review workflows such as blast-radius analysis and risk-scored pull-request reviews. The Python package can be installed with pip or pipx; its install command detects supported AI coding platforms and configures MCP settings, hooks, skills, and graph-aware instructions.

Repowise is an open-source, self-hosted codebase-intelligence tool for developers and coding agents. It builds a local index of source code, dependency and symbol relationships, Git history, tests, documentation, architectural decisions, dead code, and code-health signals, then exposes the indexed evidence through an MCP server, local dashboard, CLI, editor integrations, and pull-request analysis. Its graph is built from AST-parsed code and Git data, with confidence-stamped call resolution, change-impact and co-change analysis, hotspot and ownership history, documentation-drift checks, decision evidence, and deterministic code-health detectors. The MCP interface provides task-oriented tools for repository overviews, cited answers, file and symbol context, code search, risk analysis, change risk, architectural rationale, dead-code detection, and health and refactoring plans. Optional model-written documentation and prose can be generated through a configured provider, while the core graph, risk, health, test, and dead-code analysis does not require LLM calls or an API key. Repowise also includes reversible command-output distillation for agents, proactive context hooks, generated agent instruction files, a GitHub PR bot, multi-repository workspace analysis, contract and cross-repository blast-radius mapping, and a VS Code extension. It can be installed with pip and run locally; the repository states that raw source is processed transiently and not persisted, while indexed graph data, embeddings, wiki pages, and Git metadata are stored. The core project is licensed under AGPL-3.0, with commercial licensing available for specified enterprise capabilities and embedding scenarios.