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.

这是一个基于Claude Skill的**AI人像Prompt生成系统**，能够从特征库中智能组合生成高质量的人像描述Prompt，并具备自动学习和库扩展能力。核心能力: Prompt生成、特征提取、自动学习、智能审核、版本控制

01Result

huangserva/skill-prompt-generator ↗

huangserva/skill-prompt-generator AI coding statistics: 0%

of 2 commits in February–September 15, 2026 were committed by an AI agent — a floor, not an estimate

Too little recent activity to publish. Only 2 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 2 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	−1
CI / dependency bots removed	−0
Considered	2

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

Continue exploring

Python AI coding statisticsCompare evidence across Python 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 huangserva/skill-prompt-generator

Scan another repository

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