Dataset: 2026-10-27-catalog-ai-v2
Ruleset: b2e8889c66f7
Scanned: 2026-10-27
Window: February–September 15, 2026

Catalog tracking observation. This repository is measured with the same fixed window and ruleset, but it is not part of the published benchmark or its aggregate claims.

一款全面的、便捷的cocos creator AI MCP服务插件，适用于3.8.0以上cocos版本，一键安装，一键启动。A comprehensive and convenient cocos creator AI MCP service plug-in, suitable for cocos versions above 3.8.0, one-click installation and one-click start.

01Result

DaxianLee/cocos-mcp-server ↗

DaxianLee/cocos-mcp-server AI coding statistics: 0%

of 3 commits in February–September 15, 2026 were committed by an AI agent — a floor, not an estimate

Too little recent activity to publish. Only 3 commits landed in this window, so the percentage is noisy. Repositories below 100 are excluded from the index for this reason.

No signed agent commits found

No agent signatures were found in the last 3 considered commits. That is a real result, not a failure to measure.

It does not mean no AI was used here. A developer can use an agent and commit by hand without leaving a detectable git signature.

agent commits

other considered commits

—

agent contributors

—

largest contributor

02Drivers

What shapes this result

Mode, timing, and detected signatures show whether the result reflects broad adoption or one concentrated workflow.

No agent signatures were detected, so there are no agent, mode, or timing drivers to break down.

03Evidence

Inspect the matches

Public commit samples let you inspect the signatures behind the aggregate result.

No matched commit samples are available for this observation.

04Method

What entered the denominator

The result begins with commit history, removes merges and routine automation, and reports literal agent signatures as a lower bound.

Exclusion ledger

Commits in window	3
Merge commits removed	−0
CI / dependency bots removed	−0
Considered	3

Bots are excluded from the denominator entirely. Agent attribution requires a literal supported signature; no code-style inference is used.

Continue exploring

TypeScript AI coding statisticsCompare evidence across TypeScript repositories →GitHub AI commit analyzerInspect another repository with the same evidence model →

Reproduce this observation

Run the same detector locally.

Deterministic signature matching; no code-style inference. Squash merges can remove trailers, so this result remains a measured floor.

npx qmmit-cli DaxianLee/cocos-mcp-server

Scan another repository

Try