- Dataset
- 2026-10-27-catalog-ai-v2
- Ruleset
- b2e8889c66f7
- Scanned
- 2026-10-27
- Window
- February–September 15, 2026
本文详细分析了 Github Copilot 这个基于机器学习的代码自动补全工具的实现原理。作者通过逆向工程的方式,深入探索了 Copilot 的核心逻辑,包括代码提示的入口、获取提示的核心方法、以及相关的缓存策略、实验特性等。
01Result
mengjian-github/copilot-analysis ↗mengjian-github/copilot-analysis AI coding statistics: 0%
No signed agent commits found
No agent signatures were found in the last 0 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.
02Drivers
What shapes this result
Mode, timing, and detected signatures show whether the result reflects broad adoption or one concentrated workflow.
03Evidence
Inspect the matches
Public commit samples let you inspect the signatures behind the aggregate result.
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 | 0 |
| Merge commits removed | −0 |
| CI / dependency bots removed | −0 |
| Considered | 0 |
Bots are excluded from the denominator entirely. Agent attribution requires a literal supported signature; no code-style inference is used.
Continue exploring
Related coding-agent evidence
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 mengjian-github/copilot-analysis