公开记录良好
软件健康报告模式 0.17.0 · 指标 1.13.0 · 2026-07-21 04:07 UTC
Nixtla / mlforecast
Scalable machine 🤖 learning for time series forecasting.
Nixtla/mlforecast 的健康指数为 100 分中的 83 分，处于「良好」区间。 其得分最高的类别是Engineering Quality（92/100），最低的是AI Readiness（54/100）。 最近一次更新在今天。 近期的大部分工作由 1 位贡献者完成。
83
总分 / 100软件健康指数
指标归入加权类别，统一采用 1–100 量表。总体分先取类别加权平均；当公开证据触发高风险司法辖区政策时，评级会按政策调整，并设置 49（有风险）的上限。AI 就绪度不计入总体分。
83
优秀85-100堪称典范；基本满足所有检验标准
良好70-84健康；仅有轻微不足
中等50-69可接受，但存在明显不足；建议进行审查
存在风险30-49存在重大薄弱环节；采用时应保持审慎
危急1-29问题严重（项目被弃置、仅有单一维护者、缺乏基本工程规范）
评分画像
每条轴代表一个类别。形状比平均值更重要——健康的对象会填满整个图形，而“一峰一谷”式画像意味着某一维度的优势正掩盖另一维度的风险。
所有权
软件包生态系统
| 注册表 | 软件包 | 版本 | 月下载量 | 版本数 | 最近发布 | 标签 |
|---|---|---|---|---|---|---|
| PyPI | mlforecast | 1.1.0 | 466,392 | 58 | 11 天前 | pythonforecastforecastingmachine-learningdask |
按类别列示的指标
活力
项目是否仍有生命——是否仍在编写代码，是否仍在发布版本？
91
开发活跃度
90优秀
评分方式
| 36/36 | 推送新近度 — 最近一次推送于 0 天前 |
| 25.6/36 | 提交节奏 — 52 周中有 37 周有提交 |
| 18/18 | 提交量 — 最近一年 133 次提交 |
| 10/10 | OpenSSF Scorecard：Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
所用输入
| commits_last_year | 133 |
| human_commit_share | — |
| days_since_last_push | 0 |
| active_weeks_last_year | 37 |
发布纪律
92优秀
评分方式
| 27/27 | 有发布版本 — 已发布 44 个发布版本 |
| 36/36 | 发布时效 — 最近一次发布版本于 11 天前 |
| 19.8/27 | 发布节奏 — 约每 67.6 天发布一次 |
| 0/10 | OpenSSF Scorecard：Signed-Releases — 无数据 |
所用输入
| releases_count | 44 |
| latest_release_tag | v1.1.0 |
| releases_from_tags | 否 |
| days_since_latest_release | 11 |
| mean_days_between_releases | 67.6 |
已排除计分（无数据或不适用）：OpenSSF Scorecard：Signed-Releases。 其余权重已重新归一化。
社区与采用
项目是否拥有用户、下载量与关注度，并具备欢迎贡献者参与的配置？
85
流行度与采用
73良好
评分方式
| 50.3/60 | 星标 — 1,254 个星标 |
| 17.5/25 | 复刻 — 128 个复刻 |
| 5/15 | 关注者 — 9 位关注者 |
所用输入
| forks | 128 |
| stars | 1,254 |
| watchers | 9 |
| growth_state | organic |
| growth_factor_pct | 100 |
社区健康
92优秀
评分方式
| 22.5/22.5 | README |
| 22.5/22.5 | 许可证 — 可识别的许可证（Apache-2.0） |
| 18/18 | CONTRIBUTING 指南 |
| 13.5/13.5 | 行为准则 |
| 0/7.2 | 议题模板 |
| 6.3/6.3 | PR 模板 |
所用输入
| has_readme | 是 |
| has_license | 是 |
| has_contributing | 是 |
| has_issue_template | 否 |
| has_code_of_conduct | 是 |
| has_pull_request_template | 是 |
生态采用（下载量）
94优秀
评分方式
| 75.6/80 | 月度下载量 — pypi 合计每月 466,392 次下载 |
| 0/20 | 注册表被依赖数 — 该生态系统不报告此项 |
所用输入
| packages | mlforecast |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 466,392 |
已排除计分（无数据或不适用）：注册表被依赖数。 其余权重已重新归一化。
可持续性与治理
项目能否在其成员之外延续——巴士系数、响应能力、由谁支持，以及软件包的维护状况？
76
维护者韧性（巴士系数）
42存在风险
评分方式
| 9/54 | 巴士系数 — 1 位贡献者贡献了半数提交 |
| 9.3/22.5 | 提交分布 — 头号贡献者编写了 59% 的提交 |
| 13.5/13.5 | 贡献者广度 — 25 位贡献者 |
| 10/10 | OpenSSF Scorecard：Contributors — project has 5 contributing companies or organizations |
所用输入
| bus_factor | 1 |
| contributors_sampled | 25 |
| top_contributor_share | 0.587 |
议题与 PR 响应能力
95优秀
评分方式
| 45.6/46.8 | 议题解决 — 98% 的议题已关闭 |
| 34.1/38.3 | PR 接受 — 已裁定的 PR 中 365/410 已合并 |
| 15/15 | OpenSSF Scorecard：Code-Review — all changesets reviewed |
所用输入
| merged_prs | 365 |
| open_issues | 5 |
| closed_issues | 199 |
| issue_closed_ratio | 0.975 |
| closed_unmerged_prs | 45 |
所有权与托管责任
77良好
评分方式
| 30/30 | 所有权背书 — 组织持有 |
| 0/20 | 已验证域名 |
| 24.8/25 | 所有者影响力 — Nixtla 有 2,817 位关注者 |
| 22.4/25 | 既往记录 — 39 个公开仓库，账户约 5 年 |
所用输入
| followers | 2,817 |
| owner_type | Organization |
| is_verified | — |
| owner_login | Nixtla |
| public_repos | 39 |
| account_age_days | 1,966 |
软件包维护
100优秀
评分方式
| 25/25 | 已发布且可解析 — pypi 上有 1 个软件包 |
| 35/35 | 发布时效 — 最近一次发布于 11 天前 |
| 20/20 | 版本历史 — 58 个已发布版本 |
| 20/20 | 未被弃用 — 活跃，未被弃用或撤回 |
所用输入
| packages | mlforecast |
| ecosystems | pypi |
| any_deprecated | 否 |
| min_days_since_publish | 11 |
工程质量
基础的工程与文档实践是否到位？
92
工程实践
94优秀
评分方式
| 24/24 | CI 工作流 — 7 个工作流 |
| 24/24 | 存在测试 |
| 16/16 | Linter 配置 |
| 9.6/9.6 | Pre-commit 钩子 |
| 0/6.4 | .editorconfig |
| 20/20 | OpenSSF Scorecard：CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10 |
所用输入
| has_ci | 是 |
| has_tests | 是 |
| has_editorconfig | 否 |
| has_linter_config | 是 |
| has_precommit_config | 是 |
文档
90优秀
评分方式
| 30/30 | README |
| 25/25 | 文档目录 |
| 15/15 | 文档 / 主页站点 — https://nixtlaverse.nixtla.io/mlforecast |
| 10/10 | 仓库描述 |
| 10/10 | 主题标签 — 8 个主题标签 |
| 0/10 | Wiki |
所用输入
| topics | forecast, forecasting, machine-learning, lightgbm, xgboost, dask, python, time-series |
| has_wiki | 否 |
| homepage | https://nixtlaverse.nixtla.io/mlforecast |
| has_readme | 是 |
| has_docs_dir | 是 |
| has_description | 是 |
安全
可见的安全与供应链实践是否稳固，且不存在未解决的高风险司法辖区暴露？
68
安全态势
60中等
评分方式
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 3.8/7.5 | Branch-Protection — branch protection is not maximal on development and all release branches |
| 2.5/2.5 | CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 7.5/7.5 | Code-Review — all changesets reviewed |
| 2.5/2.5 | Contributors — project has 5 contributing companies or organizations |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 7.5/7.5 | Dependency-Update-Tool — update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.5/2.5 | 许可证 — license file detected |
| 7.5/7.5 | Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
| 0/5 | Packaging — 无数据 |
| 2/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4 |
| 2.5/5 | SAST — SAST tool is not run on all commits -- score normalized to 5 |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — 无数据 |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 0/7.5 | Vulnerabilities — 16 existing vulnerabilities detected |
所用输入
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 6 |
已排除计分（无数据或不适用）：packaging, signed_releases。 其余权重已重新归一化。
依赖安全公告
100优秀
评分方式
| 35/35 | 直接依赖不含已知公告 — 没有直接依赖携带已知公告 |
| 25/25 | 间接依赖不含已知公告 — 没有间接依赖携带已知公告 |
| 0/40 | 没有长期未处理的公告 — 没有公告带有发布日期 |
所用输入
| source | osv |
| advisories | 0 |
| affected_packages | 0 |
| assessed_packages | 24 |
| unassessed_packages | 0 |
| affected_by_severity | none |
| direct_affected_packages | 0 |
已排除计分（无数据或不适用）：没有长期未处理的公告。 其余权重已重新归一化。 比对的是 pypi:mlforecast@1.1.0 的运行时依赖闭包——安装已发布的软件包时真正被拉取进来的内容——共 24 个软件包。 未对可达性进行分析。
AI 就绪度
该仓库在多大程度上具备与 AI 编码代理协同开发与维护的条件？这是一枚独立的实验性徽章——权重为 0.0，因此单独呈现，不影响总体健康评分。
54
AI 代理上下文与指引
1危急
评分方式
| 0/45 | 代理指令 — 没有 CLAUDE.md / AGENTS.md / 编辑器规则 |
| 0/15 | 机器可读文档（llms.txt） |
| 0/40 | 可读的提交历史 — 无数据 |
所用输入
| has_llms_txt | 否 |
| legible_history_share | — |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
已排除计分（无数据或不适用）：可读的提交历史。 其余权重已重新归一化。
评分方式
| 18/18 | 一条命令的引导启动 — Makefile |
| 22/22 | 自动化测试 |
| 11/11 | Lint / 格式化配置 |
| 11/11 | 静态类型检查 — mlforecast/py.typed |
| 10/10 | 可复现环境 — lockfile |
| 0/10 | 已体现的代理实践 — 无数据 |
| 5/8 | 自动化维护 — 已配置依赖自动化，但在抽样提交中未观察到 |
| 4/10 | OpenSSF Scorecard：Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4 |
所用输入
| has_nix | 否 |
| has_tests | 是 |
| lockfiles | uv.lock |
| has_dockerfile | 否 |
| typed_language | 否 |
| bootstrap_files | Makefile |
| has_devcontainer | 否 |
| has_linter_config | 是 |
| typecheck_configs | mlforecast/py.typed |
| agent_commit_share | — |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0 |
已排除计分（无数据或不适用）：已体现的代理实践。 其余权重已重新归一化。
面向模型的代码可读性
77良好
评分方式
| 27/45 | 可类型检查的代码 — Python，已配置类型检查（mlforecast/py.typed） |
| 49.8/55 | 可控的文件大小 — 采样的 64 个源文件中有 6 个超过 60KB |
所用输入
| primary_language | Python |
| largest_source_bytes | 154,837 |
| source_files_sampled | 64 |
| oversized_source_files | 6 |
机器可读接口
40存在风险
评分方式
| 0/40 | API 模式（OpenAPI/GraphQL/proto） |
| 0/20 | MCP 服务器 |
| 40/40 | 可运行示例 — notebooks |
所用输入
| example_dirs | notebooks |
| has_mcp_signal | 否 |
| api_schema_files | — |
关键数据
1,254GitHub 星标
25贡献者
133最近 12 个月提交数
0距最近推送天数
44发布版本数
1巴士系数（bus factor）
5开放议题
PyPI软件包生态系统数
更多细节
Star 与 Fork 历史 1,254 ★ / 0 ⇿
1,254Star
每颗 star 和每个 fork 的添加时间，来自 GitHub 并按天汇总。累计增长位于其构成来源——每日新增——的正上方，二者可相互对照：稳定的自然增长与短暂的突增形态截然不同。当这一差别可被衡量时，它会作为增长真实性予以报告。
仅显示最近的历史——该仓库超出采集窗口，因此未采集最早的历史记录。
每个点涵盖 4 天。
OpenSSF Scorecard 6.0 / 10
6.0综合
来自开源项目 OpenSSF Scorecard 的独立、工具无关的安全评估。每项检查奖励的是安全实践本身，而非特定供应商的工具。Scorecard 无法判定的检查项标记为 不适用，并从安全评分中剔除（绝不按零分计）。
| 10 | Binary-Artifacts | no binaries found in the repo |
| 5 | Branch-Protection | branch protection is not maximal on development and all release branches |
| 10 | CI-Tests | 30 out of 30 merged PRs checked by a CI test -- score normalized to 10 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 10 | Code-Review | all changesets reviewed |
| 10 | Contributors | project has 5 contributing companies or organizations |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 10 | Dependency-Update-Tool | update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 10 | License | license file detected |
| 10 | Maintained | 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
| 不适用 | Packaging | packaging workflow not detected |
| 4 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 4 |
| 5 | SAST | SAST tool is not run on all commits -- score normalized to 5 |
| 0 | Security-Policy | security policy file not detected |
| 不适用 | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 0 | Vulnerabilities | 16 existing vulnerabilities detected |
直接依赖 8
| 注册表 | 软件包 | 版本约束 | 清单文件 |
|---|---|---|---|
| PyPI | cloudpickle | — | pyproject.toml |
| PyPI | coreforecast | >=0.0.15 | pyproject.toml |
| PyPI | fsspec | — | pyproject.toml |
| PyPI | narwhals | — | pyproject.toml |
| PyPI | optuna | — | pyproject.toml |
| PyPI | pandas | <3.0 | pyproject.toml |
| PyPI | scikit-learn | — | pyproject.toml |
| PyPI | utilsforecast | >=0.2.9 | pyproject.toml |
全部依赖 216
来自 GitHub 依赖图的完整解析依赖集合：9 个直接依赖与 207 个间接（传递）软件包。仓库提交锁文件时，传递闭包才是完整的。
| 注册表 | 软件包 | 版本 | 关系 |
|---|---|---|---|
| PyPI | cloudpickle | 3.1.2 | 直接 |
| PyPI | coreforecast | 0.0.18 | 直接 |
| PyPI | fsspec | 2026.1.0 | 直接 |
| PyPI | narwhals | 2.15.0 | 直接 |
| PyPI | optuna | 4.7.0 | 直接 |
| PyPI | pandas | 2.3.3 | 直接 |
| PyPI | scikit-learn | 1.7.2 | 直接 |
| PyPI | scikit-learn | 1.8.0 | 直接 |
| PyPI | utilsforecast | 0.2.15 | 直接 |
| PyPI | adagio | 0.2.6 | 间接 |
| PyPI | adlfs | 2025.8.0 | 间接 |
| PyPI | aiobotocore | 3.1.1 | 间接 |
| PyPI | aiohappyeyeballs | 2.6.1 | 间接 |
| PyPI | aiohttp | 3.14.1 | 间接 |
| PyPI | aioitertools | 0.13.0 | 间接 |
| PyPI | aiosignal | 1.4.0 | 间接 |
| PyPI | alembic | 1.18.1 | 间接 |
| PyPI | annotated-doc | 0.0.4 | 间接 |
| PyPI | annotated-types | 0.7.0 | 间接 |
| PyPI | anyio | 4.12.1 | 间接 |
| PyPI | async-timeout | 5.0.1 | 间接 |
| PyPI | attrs | 25.4.0 | 间接 |
| PyPI | azure-core | 1.38.0 | 间接 |
| PyPI | azure-datalake-store | 0.0.53 | 间接 |
| PyPI | azure-identity | 1.25.1 | 间接 |
| PyPI | azure-storage-blob | 12.28.0 | 间接 |
| PyPI | blinker | 1.9.0 | 间接 |
| PyPI | bokeh | 3.8.2 | 间接 |
| PyPI | botocore | 1.42.30 | 间接 |
| PyPI | cachetools | 6.2.4 | 间接 |
| PyPI | catboost | 1.2.10 | 间接 |
| PyPI | certifi | 2026.1.4 | 间接 |
| PyPI | cffi | 2.0.0 | 间接 |
| PyPI | cfgv | 3.5.0 | 间接 |
| PyPI | charset-normalizer | 3.4.4 | 间接 |
| PyPI | click | 8.3.1 | 间接 |
| PyPI | colorama | 0.4.6 | 间接 |
| PyPI | colorlog | 6.10.1 | 间接 |
| PyPI | contourpy | 1.3.2 | 间接 |
| PyPI | contourpy | 1.3.3 | 间接 |
| PyPI | convertdate | 2.4.0 | 间接 |
| PyPI | coverage | 7.13.1 | 间接 |
| PyPI | cryptography | 48.0.1 | 间接 |
| PyPI | cycler | 0.12.1 | 间接 |
| PyPI | dask | 2026.1.1 | 间接 |
| PyPI | databricks-sdk | 0.80.0 | 间接 |
| PyPI | datasetsforecast | 1.0.1 | 间接 |
| PyPI | decorator | 5.2.1 | 间接 |
| PyPI | distlib | 0.4.0 | 间接 |
| PyPI | distributed | 2026.1.1 | 间接 |
| PyPI | docker | 7.1.0 | 间接 |
| PyPI | duckdb | 1.4.3 | 间接 |
| PyPI | exceptiongroup | 1.3.1 | 间接 |
| PyPI | execnet | 2.1.2 | 间接 |
| PyPI | fastapi | 0.136.3 | 间接 |
| PyPI | filelock | 3.20.3 | 间接 |
| PyPI | flask | 3.1.3 | 间接 |
| PyPI | flask-cors | 6.0.2 | 间接 |
| PyPI | fonttools | 4.61.1 | 间接 |
| PyPI | frozenlist | 1.8.0 | 间接 |
| PyPI | fugue | 0.9.4 | 间接 |
| PyPI | gcsfs | 2026.1.0 | 间接 |
| PyPI | gitdb | 4.0.12 | 间接 |
| PyPI | gitpython | 3.1.50 | 间接 |
| PyPI | google-api-core | 2.29.0 | 间接 |
| PyPI | google-auth | 2.47.0 | 间接 |
| PyPI | google-auth-oauthlib | 1.2.4 | 间接 |
| PyPI | google-cloud-core | 2.5.0 | 间接 |
| PyPI | google-cloud-storage | 3.8.0 | 间接 |
| PyPI | google-cloud-storage-control | 1.9.0 | 间接 |
| PyPI | google-crc32c | 1.8.0 | 间接 |
| PyPI | google-resumable-media | 2.8.0 | 间接 |
| PyPI | googleapis-common-protos | 1.72.0 | 间接 |
| PyPI | graphene | 3.4.3 | 间接 |
| PyPI | graphql-core | 3.2.7 | 间接 |
| PyPI | graphql-relay | 3.2.0 | 间接 |
| PyPI | graphviz | 0.21 | 间接 |
| PyPI | greenlet | 3.3.0 | 间接 |
| PyPI | griffe2md | 1.5.0 | 间接 |
| PyPI | griffelib | 2.0.0 | 间接 |
| PyPI | grpc-google-iam-v1 | 0.14.3 | 间接 |
| PyPI | grpcio | 1.76.0 | 间接 |
| PyPI | grpcio-status | 1.76.0 | 间接 |
| PyPI | gunicorn | 23.0.0 | 间接 |
| PyPI | h11 | 0.16.0 | 间接 |
| PyPI | hijri-converter | 2.3.2.post1 | 间接 |
| PyPI | hijridate | 2.3.0 | 间接 |
| PyPI | holidays | 0.20 | 间接 |
| PyPI | huey | 2.6.0 | 间接 |
| PyPI | identify | 2.6.16 | 间接 |
| PyPI | idna | 3.15 | 间接 |
| PyPI | importlib-metadata | 8.7.1 | 间接 |
| PyPI | iniconfig | 2.3.0 | 间接 |
| PyPI | isodate | 0.7.2 | 间接 |
| PyPI | itsdangerous | 2.2.0 | 间接 |
| PyPI | jinja2 | 3.1.6 | 间接 |
| PyPI | jmespath | 1.1.0 | 间接 |
| PyPI | joblib | 1.5.3 | 间接 |
| PyPI | jsonschema | 4.26.0 | 间接 |
| PyPI | jsonschema-specifications | 2025.9.1 | 间接 |
| PyPI | kiwisolver | 1.4.9 | 间接 |
| PyPI | korean-lunar-calendar | 0.3.1 | 间接 |
| PyPI | librt | 0.7.8 | 间接 |
| PyPI | lightgbm | 4.6.0 | 间接 |
| PyPI | lightgbm-ray | 0.1.9 | 间接 |
| PyPI | locket | 1.0.0 | 间接 |
| PyPI | lz4 | 4.4.5 | 间接 |
| PyPI | mako | 1.3.12 | 间接 |
| PyPI | markdown-it-py | 4.0.0 | 间接 |
| PyPI | markupsafe | 3.0.3 | 间接 |
| PyPI | matplotlib | 3.10.8 | 间接 |
| PyPI | mdformat | 1.0.0 | 间接 |
| PyPI | mdurl | 0.1.2 | 间接 |
| PyPI | mkdocstrings-parser | 0.0.1 | 间接 |
| PyPI | mlflow | 3.14.0 | 间接 |
| PyPI | mlflow-skinny | 3.14.0 | 间接 |
| PyPI | mlflow-tracing | 3.14.0 | 间接 |
| PyPI | mlforecast | 1.1.0 | 间接 |
| PyPI | msal | 1.34.0 | 间接 |
| PyPI | msal-extensions | 1.3.1 | 间接 |
| PyPI | msgpack | 1.2.1 | 间接 |
| PyPI | multidict | 6.7.0 | 间接 |
| PyPI | mypy | 1.19.1 | 间接 |
| PyPI | mypy-extensions | 1.1.0 | 间接 |
| PyPI | nodeenv | 1.10.0 | 间接 |
| PyPI | numpy | 2.2.6 | 间接 |
| PyPI | numpy | 2.4.1 | 间接 |
| PyPI | oauthlib | 3.3.1 | 间接 |
| PyPI | opentelemetry-api | 1.39.1 | 间接 |
| PyPI | opentelemetry-proto | 1.39.1 | 间接 |
| PyPI | opentelemetry-sdk | 1.39.1 | 间接 |
| PyPI | opentelemetry-semantic-conventions | 0.60b1 | 间接 |
| PyPI | packaging | 25.0 | 间接 |
| PyPI | partd | 1.4.2 | 间接 |
| PyPI | pathspec | 1.0.3 | 间接 |
| PyPI | patsy | 1.0.2 | 间接 |
| PyPI | pillow | 12.2.0 | 间接 |
| PyPI | pip-licenses | 5.5.0 | 间接 |
| PyPI | platformdirs | 4.5.1 | 间接 |
| PyPI | plotly | 6.7.0 | 间接 |
| PyPI | pluggy | 1.6.0 | 间接 |
| PyPI | polars | 1.31.0 | 间接 |
| PyPI | pre-commit | 4.5.1 | 间接 |
| PyPI | prettytable | 3.17.0 | 间接 |
| PyPI | propcache | 0.4.1 | 间接 |
| PyPI | proto-plus | 1.27.0 | 间接 |
| PyPI | protobuf | 6.33.5 | 间接 |
| PyPI | psutil | 7.2.1 | 间接 |
| PyPI | py-cpuinfo | 9.0.0 | 间接 |
| PyPI | py4j | 0.10.9.9 | 间接 |
| PyPI | pyarrow | 23.0.1 | 间接 |
| PyPI | pyasn1 | 0.6.3 | 间接 |
| PyPI | pyasn1-modules | 0.4.2 | 间接 |
| PyPI | pycparser | 3.0 | 间接 |
| PyPI | pydantic | 2.12.5 | 间接 |
| PyPI | pydantic-core | 2.41.5 | 间接 |
| PyPI | pygments | 2.20.0 | 间接 |
| PyPI | pyjwt | 2.13.0 | 间接 |
| PyPI | pymeeus | 0.5.12 | 间接 |
| PyPI | pyparsing | 3.3.2 | 间接 |
| PyPI | pyspark | 4.1.1 | 间接 |
| PyPI | pytest | 9.0.3 | 间接 |
| PyPI | pytest-benchmark | 5.2.3 | 间接 |
| PyPI | pytest-codspeed | 4.2.0 | 间接 |
| PyPI | pytest-cov | 7.0.0 | 间接 |
| PyPI | pytest-rerunfailures | 16.1 | 间接 |
| PyPI | pytest-xdist | 3.8.0 | 间接 |
| PyPI | python-dateutil | 2.9.0.post0 | 间接 |
| PyPI | python-dotenv | 1.2.2 | 间接 |
| PyPI | pytz | 2025.2 | 间接 |
| PyPI | pywin32 | 311 | 间接 |
| PyPI | pyyaml | 6.0.3 | 间接 |
| PyPI | ray | 2.55.0 | 间接 |
| PyPI | referencing | 0.37.0 | 间接 |
| PyPI | requests | 2.33.0 | 间接 |
| PyPI | requests-oauthlib | 2.0.0 | 间接 |
| PyPI | rich | 14.2.0 | 间接 |
| PyPI | rpds-py | 0.30.0 | 间接 |
| PyPI | rsa | 4.9.1 | 间接 |
| PyPI | ruff | 0.14.14 | 间接 |
| PyPI | s3fs | 2026.1.0 | 间接 |
| PyPI | scipy | 1.15.3 | 间接 |
| PyPI | scipy | 1.17.0 | 间接 |
| PyPI | setuptools | 80.10.1 | 间接 |
| PyPI | six | 1.17.0 | 间接 |
| PyPI | skops | 0.13.0 | 间接 |
| PyPI | smmap | 5.0.2 | 间接 |
| PyPI | sortedcontainers | 2.4.0 | 间接 |
| PyPI | sqlalchemy | 2.0.46 | 间接 |
| PyPI | sqlparse | 0.5.5 | 间接 |
| PyPI | starlette | 1.3.1 | 间接 |
| PyPI | statsmodels | 0.14.6 | 间接 |
| PyPI | tblib | 3.2.2 | 间接 |
| PyPI | threadpoolctl | 3.6.0 | 间接 |
| PyPI | tomli | 2.4.0 | 间接 |
| PyPI | toolz | 1.1.0 | 间接 |
| PyPI | tornado | 6.5.7 | 间接 |
| PyPI | tqdm | 4.67.1 | 间接 |
| PyPI | triad | 1.0.0 | 间接 |
| PyPI | typing-extensions | 4.15.0 | 间接 |
| PyPI | typing-inspection | 0.4.2 | 间接 |
| PyPI | tzdata | 2025.3 | 间接 |
| PyPI | urllib3 | 2.7.0 | 间接 |
| PyPI | uvicorn | 0.40.0 | 间接 |
| PyPI | virtualenv | 20.36.1 | 间接 |
| PyPI | waitress | 3.0.2 | 间接 |
| PyPI | wcwidth | 0.3.1 | 间接 |
| PyPI | werkzeug | 3.1.6 | 间接 |
| PyPI | wrapt | 2.0.1 | 间接 |
| PyPI | xgboost | 2.0.3 | 间接 |
| PyPI | xgboost-ray | 0.1.19 | 间接 |
| PyPI | xlrd | 2.0.2 | 间接 |
| PyPI | xyzservices | 2025.11.0 | 间接 |
| PyPI | yarl | 1.22.0 | 间接 |
| PyPI | zict | 3.0.0 | 间接 |
| PyPI | zipp | 3.23.0 | 间接 |
依赖安全公告 0
安装 pypi:mlforecast@1.1.0 会引入 24 个包（直接与传递）：其中 0 个存在已知公告，0 个为直接依赖。
没有已知公告影响已评估的依赖。
公告表示依赖图中记录的版本落入某条公告的受影响范围。可达性未经分析，且依赖图包含开发与测试的版本固定——某项发现可能只涉及工具链而非交付的软件。
原始 JSON 报告 机器可读
{
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{
"key": "contributors",
"name": "Contributors",
"detail": "project has 5 contributing companies or organizations",
"points": 2.5,
"status": "met",
"details": [],
"max_points": 2.5
},
{
"key": "dangerous_workflow",
"name": "Dangerous-Workflow",
"detail": "no dangerous workflow patterns detected",
"points": 10,
"status": "met",
"details": [],
"max_points": 10
},
{
"key": "dependency_update_tool",
"name": "Dependency-Update-Tool",
"detail": "update tool detected",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
},
{
"key": "fuzzing",
"name": "Fuzzing",
"detail": "project is not fuzzed",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "license",
"name": "License",
"detail": "license file detected",
"points": 2.5,
"status": "met",
"details": [],
"max_points": 2.5
},
{
"key": "maintained",
"name": "Maintained",
"detail": "30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
},
{
"key": "packaging",
"name": "Packaging",
"detail": "packaging workflow not detected",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 5
},
{
"key": "pinned_dependencies",
"name": "Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 4",
"points": 2,
"status": "partial",
"details": [],
"max_points": 5
},
{
"key": "sast",
"name": "SAST",
"detail": "SAST tool is not run on all commits -- score normalized to 5",
"points": 2.5,
"status": "partial",
"details": [],
"max_points": 5
},
{
"key": "security_policy",
"name": "Security-Policy",
"detail": "security policy file not detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "signed_releases",
"name": "Signed-Releases",
"detail": "no releases found",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 7.5
},
{
"key": "token_permissions",
"name": "Token-Permissions",
"detail": "detected GitHub workflow tokens with excessive permissions",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
},
{
"key": "vulnerabilities",
"name": "Vulnerabilities",
"detail": "16 existing vulnerabilities detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
}
]
},
{
"key": "dependency_advisories",
"band": "excellent",
"name": "Dependency advisories",
"note": "Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:mlforecast@1.1.0 runtime dependency closure — what installing the published package pulls in — 24 packages. Reachability is not analyzed.",
"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
"no_advisories_left_outstanding"
]
}
},
{
"code": "weights_renormalized",
"params": {}
},
{
"code": "advisories_scope_published",
"params": {
"package": "pypi:mlforecast@1.1.0",
"assessed": 24
}
},
{
"code": "advisories_reachability",
"params": {}
}
],
"value": 100,
"inputs": {
"source": "osv",
"advisories": 0,
"affected_packages": 0,
"assessed_packages": 24,
"unassessed_packages": 0,
"affected_by_severity": "none",
"direct_affected_packages": 0
},
"components": [
{
"key": "direct_dependencies_free_of_known_advisories",
"name": "Direct dependencies free of known advisories",
"detail": "no direct dependency carries a known advisory",
"points": 35,
"status": "met",
"details": [
{
"code": "no_direct_advisories",
"params": {}
}
],
"max_points": 35
},
{
"key": "indirect_dependencies_free_of_known_advisories",
"name": "Indirect dependencies free of known advisories",
"detail": "no indirect dependency carries a known advisory",
"points": 25,
"status": "met",
"details": [
{
"code": "no_indirect_advisories",
"params": {}
}
],
"max_points": 25
},
{
"key": "no_advisories_left_outstanding",
"name": "No advisories left outstanding",
"detail": "no advisory carries a publication date",
"points": 0,
"status": "excluded",
"details": [
{
"code": "advisories_no_publication_date",
"params": {}
}
],
"max_points": 40
}
]
},
{
"key": "malicious_dependencies",
"band": "excellent",
"name": "Malicious dependencies",
"note": null,
"notes": [],
"value": 100,
"inputs": {
"source": "osv",
"meaning": "reported as a malicious package by the OpenSSF corpus; the remedy is removal or moving off the compromised name, never an upgrade of the same artifact. Versions the registry has since pulled are listed but not scored",
"packages": [],
"red_flag": false,
"assessed_packages": 24,
"malicious_packages": 0,
"direct_malicious_packages": 0,
"withdrawn_malicious_packages": 0,
"installable_malicious_packages": 0
},
"components": [
{
"key": "no_dependency_reported_as_a_malicious_package",
"name": "No dependency reported as a malicious package",
"detail": "no dependency is reported as a malicious package",
"points": 100,
"status": "met",
"details": [
{
"code": "no_malicious_dependencies",
"params": {}
}
],
"max_points": 100
}
]
},
{
"key": "high_risk_jurisdiction_exposure",
"band": "excellent",
"name": "High-Risk Jurisdiction Exposure",
"note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
"notes": [
{
"code": "jurisdiction_evidence_limits",
"params": {}
}
],
"value": 100,
"inputs": {
"meaning": "self-published location evidence; not nationality or citizenship",
"red_flag": false,
"exposures": [],
"policy_countries": [
"Russia",
"Iran",
"North Korea"
],
"review_only_matches": 0,
"assessed_self_published_locations": 10
},
"components": [
{
"key": "policy_exposure_multiplier",
"name": "Policy exposure multiplier",
"detail": "no confirmed policy-scope location match",
"points": 100,
"status": "met",
"details": [
{
"code": "jurisdiction_no_match",
"params": {}
}
],
"max_points": 100
}
]
}
],
"description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
},
{
"key": "ai_readiness",
"band": "moderate",
"name": "AI Readiness",
"value": 54,
"weight": 0,
"metrics": [
{
"key": "ai_agent_context",
"band": "critical",
"name": "Agent context & guidance",
"note": "Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.",
"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
"legible_commit_history"
]
}
},
{
"code": "weights_renormalized",
"params": {}
}
],
"value": 1,
"inputs": {
"has_llms_txt": false,
"legible_history_share": null,
"agent_instruction_files": [],
"agent_instruction_max_bytes": null
},
"components": [
{
"key": "agent_instructions",
"name": "Agent instructions",
"detail": "no CLAUDE.md / AGENTS.md / editor rules",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_instructions",
"params": {}
}
],
"max_points": 45
},
{
"key": "machine_readable_docs_llms_txt",
"name": "Machine-readable docs (llms.txt)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 15
},
{
"key": "legible_commit_history",
"name": "Legible commit history",
"detail": "no data",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 40
}
]
},
{
"key": "ai_verify_loop",
"band": "excellent",
"name": "Verify loop (build / test / typecheck)",
"note": "Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.",
"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
"demonstrated_agent_practice"
]
}
},
{
"code": "weights_renormalized",
"params": {}
}
],
"value": 90,
"inputs": {
"has_nix": false,
"has_tests": true,
"lockfiles": [
"uv.lock"
],
"has_dockerfile": false,
"typed_language": false,
"bootstrap_files": [
"Makefile"
],
"has_devcontainer": false,
"has_linter_config": true,
"typecheck_configs": [
"mlforecast/py.typed"
],
"agent_commit_share": null,
"toolchain_manifests": [],
"dependency_bot_commit_share": 0
},
"components": [
{
"key": "one_command_bootstrap",
"name": "One-command bootstrap",
"detail": "Makefile",
"points": 18,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "Makefile"
}
}
],
"max_points": 18
},
{
"key": "automated_tests",
"name": "Automated tests",
"detail": null,
"points": 22,
"status": "met",
"details": [],
"max_points": 22
},
{
"key": "lint_format_config",
"name": "Lint / format config",
"detail": null,
"points": 11,
"status": "met",
"details": [],
"max_points": 11
},
{
"key": "static_type_checking",
"name": "Static type checking",
"detail": "mlforecast/py.typed",
"points": 11,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "mlforecast/py.typed"
}
}
],
"max_points": 11
},
{
"key": "reproducible_environment",
"name": "Reproducible environment",
"detail": "lockfile",
"points": 10,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "lockfile"
}
}
],
"max_points": 10
},
{
"key": "demonstrated_agent_practice",
"name": "Demonstrated agent practice",
"detail": "no data",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 10
},
{
"key": "automated_maintenance",
"name": "Automated maintenance",
"detail": "dependency automation configured, none observed in the sampled commits",
"points": 5,
"status": "partial",
"details": [
{
"code": "dependency_bot_config_only",
"params": {}
}
],
"max_points": 8
},
{
"key": "openssf_scorecard_pinned_dependencies",
"name": "OpenSSF Scorecard: Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 4",
"points": 4,
"status": "partial",
"details": [],
"max_points": 10
}
]
},
{
"key": "ai_code_legibility",
"band": "good",
"name": "Code legibility for models",
"note": null,
"notes": [],
"value": 77,
"inputs": {
"primary_language": "Python",
"largest_source_bytes": 154837,
"source_files_sampled": 64,
"oversized_source_files": 6
},
"components": [
{
"key": "type_checkable_code",
"name": "Type-checkable code",
"detail": "Python with type-check config (mlforecast/py.typed)",
"points": 27,
"status": "partial",
"details": [
{
"code": "typecheck_config_language",
"params": {
"files": "mlforecast/py.typed",
"language": "Python"
}
}
],
"max_points": 45
},
{
"key": "manageable_file_sizes",
"name": "Manageable file sizes",
"detail": "6/64 source files over 60KB",
"points": 49.8,
"status": "partial",
"details": [
{
"code": "oversized_source_files",
"params": {
"kb": 60,
"sampled": 64,
"oversized": 6
}
}
],
"max_points": 55
}
]
},
{
"key": "ai_interfaces",
"band": "at_risk",
"name": "Machine-readable interfaces",
"note": null,
"notes": [],
"value": 40,
"inputs": {
"example_dirs": [
"notebooks"
],
"has_mcp_signal": false,
"api_schema_files": []
},
"components": [
{
"key": "api_schema_openapi_graphql_proto",
"name": "API schema (OpenAPI/GraphQL/proto)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 40
},
{
"key": "mcp_server",
"name": "MCP server",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 20
},
{
"key": "runnable_examples",
"name": "Runnable examples",
"detail": "notebooks",
"points": 40,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "notebooks"
}
}
],
"max_points": 40
}
]
}
],
"description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
}
],
"metrics_version": "1.13.0"
},
"warnings": [],
"report_type": "repository",
"generated_at": "2026-07-21T04:07:38.283893Z",
"schema_version": "0.17.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/n/Nixtla/mlforecast.svg",
"full_name": "Nixtla/mlforecast",
"license_state": "standard",
"license_spdx": "Apache-2.0"
}