dataset string | bucket string | prefix string | generated_at timestamp[s] | totals dict | structure dict | files list |
|---|---|---|---|---|---|---|
vibecoding_task_packages | complete-bucket | vibeCoding/ | 2026-09-18T18:36:00 | {
"packages": 177,
"bytes": 98721274277,
"gb": 91.94,
"min_size": 140934612,
"median_size": 455918840,
"max_size": 1994428335,
"distinct_orgs": 167,
"latest_update": "2026-09-18T00:00:00",
"updated_on_latest_day": 84
} | {
"docker/<issueId>/": "可运行沙箱环境(OCI 镜像:index.json / manifest.json / oci-layout / blobs/sha256/*)",
"json/<org__repo-issueId>/": "任务层:_README.md、任务 json/jsonl、code.patch、model.patch、final.diff、test.patch、check_delivery.py、grade.sh、dockerfile、setup_env.sh、setup_repo.sh、pin_by_date.py"
} | [
{
"name": "AsyncHttpClient__async-http-client-2250.zip",
"size": 506760821,
"mtime": "2026-09-04T00:00:00",
"org": "AsyncHttpClient",
"repo": "async-http-client",
"issue": "2250"
},
{
"name": "Choices-js__Choices-1401.zip",
"size": 227552685,
"mtime": "2026-09-18T00:00:00",
... |
DM0012 · Vibe Coding Repository-Level Task Samples / Vibe Coding 仓库级任务样例
| Item / 项目 | Value / 值 |
|---|---|
| Dataset ID / 数据集编号 | DM0012 |
| Files / 数据文件 | 1 |
| Size / 数据体积 | 29.36 KB |
| Provided by / 提供方 | Zovda |
| Contact / 联系邮箱 | lucas@zovda.com |
Overview / 简介
Repository-level task corpus for coding-agent (SWE-Agent) training and evaluation: each package targets a real GitHub issue and bundles a runnable Docker sandbox, task description, reference patches and automatic grading scripts.
面向代码智能体(SWE-Agent)训练与评测的仓库级任务语料:每个任务包对应一个真实开源仓库的 GitHub Issue,含可运行 Docker 沙箱、任务描述、参考补丁与自动验收脚本。
What's inside / 数据内容
manifest.json: the public task-package manifest listing name, size, date, org, repo and issue id for each package.manifest.json:随样例库公开的任务包清单,逐条记录包名、大小、日期、org、repo 与 issue 编号。- Full packages (
<org>__<repo>-<issueId>.zip) are held in a private delivery store; about 42 entries per package —docker/<issueId>/(OCI-layout sandbox image) plusjson/<org__repo-issueId>/(task description,code.patch,model.patch,final.diff,test.patch,check_delivery.py,grade.sh,dockerfile,setup_env.sh,setup_repo.sh,pin_by_date.py). - 完整任务包(
<org>__<repo>-<issueId>.zip)存放于私有交付目录,每包约 42 个条目,结构为:docker/<issueId>/(OCI 布局沙箱镜像)与json/<org__repo-issueId>/(任务描述、code.patch、model.patch、final.diff、test.patch、check_delivery.py、grade.sh、dockerfile、setup_env.sh、setup_repo.sh、pin_by_date.py)。 - Contact lucas@zovda.com to obtain packages.
- 取数请联系 lucas@zovda.com。
Specifications / 规格
- Packaging: Docker (OCI layout) + JSON, two layers
- 打包:Docker(OCI 布局)+ JSON 双层
- Naming:
<org>__<repo>-<issueId>, traceable to the real issue - 命名:
<org>__<repo>-<issueId>,issueId 可回溯真实 Issue - Acceptance:
grade.shauto-grades inside the sandbox;check_delivery.pyverifies delivery completeness - 验收:
grade.sh在沙箱内自动评分,check_delivery.py校验交付完整性
Suitable tasks / 适用任务
- Code-model post-training (SFT): paired "issue description → reference patch" samples
- 代码模型后训练(SFT):「Issue 描述 → 参考补丁」成对样本
- Reinforcement learning / agent training: sandbox + auto-grading provide reward signals
- 强化学习 / Agent 训练:沙箱 + 自动评分提供奖励信号
- End-to-end coding-agent evaluation: issue-resolution pass rate
- 代码智能体端到端评测:Issue 修复通过率
Files / 文件清单
| # | File | Size |
|---|---|---|
| 1 | vibeCoding/manifest.json |
29.36 KB |
Usage / 使用方式
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="Zovda/dm0012-vibe-coding-samples",
repo_type="dataset",
local_dir="./dm0012-vibe-coding-samples",
)
Rights and disclaimer / 授权与声明
Tasks are built from real issues in public open-source repositories, which retain their original open-source licences. This manifest demonstrates task organisation; packages are licensed on request.
任务基于公开开源仓库的真实 Issue 构建,仓库代码遵循其原始开源许可。本清单用于展示任务组织方式,任务包按需授权交付。
This dataset is shared to demonstrate data organisation, technical specifications and delivery structure. For formal licensing, large-scale access or custom production, contact lucas@zovda.com.
本数据集仅用于展示数据组织方式、技术规格与交付结构。正式授权、规模化取数与定制生产请联系 lucas@zovda.com。
About Zovda / 关于 Zovda
Zovda builds, cleans, annotates and packages training data across video, audio, images, e-commerce image-text pairs, exam question banks, embodied AI and coding-agent corpora.
Zovda 提供多模态与文本训练语料的数据采集、清洗、标注与结构化交付服务,覆盖视频、音频、图像、电商图文、K12 与职业题库、具身智能、代码智能体等方向。
Contact / 联系方式: lucas@zovda.com
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