Публічний реєстрУ зоні ризику
Звіт про здоров'я програмного забезпеченнясхема 0.26.0 · метрики 1.13.0 · 2026-07-22 00:12 UTC
pymc-devs / pymc
Bayesian Modeling and Probabilistic Programming in Python
pymc-devs/pymc має індекс здоров’я 44 зі 100, що відповідає смузі «У зоні ризику». Найвищий показник — Vitality (98/100), найнижчий — AI Readiness (52/100). Останнє оновлення — сьогодні. Більшість нещодавньої роботи виконують 6 учасників.
44
загалом / 100Індекс здоров'я програмного забезпечення
Метрики згруповано у зважені категорії на шкалі 1–100. Загальна оцінка починається як їхнє середнє; коли публічні дані активують Політику юрисдикцій високого ризику, рейтинг коригується й отримує верхню межу 49 («Під ризиком»). Готовність до ШІ не входить до індексу.
44
Відмінний85-100Зразковий; відповідає практично всім перевіреним критеріям
Добрий70-84Здоровий; незначні прогалини
Помірний50-69Прийнятний, але з помітними прогалинами; рекомендовано перевірку
У зоні ризику30-49Суттєві слабкі місця; впровадження потребує обережності
Критичний1-29Серйозні проблеми (покинутий, єдиний мейнтейнер, без базової гігієни)
Профіль оцінок
Кожна вісь — окрема категорія. Форма важить більше, ніж середнє: здоровий об'єкт заповнює всю фігуру, тоді як профіль із піками та провалами означає, що сила в одному вимірі маскує ризик в іншому.
Політика юрисдикцій високого ризику застосовує множник 50% до зваженого загального індексу здоров’я і встановлює для нього межу «У зоні ризику» на рівні 49.
Власність
PyMCОрганізація
619 підписників44 публічні репозиторіїз трав. 2009 р.
За цим репозиторієм стоїть організація — спільна, підзвітна опіка, здатна пережити будь-якого окремого мейнтейнера.
Пакетні екосистеми
| Реєстр | Пакет | Версія | Завантажень / міс | Версії | Остання публікація |
|---|---|---|---|---|---|
| PyPI | pymc | 6.1.0 | - | 101 | 14 днів тому |
Метрики за категоріями
Життєздатність
Чи живий проєкт — чи пишеться код і чи виходять релізи?
98
Активність розробки
96Відмінний
Як обчислюється оцінка
| 36/36 | Свіжість push — останній push 0 дн. тому |
| 32.5/36 | Ритм комітів — 47/52 тижнів із комітами |
| 18/18 | Обсяг комітів — 263 комітів за останній рік |
| 10/10 | OpenSSF Scorecard: Maintained — 30 commit(s) and 18 issue activity found in the last 90 days -- score normalized to 10 |
Використані вхідні дані
| commits_last_year | 263 |
| human_commit_share | 0,98 |
| days_since_last_push | 0 |
| active_weeks_last_year | 47 |
Дисципліна релізів
100Відмінний
Як обчислюється оцінка
| 27/27 | Випускає релізи — опубліковано 100 релізів |
| 36/36 | Свіжість релізів — останній реліз 14 дн. тому |
| 27/27 | Ритм релізів — реліз кожні ~18 дн. |
| 0/10 | OpenSSF Scorecard: Signed-Releases — немає даних |
Використані вхідні дані
| releases_count | 100 |
| latest_release_tag | v6.1.0 |
| releases_from_tags | ні |
| days_since_latest_release | 14 |
| mean_days_between_releases | 18 |
Виключено з оцінювання (немає даних або не застосовно): OpenSSF Scorecard: Signed-Releases. Залишкові ваги перенормовано.
Спільнота та впровадження
Чи має проєкт користувачів, завантаження, увагу та влаштовані умови для контриб’юторів?
92
Популярність і впровадження
98Відмінний
Як обчислюється оцінка
| 60/60 | Зірки — 9 679 зірок |
| 25/25 | Форки — 2 265 форків |
| 13/15 | Спостерігачі — 218 спостерігачів |
Використані вхідні дані
| forks | 2 265 |
| stars | 9 679 |
| watchers | 218 |
| growth_state | organic |
| growth_factor_pct | 100 |
Здоров'я спільноти
86Відмінний
Як обчислюється оцінка
| 22.5/22.5 | README |
| 16.9/22.5 | Ліцензія — файл ліцензії наявний, не є визнаною ліцензією |
| 18/18 | Настанови CONTRIBUTING |
| 13.5/13.5 | Кодекс поведінки |
| 0/7.2 | Шаблон issue |
| 6.3/6.3 | Шаблон PR |
Використані вхідні дані
| has_readme | так |
| has_license | так |
| has_contributing | так |
| has_issue_template | ні |
| has_code_of_conduct | так |
| has_pull_request_template | так |
Сталість та врядування
Чи переживе проєкт своїх людей — бас-фактор, реактивність, хто за ним стоїть і як супроводжуються пакети?
88
Стійкість мейнтейнерів (бас-фактор)
91Відмінний
Як обчислюється оцінка
| 48.6/54 | Бас-фактор — на 6 контриб’ютор(ів) припадає половина всіх комітів |
| 19.2/22.5 | Розподіл комітів — головний контриб’ютор — автор 14% комітів |
| 13.5/13.5 | Широта контриб’юторів — 98 контриб’юторів |
| 10/10 | OpenSSF Scorecard: Contributors — project has 70 contributing companies or organizations |
Використані вхідні дані
| bus_factor | 6 |
| contributors_sampled | 98 |
| top_contributor_share | 0,145 |
Реактивність на issue та PR
88Відмінний
Як обчислюється оцінка
| 42.7/46.8 | Вирішення issue — закрито 91% issue |
| 31.3/38.3 | Прийняття PR — злито 3 661/4 480 вирішених PR |
| 13.5/15 | OpenSSF Scorecard: Code-Review — Found 12/13 approved changesets -- score normalized to 9 |
Використані вхідні дані
| merged_prs | 3 661 |
| open_issues | 305 |
| closed_issues | 3 220 |
| issue_closed_ratio | 0,913 |
| closed_unmerged_prs | 819 |
Власність та опіка
74Добрий
Як обчислюється оцінка
| 30/30 | Підтримка власника — у власності організації |
| 0/20 | Верифікований домен |
| 20.1/25 | Охоплення власника — 619 підписників у pymc-devs |
| 24/25 | Послужний список — 44 публічних репозиторіїв, вік облікового запису ~17 р. |
Використані вхідні дані
| followers | 619 |
| owner_type | Organization |
| is_verified | — |
| owner_login | pymc-devs |
| public_repos | 44 |
| account_age_days | 6 286 |
Супровід пакетів
100Відмінний
Як обчислюється оцінка
| 25/25 | Опубліковано й доступно — 1 пакет(ів) у pypi |
| 35/35 | Свіжість публікацій — остання публікація 14 дн. тому |
| 20/20 | Історія версій — 101 опублікованих версій |
| 20/20 | Не застарілий — активний, не deprecated і не yanked |
Використані вхідні дані
| packages | pymc |
| ecosystems | pypi |
| any_deprecated | ні |
| min_days_since_publish | 14 |
Інженерна якість
Чи наявні базові інженерні практики та документація?
96
Інженерні практики
94Відмінний
Як обчислюється оцінка
| 24/24 | Процеси CI — 10 процес(ів) CI |
| 24/24 | Наявні тести |
| 16/16 | Конфігурація лінтера |
| 9.6/9.6 | Pre-commit-хуки |
| 0/6.4 | .editorconfig |
| 20/20 | OpenSSF Scorecard: CI-Tests — 13 out of 13 merged PRs checked by a CI test -- score normalized to 10 |
Використані вхідні дані
| has_ci | так |
| has_tests | так |
| has_editorconfig | ні |
| has_linter_config | так |
| has_precommit_config | так |
Документація
100Відмінний
Як обчислюється оцінка
| 30/30 | README |
| 25/25 | Каталог документації |
| 15/15 | Сайт документації / домашня сторінка — https://www.pymc.io |
| 10/10 | Опис репозиторію |
| 10/10 | Теми — 7 тем |
| 10/10 | Wiki |
Використані вхідні дані
| topics | python, statistical-analysis, bayesian-inference, mcmc, variational-inference, probabilistic-programming, pytensor |
| has_wiki | так |
| homepage | https://www.pymc.io |
| has_readme | так |
| has_docs_dir | так |
| has_description | так |
Безпека
Чи міцні видимі практики безпеки й ланцюга постачання, без непослабленої пов’язаності з юрисдикціями високого ризику?
52
Стан безпеки
40У зоні ризику
Попередження про юрисдикційний ризик
Виявлено пов’язаність із юрисдикцією високого ризику
1 профілі ключових контриб’юторівRussiaБезпека й загальний рейтинг: межа «Під ризиком»
Сигнал ґрунтується на публічних локаціях, самостійно вказаних у профілях. Він не визначає національність, громадянство, наміри, санкційний статус чи надійність людини.
Як оцінюється юрисдикційна пов’язаністьЯк обчислюється оцінка
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — немає даних |
| 2.5/2.5 | CI-Tests — 13 out of 13 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 |
| 6.8/7.5 | Code-Review — Found 12/13 approved changesets -- score normalized to 9 |
| 2.5/2.5 | Contributors — project has 70 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 |
| 2.2/2.5 | Ліцензія — license file detected |
| 7.5/7.5 | Maintained — 30 commit(s) and 18 issue activity found in the last 90 days -- score normalized to 10 |
| 5/5 | Packaging — packaging workflow detected |
| 4/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 8 |
| 5/5 | SAST — SAST tool is run on all commits |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — немає даних |
| 7.5/7.5 | Token-Permissions — GitHub workflow tokens follow principle of least privilege |
| 3/7.5 | Vulnerabilities — 6 existing vulnerabilities detected |
Використані вхідні дані
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 7,9 |
| high_risk_jurisdiction_cap | 49 |
| high_risk_jurisdiction_multiplier | 50 |
| security_posture_after_multiplier | 40 |
| security_posture_before_jurisdiction | 79 |
Виключено з оцінювання (немає даних або не застосовно): branch_protection, signed_releases. Залишкові ваги перенормовано. Політика юрисдикцій високого ризику застосовує множник 50% і встановлює для стану безпеки межу «У зоні ризику» на рівні 49.
Сповіщення про залежності
100Відмінний
Як обчислюється оцінка
| 35/35 | Прямі залежності без відомих сповіщень — жодна пряма залежність не має відомих сповіщень |
| 25/25 | Непрямі залежності без відомих сповіщень — жодна непряма залежність не має відомих сповіщень |
| 0/40 | Немає задавнених сповіщень — жодне сповіщення не має дати публікації |
Використані вхідні дані
| source | osv |
| advisories | 0 |
| affected_packages | 0 |
| assessed_packages | 32 |
| unassessed_packages | 0 |
| affected_by_severity | none |
| direct_affected_packages | 0 |
Виключено з оцінювання (немає даних або не застосовно): Немає задавнених сповіщень. Залишкові ваги перенормовано. Звірено з runtime-замиканням залежностей pypi:pymc@6.1.0 — тим, що тягне за собою встановлення опублікованого пакета, — 32 пакетів. Досяжність не аналізується.
Готовність до ШІ
Наскільки репозиторій оснащений для розробки та супроводу за участі ШІ-агентів? Незалежний, експериментальний бейдж — вага 0.0, тож він подається окремо і не впливає на загальний індекс здоров'я.
52
Контекст і настанови для агентів
23Критичний
Як обчислюється оцінка
| 0/45 | Інструкції для агентів — немає CLAUDE.md / AGENTS.md / правил редактора |
| 0/15 | Машиночитана документація (llms.txt) |
| 22.9/40 | Читабельна історія комітів — намір зазначено у 42 з 98 людських комітів (структурований заголовок або пояснювальний текст) |
Використані вхідні дані
| has_llms_txt | ні |
| legible_history_share | 0,429 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
Як обчислюється оцінка
| 18/18 | Розгортання однією командою — Makefile |
| 22/22 | Автоматизовані тести |
| 11/11 | Конфігурація лінтера / форматера |
| 0/11 | Статична перевірка типів |
| 10/10 | Відтворюване середовище — Dockerfile |
| 0/10 | Підтверджена практика роботи з агентами — серед останніх 100 комітів немає створених агентом |
| 8/8 | Автоматизоване супроводження — 2 з останніх 100 комітів — автоматичні оновлення залежностей |
| 8/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 8 |
Використані вхідні дані
| has_nix | ні |
| has_tests | так |
| lockfiles | — |
| has_dockerfile | так |
| typed_language | ні |
| bootstrap_files | Makefile |
| has_devcontainer | ні |
| has_linter_config | так |
| typecheck_configs | — |
| agent_commit_share | 0 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0,02 |
Читабельність коду для моделей
53Помірний
Як обчислюється оцінка
| 0/45 | Типізований код — Python без конфігурації перевірки типів |
| 52.9/55 | Керовані розміри файлів — 10/259 файлів вихідного коду понад 60 КБ |
Використані вхідні дані
| primary_language | Python |
| largest_source_bytes | 126 843 |
| source_files_sampled | 259 |
| oversized_source_files | 10 |
Машиночитні інтерфейси
40У зоні ризику
Як обчислюється оцінка
| 0/40 | Схема API (OpenAPI/GraphQL/proto) |
| 0/20 | Сервер MCP |
| 40/40 | Придатні до запуску приклади — notebooks |
Використані вхідні дані
| example_dirs | notebooks |
| has_mcp_signal | ні |
| api_schema_files | — |
Ключові факти
9 679зірок GitHub
98контриб'юторів
263комітів за останні 12 місяців
0днів від останнього пушу
100релізів
6бас-фактор
305відкритих issue
PyPIпакетних екосистем
Докладніше
Історія зірок і форків 9 679 ★ / 2 265 ⇿
9 679Зірки
2 265Форки
93Релізи
Коли додано кожну зірку й форк — зібрано з GitHub і згруповано за днями. Кумулятивне зростання розміщено просто над денними додаваннями, з яких воно складається, тож їх видно одне проти одного: рівномірне органічне накопичення виглядає зовсім інакше, ніж різкий короткочасний сплеск. Там, де цю різницю можна виміряти, її подано як автентичність росту.
Показано лише найновішу історію — цей репозиторій перевищує вікно збору, тож найраніша історія не захоплена.
Мажорні 3Мінорні 33Патчі 51
Кожна точка охоплює 5 днів.
OpenSSF Scorecard 7.9 / 10
7.9сукупно
Незалежна, не прив'язана до інструментів оцінка безпеки від відкритого проєкту OpenSSF Scorecard. Кожна перевірка винагороджує практику безпеки, а не інструмент конкретного постачальника. Перевірки, які Scorecard не зміг визначити, позначено н/д і виключено з оцінки безпеки (вони ніколи не зараховуються як нуль).
| 10 | Binary-Artifacts | no binaries found in the repo |
| н/д | Branch-Protection | internal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md |
| 10 | CI-Tests | 13 out of 13 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 |
| 9 | Code-Review | Found 12/13 approved changesets -- score normalized to 9 |
| 10 | Contributors | project has 70 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 |
| 9 | License | license file detected |
| 10 | Maintained | 30 commit(s) and 18 issue activity found in the last 90 days -- score normalized to 10 |
| 10 | Packaging | packaging workflow detected |
| 8 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 8 |
| 10 | SAST | SAST tool is run on all commits |
| 0 | Security-Policy | security policy file not detected |
| н/д | Signed-Releases | no releases found |
| 10 | Token-Permissions | GitHub workflow tokens follow principle of least privilege |
| 4 | Vulnerabilities | 6 existing vulnerabilities detected |
Усі залежності 34
Повний розв'язаний набір залежностей із графа залежностей GitHub: 0 прямих і 34 непрямих (транзитивних) пакетів. Транзитивне замикання є повним, коли в репозиторії закомічено lockfile.
| Реєстр | Пакет | Версія | Зв'язок |
|---|---|---|---|
| PyPI | arviz | — | непряма |
| PyPI | cachetools | — | непряма |
| PyPI | cloudpickle | — | непряма |
| PyPI | ipython | — | непряма |
| PyPI | jupyter-sphinx | — | непряма |
| PyPI | mcbackend | — | непряма |
| PyPI | mypy | 1.19.1 | непряма |
| PyPI | myst-nb | — | непряма |
| PyPI | netcdf4 | — | непряма |
| PyPI | numdifftools | — | непряма |
| PyPI | numpy | — | непряма |
| PyPI | numpydoc | — | непряма |
| PyPI | nutpie | — | непряма |
| PyPI | pandas | — | непряма |
| PyPI | polyagamma | — | непряма |
| PyPI | pre-commit | — | непряма |
| PyPI | pymc-sphinx-theme | — | непряма |
| PyPI | pytensor | — | непряма |
| PyPI | pytest | — | непряма |
| PyPI | pytest-cov | — | непряма |
| PyPI | rich | — | непряма |
| PyPI | scipy | — | непряма |
| PyPI | sphinx | — | непряма |
| PyPI | sphinx-copybutton | — | непряма |
| PyPI | sphinx-design | — | непряма |
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Сповіщення про залежності 0
Встановлення pypi:pymc@6.1.0 тягне 32 пакетів, прямих і транзитивних: 0 мають відомі сповіщення, з них 0 — прямі залежності.
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Звіт у форматі JSON машиночитний
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"headline": "Fix copy-pasted MeasurableRound docstring",
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"body": "* Adds a state property that mean and std are guaranteed to be on.\n\nUses the recently merged constrain_values utility from\npymc.model.transform_values to map unconstrained parameters\nback to the original constrained space, replacing the custom\n_untransform_tensor helper.",
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"headline": "Adds a state property that mean and std are guaranteed to be on. (#8319)",
"author_name": "Rob Zinkov",
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"body": "Inverted by applying the opposite operation to the value and re-applying\nthe original to the logp, mirroring MeasurableDimShuffle, including its\ntruncation of support dimensions consumed by the logp.\n\nClaims preserve the assumption that support axes are the rightmost\npositions: splits only inflate a\n[…]\nclasses after). Once pytensor canonicalizes provable\nReshapes into JoinDims/SplitDims (pymc-devs/pytensor#2280), reshape,\nravel and flatten of measurable variables will be covered by the same\nrewrite.",
"is_bot": false,
"headline": "Infer logprob of join_dims and split_dims",
"author_name": "Ricardo Vieira",
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"body": "ScalarFromTensor, SpecifyAssumptions and DeepCopyOp leave the value\nuntouched, so measurability and logp/logcdf/icdf pass through to their\ninput. The assumptions of a SpecifyAssumptions are deliberately not\nstamped onto the value: it is an arbitrary query point (e.g., a sampler\nproposal), and a dens\n[…]\nst density would return -inf.\n\nPorted from pymc-extras, which registers an equivalent rewrite under a\ndifferent name (whichever claims the node first wins; the other no-ops\non the MeasurableOp guard).",
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"headline": "Infer logprob through value-identity ops",
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"body": "Casts that preserve or widen the dtype kind (float->float, int->int,\nbool->int/float, int->float) pass the measure through untouched, per\nthe easy cases of #6983. Discretizing casts (float->int/bool) are not\nclaimed; they would need a censoring-style logp.\n\nCasts of discrete variables to float are only claimed when directly\nvalued: other rewrites classify discreteness by dtype, so an\nunrestricted claim would e.g. apply a continuous jacobian to\n2 * cast(poisson, 'float64').",
"is_bot": false,
"headline": "Infer logprob of non-discretizing casts",
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"body": "Rewritten as multiplication by a constant, reusing ScaleTransform and\nits sign-aware logcdf/icdf branches.",
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"headline": "Infer logprob of deg2rad and rad2deg",
"author_name": "Ricardo Vieira",
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"body": "Direct counterparts of the already-supported hyperbolic and erf/erfc\ntransforms. Monotonicity registration gives logcdf/icdf for free\n(arccos and erfcinv exercise the decreasing branch, which previously\nhad no icdf coverage).",
"is_bot": false,
"headline": "Infer logprob of arcsin, arccos, arctan, erfinv and erfcinv",
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"body": "The module held only the measurable Alloc/broadcast, while every other\nstructural op (DimShuffle, Join, Split, MakeVector) already lived in\nlogprob.tensor. The `shape` rewrite tag is renamed to `tensor` to match;\nonly the `basic` tag is ever queried.",
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"headline": "Merge logprob.shape into logprob.tensor",
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"body": "Broadcasting lowers to Alloc; the logp scores the base variable at the\nrepresentative entries and returns -inf where the value is inconsistent\nwith a broadcast, elementwise over the base's batch dimensions. The\nexpanded and broadcast dimensions are degenerate copies of the base\nentries, so they are \n[…]\nan of a transform once per copy), so the broadcast is only\nclaimed when directly valued. For the same reason the implicit\nbroadcasting of measurable inputs inside elemwise transforms is not\nattempted.",
"is_bot": false,
"headline": "Derive probability for broadcast operation",
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"headline": "Install jax from pip",
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"headline": "Implement icdf for Bernoulli",
"author_name": "Jacob Patterson-Stein",
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"headline": "Declare ndim_supp on value transforms",
"author_name": "Ricardo Vieira",
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"headline": "Deprecate DensityDist in favor of CustomDist (#8349)",
"author_name": "Karthik Laishetti",
"author_login": "laishettikarthik-tech",
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"headline": "Add utility to extract and reinsert deterministics",
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"body": "scipy's spline.integral now rejects non-0-dimensional bounds. When a\nbatched size prepends length-1 dims to x_points/pdf_points, squeeze them\nback to 1-D before building the spline and computing the normalization.",
"is_bot": false,
"headline": "Fix Interpolated logp for batched size with scipy 1.18",
"author_name": "Ricardo Vieira",
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"headline": "Handle pytest deprecation",
"author_name": "Ricardo Vieira",
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"headline": "Update installation instructions to use pip",
"author_name": "DrEntropy",
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"body": "LatentKron._build_prior was passing **kwargs (including dims) to the\ninternal pm.Normal rotated variable, but was not forwarding dims to\nthe final pm.Deterministic node representing the GP function f.\n\nThis made it inconsistent with gp.Latent and gp.TP, which already\ncorrectly pass dims to their Det\n[…]\nargs.get('dims', None) to pm.Deterministic in\n LatentKron._build_prior\n- Add test testLatentKronDims to verify that the GP variable returned\n by LatentKron.prior carries the correct named dimensions",
"is_bot": false,
"headline": "Add dims support to LatentKron GP prior",
"author_name": "Dhairya Motta",
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"headline": "Implement `constrain_values` and `unconstrain_values` utility (#8327)",
"author_name": "Rob Zinkov",
"author_login": "zaxtax",
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"headline": "Add link to code of conduct violation reporting form (#8332)",
"author_name": "Chris Fonnesbeck",
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"headline": "Fix regression where passing a model into `fit` stopped working (#8316)",
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"headline": "Change install command to use v6 (#8323)",
"author_name": "Chris Fonnesbeck",
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"headline": "Visually split tuning/draw phase in progress bar",
"author_name": "ricardoV94",
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"body": "Co-authored-by: Ricardo Vieira <28983449+ricardoV94@users.noreply.github.com>",
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"headline": "Add Poisson and NegativeBinomial to pymc.dims (#8305)",
"author_name": "Will Dean",
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"body": "Profiling-backed changes targeting the two tests where iteration\nreduction yields meaningful savings.\n\ntest_weakref_leak (89s on CI → estimated ~30s):\nObject count profiling shows memory state stabilizes at iteration 2.\nThe original test used 16 warmup iterations before checking — reduced\nto 3 warmu\n[…]\ne(3): compile\n time is 99.98% of cost, loop saves 0.6ms total\n- test_interpolated x_points 100k→20k: compilation is 91.8% of cost,\n array reduction saves ~296ms across 56 iterations\n\nAddresses #7686",
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"headline": "Speed up test_weakref_leak and test_step_args",
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"author_login": "mcwanza",
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"headline": "Fix non terminating speed/elapsed in marimo progress bar",
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"headline": "Fix display of nutpie progress bar in marimo",
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"headline": "Pin to non-failing micromamba-version",
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"body": "Apply bold brand theme (teal #12698A nodes, white text, dark gray\nlines) to the mermaid graph shown in marimo notebooks via _display_.\nFalls back gracefully to the default theme when marimo does not yet\nsupport the theme_variables parameter.",
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"headline": "feat: add PyMC brand styling to Model._display_ mermaid diagram",
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"oid": "3c6a3b290fdeb86d0b7dd8babd129958c86ab0ed",
"body": "Verify `import pymc` does not pull in arviz_base, arviz_plots,\narviz_stats, scipy submodules, zarr, or pytensor.sparse. Uses a\nbuiltins.__import__ hook to capture stack traces for diagnostics.",
"is_bot": false,
"headline": "Add eager-import regression test",
"author_name": "ricardoV94",
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"body": "Replace top-level arviz_base imports with lazy patterns:\n- backends/arviz.py: self-replacing stubs for dict_to_dataset and\n make_attrs, inline rcParams, copy requires decorator\n- backends/zarr.py: import make_attrs from backends.arviz stub\n- stats/convergence.py: function-local arviz_stats import\n\nTemporary workaround until arviz cleans up its eager imports",
"is_bot": false,
"headline": "Defer arviz_base import",
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"body": "Use module-level __getattr__ / __dir__ to lazily import arviz_stats\nand arviz_plots on first attribute access, keeping `import pymc` fast.",
"is_bot": false,
"headline": "Defer arviz_stats and arviz_plots import",
"author_name": "ricardoV94",
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"oid": "fc5e95231e86bc5b9f8be6681333b2bb221253c7",
"body": "Defer IPython registration in printing.py to avoid pulling in the\nheavy IPython dependency at `import pymc` time. The pretty printer\nis only registered when IPython is already loaded.",
"is_bot": false,
"headline": "Defer IPython import",
"author_name": "ricardoV94",
"author_login": "ricardoV94",
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"oid": "b769f5fb7fa201c4d9596e75fdf0950db91a0e5c",
"body": "Replace the eager ``from mcbackend import Backend, Run`` with\n``importlib.util.find_spec`` for the availability check and a lazy\nimport inside ``init_traces()``. The old try/except pulled in\nmcbackend at module load, which transitively imported arviz_base\nand all of scipy.",
"is_bot": false,
"headline": "Defer mcbackend import",
"author_name": "ricardoV94",
"author_login": "ricardoV94",
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"body": "Introduce lightweight marker base classes ``_ZarrChainBase`` and\n``_ZarrTraceBase`` in ``pymc.backends`` so that ``isinstance`` checks\nwork without importing the real zarr-backed classes (which pull in the\nheavy ``zarr`` dependency). The actual ``ZarrTrace`` is now exposed\nthrough ``__getattr__`` on the backends package.",
"is_bot": false,
"headline": "Defer zarr import",
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"description": "Does the project have users, downloads, attention, and a welcoming setup for contributors?"
},
{
"key": "governance",
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"name": "Sustainability & Governance",
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"weight": 0.24,
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{
"key": "maintainer_resilience",
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"note": null,
"notes": [],
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"components": [
{
"key": "bus_factor",
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"points": 48.6,
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{
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{
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{
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],
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{
"key": "openssf_scorecard_contributors",
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]
},
{
"key": "responsiveness",
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"notes": [],
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"open_issues": 305,
"closed_issues": 3220,
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"components": [
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{
"key": "pr_acceptance",
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"status": "partial",
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{
"key": "openssf_scorecard_code_review",
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]
},
{
"key": "stewardship",
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"notes": [],
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"is_verified": null,
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{
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"detail": null,
"points": 0,
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{
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"status": "partial",
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{
"key": "track_record",
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"points": 24,
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},
{
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]
},
{
"key": "package_maintenance",
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"note": null,
"notes": [],
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"inputs": {
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"ecosystems": "pypi",
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"min_days_since_publish": 14
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"components": [
{
"key": "published_resolvable",
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"status": "met",
"details": [
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"code": "packages_published",
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"count": 1,
"ecosystems": "pypi"
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}
],
"max_points": 25
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{
"key": "publish_recency",
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"detail": "latest publish 14 days ago",
"points": 35,
"status": "met",
"details": [
{
"code": "publish_recency",
"params": {
"days": 14
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}
],
"max_points": 35
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{
"key": "version_history",
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"detail": "101 published versions",
"points": 20,
"status": "met",
"details": [
{
"code": "published_versions",
"params": {
"count": 101
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}
],
"max_points": 20
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{
"key": "not_deprecated",
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"points": 20,
"status": "met",
"details": [
{
"code": "package_not_deprecated",
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}
],
"max_points": 20
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]
}
],
"description": "Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?"
},
{
"key": "engineering",
"band": "excellent",
"name": "Engineering Quality",
"value": 96,
"weight": 0.2,
"metrics": [
{
"key": "engineering_practices",
"band": "excellent",
"name": "Engineering practices",
"note": null,
"notes": [],
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"inputs": {
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"has_tests": true,
"has_editorconfig": false,
"has_linter_config": true,
"has_precommit_config": true
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"components": [
{
"key": "ci_workflows",
"name": "CI workflows",
"detail": "10 workflow(s)",
"points": 24,
"status": "met",
"details": [
{
"code": "ci_workflows",
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"count": 10
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}
],
"max_points": 24
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{
"key": "tests_present",
"name": "Tests present",
"detail": null,
"points": 24,
"status": "met",
"details": [],
"max_points": 24
},
{
"key": "linter_config",
"name": "Linter config",
"detail": null,
"points": 16,
"status": "met",
"details": [],
"max_points": 16
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{
"key": "pre_commit_hooks",
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"detail": null,
"points": 9.6,
"status": "met",
"details": [],
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},
{
"key": "editorconfig",
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"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 6.4
},
{
"key": "openssf_scorecard_ci_tests",
"name": "OpenSSF Scorecard: CI-Tests",
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"points": 20,
"status": "met",
"details": [],
"max_points": 20
}
]
},
{
"key": "documentation",
"band": "excellent",
"name": "Documentation",
"note": null,
"notes": [],
"value": 100,
"inputs": {
"topics": [
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"statistical-analysis",
"bayesian-inference",
"mcmc",
"variational-inference",
"probabilistic-programming",
"pytensor"
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"has_wiki": true,
"homepage": "https://www.pymc.io",
"has_readme": true,
"has_docs_dir": true,
"has_description": true
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"components": [
{
"key": "readme",
"name": "README",
"detail": null,
"points": 30,
"status": "met",
"details": [],
"max_points": 30
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{
"key": "documentation_directory",
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"detail": null,
"points": 25,
"status": "met",
"details": [],
"max_points": 25
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{
"key": "documentation_homepage_site",
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"status": "met",
"details": [],
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{
"key": "repository_description",
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"points": 10,
"status": "met",
"details": [],
"max_points": 10
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{
"key": "topics",
"name": "Topics",
"detail": "7 topics",
"points": 10,
"status": "met",
"details": [
{
"code": "topics_count",
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}
],
"max_points": 10
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{
"key": "wiki",
"name": "Wiki",
"detail": null,
"points": 10,
"status": "met",
"details": [],
"max_points": 10
}
]
}
],
"description": "Are baseline engineering and documentation practices in place?"
},
{
"key": "security",
"band": "moderate",
"name": "Security",
"value": 52,
"weight": 0.16,
"metrics": [
{
"key": "security_posture",
"band": "at_risk",
"name": "Security posture",
"note": "Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized. High-Risk Jurisdiction Policy applies a 50% multiplier to Security posture and gives it an At risk ceiling of 49.",
"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
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},
{
"code": "weights_renormalized",
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},
{
"code": "jurisdiction_posture_adjustment",
"params": {
"cap": 49,
"pct": 50
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}
],
"value": 40,
"inputs": {
"source": "openssf_scorecard",
"checks_evaluated": 16,
"scorecard_version": "v5.5.0",
"checks_inconclusive": 2,
"scorecard_aggregate": 7.9,
"high_risk_jurisdiction_cap": 49,
"high_risk_jurisdiction_multiplier": 50,
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"security_posture_before_jurisdiction": 79
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"components": [
{
"key": "binary_artifacts",
"name": "Binary-Artifacts",
"detail": "no binaries found in the repo",
"points": 7.5,
"status": "met",
"details": [],
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},
{
"key": "branch_protection",
"name": "Branch-Protection",
"detail": "internal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md",
"points": 0,
"status": "excluded",
"details": [
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"code": "no_data",
"params": {}
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],
"max_points": 7.5
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{
"key": "ci_tests",
"name": "CI-Tests",
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"points": 2.5,
"status": "met",
"details": [],
"max_points": 2.5
},
{
"key": "cii_best_practices",
"name": "CII-Best-Practices",
"detail": "no effort to earn an OpenSSF best practices badge detected",
"points": 0,
"status": "missed",
"details": [],
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},
{
"key": "code_review",
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"points": 6.8,
"status": "partial",
"details": [],
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{
"key": "contributors",
"name": "Contributors",
"detail": "project has 70 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": 2.2,
"status": "partial",
"details": [],
"max_points": 2.5
},
{
"key": "maintained",
"name": "Maintained",
"detail": "30 commit(s) and 18 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 detected",
"points": 5,
"status": "met",
"details": [],
"max_points": 5
},
{
"key": "pinned_dependencies",
"name": "Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 8",
"points": 4,
"status": "partial",
"details": [],
"max_points": 5
},
{
"key": "sast",
"name": "SAST",
"detail": "SAST tool is run on all commits",
"points": 5,
"status": "met",
"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": "GitHub workflow tokens follow principle of least privilege",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
},
{
"key": "vulnerabilities",
"name": "Vulnerabilities",
"detail": "6 existing vulnerabilities detected",
"points": 3,
"status": "partial",
"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:pymc@6.1.0 runtime dependency closure — what installing the published package pulls in — 32 packages. Reachability is not analyzed.",
"notes": [
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"code": "excluded_no_data",
"params": {
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},
{
"code": "weights_renormalized",
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{
"code": "advisories_scope_published",
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},
{
"code": "advisories_reachability",
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}
],
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"advisories": 0,
"affected_packages": 0,
"assessed_packages": 32,
"unassessed_packages": 0,
"affected_by_severity": "none",
"direct_affected_packages": 0
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"components": [
{
"key": "direct_dependencies_free_of_known_advisories",
"name": "Direct dependencies free of known advisories",
"detail": "no direct dependency carries a known advisory",
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"status": "met",
"details": [
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"code": "no_direct_advisories",
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}
],
"max_points": 35
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{
"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": [
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"code": "no_indirect_advisories",
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}
],
"max_points": 25
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{
"key": "no_advisories_left_outstanding",
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"points": 0,
"status": "excluded",
"details": [
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"code": "advisories_no_publication_date",
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],
"max_points": 40
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]
},
{
"key": "malicious_dependencies",
"band": "excellent",
"name": "Malicious dependencies",
"note": null,
"notes": [],
"value": 100,
"inputs": {
"source": "osv",
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"red_flag": false,
"assessed_packages": 32,
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"direct_malicious_packages": 0,
"withdrawn_malicious_packages": 0,
"installable_malicious_packages": 0
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"components": [
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"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",
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"status": "met",
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],
"max_points": 100
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]
},
{
"key": "high_risk_jurisdiction_exposure",
"band": "moderate",
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"notes": [
{
"code": "jurisdiction_evidence_limits",
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}
],
"value": 50,
"inputs": {
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"role": "top_contributor",
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],
"policy_countries": [
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"review_only_matches": 0,
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"components": [
{
"key": "policy_exposure_multiplier",
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"code": "jurisdiction_exposure",
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],
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]
}
],
"description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
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{
"key": "ai_readiness",
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"name": "AI Readiness",
"value": 52,
"weight": 0,
"metrics": [
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"band": "critical",
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"note": null,
"notes": [],
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"inputs": {
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"agent_instruction_files": [],
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"components": [
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"key": "agent_instructions",
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],
"max_points": 45
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{
"key": "machine_readable_docs_llms_txt",
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"points": 0,
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{
"key": "legible_commit_history",
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]
},
{
"key": "ai_verify_loop",
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"note": null,
"notes": [],
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"inputs": {
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"lockfiles": [],
"has_dockerfile": true,
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"toolchain_manifests": [],
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"components": [
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"key": "one_command_bootstrap",
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"points": 18,
"status": "met",
"details": [
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"code": "file_list",
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{
"key": "automated_tests",
"name": "Automated tests",
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"points": 22,
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{
"key": "lint_format_config",
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"points": 11,
"status": "met",
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{
"key": "static_type_checking",
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"detail": null,
"points": 0,
"status": "missed",
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{
"key": "reproducible_environment",
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"points": 10,
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"details": [
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"code": "file_list",
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],
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{
"key": "demonstrated_agent_practice",
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"points": 0,
"status": "missed",
"details": [
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],
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{
"key": "automated_maintenance",
"name": "Automated maintenance",
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"status": "met",
"details": [
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"code": "dependency_bot_commits",
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}
],
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},
{
"key": "openssf_scorecard_pinned_dependencies",
"name": "OpenSSF Scorecard: Pinned-Dependencies",
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"status": "partial",
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]
},
{
"key": "ai_code_legibility",
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"key": "type_checkable_code",
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},
{
"key": "manageable_file_sizes",
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"key": "ai_interfaces",
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"inputs": {
"example_dirs": [
"notebooks"
],
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"key": "api_schema_openapi_graphql_proto",
"name": "API schema (OpenAPI/GraphQL/proto)",
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"status": "missed",
"details": [],
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{
"key": "mcp_server",
"name": "MCP server",
"detail": null,
"points": 0,
"status": "missed",
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},
{
"key": "runnable_examples",
"name": "Runnable examples",
"detail": "notebooks",
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],
"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."
}
],
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},
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"report_type": "repository",
"generated_at": "2026-07-22T00:12:27.079509Z",
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}Оцінки — це сигнали, а не гарантії. Вони відображають публічно видимі практики на GitHub — це не аудит коду й не гарантія безпеки.
Відсутні дані виключаються, а ваги перенормовуються — нуль за відсутність ніколи не ставиться. Методологія версіонована й відкрита: метрики v1.13.0, схема v0.26.0 — повна методологія · вікі метрик.
Як окремий результат виглядає на тлі всього реєстру: сукупна статистика — PyPI.