codedelta
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- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to mine a git repository's history and answer pre-change questions: which files are riskiest, who genuinely knows a file or directory (blame-weighted, with bus factor and inactive authors), which files secretly change together, churn timelines, commit search, and release notes. It runs read-only against real git with noise filtering and reproducible, documented formulas.MIT
- AlicenseAqualityAmaintenanceEvidence-backed, read-only codebase diagnostics for coding agents: secrets, dependency, RLS, and source-impact audits with a verify loop and PR review verdicts. Never writes files or uses the network by default.7114 npmMIT
- AlicenseNot gradedqualityBmaintenanceGives coding agents local software intelligence: it surfaces the files, tests, and helpers relevant to a task before edits, flags missed callers and uncovered code during review, and returns a single SHIP / NOT PROVEN / NO-SHIP verdict when the agent stops. Everything runs on the local index with no network calls, so repository code stays on the machine.3,819 npm10MIT
- FlicenseNot gradedqualityBmaintenanceEnables developers and AI agents to compute deterministic in-memory unified diffs between successive versions of live artifacts and receive structured change telemetry in return. It runs with zero external dependencies and plugs into MCP-compliant clients such as Claude Desktop, Cursor, and Windsurf.8-
- AlicenseNot gradedqualityBmaintenanceLocal code-graph engine for AI coding workflows: static analysis of Rust, Cangjie, ArkTS, TypeScript/JavaScript, C/C++, Python, and Shell projects into symbols, imports, call graphs, and quality gates, with pre-commit change review and impact analysis. Read-only by design — never executes build scripts or uploads code.61MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI assistants to audit git diffs, scan for leaked secrets and unsafe APIs, detect breaking changes, triage issues, generate changelogs, and assess repository hygiene through native stdio JSON-RPC tools.6Apache 2.0
TDQS
Scored across 3 tools
churn_scan is clearly distinct, but ai_audit and agent_scan both target the 'AI in this codebase' question — one measuring AI-generated provenance, the other AI SDK/agent usage and commits. The descriptions help separate them, but a vague query like 'how much AI is in this repo?' could reasonably route to either, creating real misfire risk.
All three follow the same snake_case <subject>_<action> pattern (ai_audit, churn_scan, agent_scan). The verbs vary slightly (audit vs. scan) but the convention is uniform and predictable.
Three tools is well-scoped for a focused code-delta/audit engine, and each covers a genuinely distinct analysis (churn metrics, AI-generated code, AI agent inventory). No filler tools and no redundancy in count.
The three core analyses cover the domain's main questions, and churn_scan produces HTML reports. Minor gaps: no tool to retrieve/correlate prior reports, combine results (e.g. churn vs. AI-generated files), or compare the outputs of the analyses, but these are workable around.