by Ataraxy-Labs · MCP Server · ★ 120
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Part of the Ataraxy Labs stack — agent-native infrastructure for software development. See also: sem (semantic version control) · weave (entity-level merge driver) · opensessions (tmux sidebar for coding agents). Read the manifesto: https://ataraxy-labs.com/#thesis · Essays: https://ataraxy-labs.com/blogs · LLMs: https://ataraxy-labs.com/llms.txt inspect Entity-level code review for Git. Triage PRs by structural risk, not line count. inspect is a code review tool that works at the entity level instead of files or lines. It parses your diff with tree-sitter, classifies each changed entity (text-only, syntax, functional), scores it by risk using the cross-file dependency graph, and groups independent changes so tangled commits can be reviewed as separate units. Every code review tool today shows you files and lines. inspect shows you which functions matter. A renamed variable, a reformatted function, and a deleted public API method all look the same in a line diff. inspect scores the API deletion as critical (high blast radius, many dependents) and the rename as low risk. Review the critical stuff first, skip the noise. On the Greptile benchmark (141 fi
| Stars | 120 |
| Forks | 4 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 68.0443004466605/100 |
| Last Updated | 2026-04-10 |
| Created | 2026-02-12 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~389k |
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inspect is Entity-level code review for Git. Graph-based risk scoring, change classification, commit untangling. 95% recall on the Greptile benchmark.. It is categorized as a MCP Server with 120 GitHub stars.
inspect is primarily written in Python. It covers topics such as ai-agents, cli, code-review.
You can find installation instructions and usage details in the inspect GitHub repository at github.com/Ataraxy-Labs/inspect. The project has 120 stars and 4 forks, indicating an active community.
The top alternatives to inspect on Agent Skills Hub include roam-code, claude-code-skills, octocode. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.