Public record
Software health reportschema 0.27.0 · metrics 1.13.0 · 2026-07-27 08:33 UTC

rmax / databrewer

The missing datasets manager. Like hombrew but for datasets. CLI-tool for search and discover datasets!

Python · MakefileMIT★ 41 stars⑂ 4 forkssince Jul 2016View on GitHub ↗

rmax/databrewer holds a health index of 34 out of 100, placing it in the At risk band. It scores highest on Engineering Quality (75/100) and lowest on Security (14/100). It was last updated 3346 days ago. A single contributor accounts for most of its recent work.

34
overall / 100
At risk

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At risk ceiling of 49. AI Readiness sits outside the overall score.

34
Excellent85-100Exemplary; meets essentially all checked criteria
Good70-84Healthy; minor gaps
Moderate50-69Acceptable with notable gaps; review recommended
At risk30-49Significant weaknesses; adoption warrants caution
Critical1-29Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

Abandonment Policy applies a 85% multiplier to weighted overall health.

Ownership

R Max EspinozaPersonal account
675 followers97 public repossince Sep 2008Milence

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIdatabrewerpoints to another repo — not scored0.1.1-33369 days agodatabrewer

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

20Critical · 22% of overall
How it's scored
0/36Push recency — last push 3,346 days ago
0/36Commit cadence — 0/52 weeks with commits
0/18Commit volume — 0 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year0
human_commit_share1
days_since_last_push3,346
active_weeks_last_year0
How it's scored
16.2/27Ships releases — 2 version tags (no GitHub releases)
0/36Release recency — latest release 3,369 days ago
27/27Release cadence — a release every ~0 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count2
latest_release_tagv0.1.1
releases_from_tagsyes
days_since_latest_release3,369
mean_days_between_releases0

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

49At risk · 18% of overall
How it's scored
26/60Stars — 41 stars
4/25Forks — 4 forks
0/15Watchers — 2 watchers
Inputs used
forks4
stars41
watchers2
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
has_contributingyes
has_issue_templateno
has_code_of_conductno
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

39At risk · 24% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.9/22.5Commit distribution — top contributor authored 96% of commits
2.7/13.5Contributor breadth — 2 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.958
How it's scored
13.4/46.8Issue resolution — 29% of issues closed
19.1/38.3PR acceptance — 1/2 decided PRs merged
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs1
open_issues5
closed_issues2
issue_closed_ratio0.286
closed_unmerged_prs1
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
20.3/25Owner reach — 675 followers of rmax
25/25Track record — 97 public repos, account ~17 yr old
Inputs used
followers675
owner_typeUser
is_verified
owner_loginrmax
public_repos97
account_age_days6,515
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

75Good · 20% of overall
How it's scored
0/24CI workflows
24/24Tests present
16/16Linter config — tox.ini
0/9.6Pre-commit hooks
6.4/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_cino
has_testsyes
has_editorconfigyes
has_linter_configyes
has_precommit_configno

Documentation

100Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://databrewer.readthedocs.org/
10/10Repository description
10/10Topics — 5 topics
10/10Wiki
Inputs used
topicsdatasets, data, discovery, command-line, python
has_wikiyes
homepagehttps://databrewer.readthedocs.org/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

14Critical · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestsdocs/requirements.txt, requirements-dev.txt, requirements-install.txt, requirements-setup.txt, requirements-tests.txt, setup.cfg, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
How it's scored
6.4/35Direct dependencies free of known advisories — 2 affected: pyyaml 3.13.0 (critical 9.8), click 6.7.0 (high 7.2)
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
32.1/40No advisories left outstanding — 1 advisory-carrying package(s) unaddressed past 90 days; oldest published 2,951 days ago
Inputs used
sourceosv
advisories5
affected_packages2
assessed_packages17
unassessed_packages0
affected_by_severitycritical 1, high 1
direct_affected_packages2
Matched the pypi:databrewer@0.1.1 runtime dependency closure — what installing the published package pulls in — 17 packages. Reachability is not analyzed.

AI Readiness

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.

38At risk · 0% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
4.3/40Legible commit history — 2 of 25 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
legible_history_share0.08
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrap — Makefile, docs/Makefile
22/22Automated tests
11/11Lint / format config — tox.ini
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practice — no agent-authored commits among the last 25
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, docs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/10 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes9,493
source_files_sampled10
oversized_source_files0

Key facts

41GitHub stars
2contributors
0commits, last 12 months
3,346days since last push
2releases
1bus factor
5open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'databrewer' points at a different repository (https://github.com/rolando/databrewer); excluded from ecosystem scoring
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Star and fork history 0 ★ / 4 ⇿
0Stars
4Forks
2Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

1223344412016-072017-092018-10
Major 0Minor 1Patch 1

Each point covers 3 days.

All dependencies 20

Full resolved dependency set from the GitHub dependency graph: 0 direct and 20 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIawesome-slugifyindirect
PyPIbumpversionindirect
PyPIcheck-manifestindirect
PyPIclickindirect
PyPIcoverageindirect
PyPIflake8indirect
PyPIjsonschemaindirect
PyPIpip-toolsindirect
PyPIpytestindirect
PyPIpyyamlindirect
PyPIrequestsindirect
PyPIsixindirect
PyPIsphinxindirect
PyPItoolzindirect
PyPItoxindirect
PyPItqdmindirect
PyPItwineindirect
PyPIwatchdogindirect
PyPIwheelindirect
PyPIwhooshindirect
Dependency advisories 2

Installing pypi:databrewer@0.1.1 pulls in 17 packages, direct and transitive: 2 carry known advisories, of which 2 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
pyyaml3.13.0directcritical45.4
click6.7.0directhigh18.3.3

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable
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    "ecosystem": {
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                ],
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              {
                "key": "owner_reach",
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                "detail": "675 followers of rmax",
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              },
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              },
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              },
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              }
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          },
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            "key": "dependency_advisories",
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            "notes": [
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                "code": "advisories_scope_published",
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                "max_points": 40
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          },
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            "key": "malicious_dependencies",
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            "notes": [],
            "value": 100,
            "inputs": {
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              "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",
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              "red_flag": false,
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              }
            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
            "band": "excellent",
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            "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",
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              "review_only_matches": 0,
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            },
            "components": [
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                "key": "policy_exposure_multiplier",
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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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            },
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              {
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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."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "warnings": [
    "Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token",
    "pypi package 'databrewer' points at a different repository (https://github.com/rolando/databrewer); excluded from ecosystem scoring",
    "OpenSSF Scorecard timed out after 240s; skipping Scorecard checks"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-27T08:33:21.043043Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/r/rmax/databrewer.svg",
  "full_name": "rmax/databrewer",
  "license_state": "standard",
  "license_spdx": "MIT"
}

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v1.13.0, schema v0.27.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.