rmax / databrewer

Wann jeder Stern und Fork hinzugefügt wurde, von GitHub erfasst und nach Tagen gruppiert. Das kumulierte Wachstum steht direkt über den täglichen Zugängen, aus denen es besteht, sodass beide gegeneinander lesbar sind: stetiger organischer Zuwachs sieht ganz anders aus als ein abrupter, kurzlebiger Ausschlag. Wo dieser Unterschied messbar ist, wird er als Wachstumsauthentizität ausgewiesen.

Jeder Punkt umfasst 3 Tage.

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 20 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

Die Installation von pypi:databrewer@0.1.1 zieht 17 Pakete nach sich, direkt und transitiv: 2 tragen bekannte Advisories, davon 2 direkte Abhängigkeiten.

Ein Advisory bedeutet, dass die im Abhängigkeitsgraphen erfasste Version in den betroffenen Bereich eines Advisories fällt. Erreichbarkeit wird nicht analysiert, und der Graph enthält Entwicklungs- und Test-Pins — ein Fund kann das Werkzeug betreffen und nicht die ausgelieferte Software.

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                ],
                "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": "1 advisory-carrying package(s) unaddressed past 90 days; oldest published 2951 days ago",
                "points": 32.1,
                "status": "partial",
                "details": [
                  {
                    "code": "advisories_stale",
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                  }
                ],
                "max_points": 40
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          },
          {
            "key": "malicious_dependencies",
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            "note": null,
            "notes": [],
            "value": 100,
            "inputs": {
              "source": "osv",
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              "packages": [],
              "red_flag": false,
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              "malicious_packages": 0,
              "direct_malicious_packages": 0,
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            "components": [
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                "details": [
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                    "code": "no_malicious_dependencies",
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                ],
                "max_points": 100
              }
            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
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            "name": "High-Risk Jurisdiction Exposure",
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            "notes": [
              {
                "code": "jurisdiction_evidence_limits",
                "params": {}
              }
            ],
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            "inputs": {
              "meaning": "self-published location evidence; not nationality or citizenship",
              "red_flag": false,
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              "policy_countries": [
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                "Iran",
                "North Korea"
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              "review_only_matches": 0,
              "assessed_self_published_locations": 2
            },
            "components": [
              {
                "key": "policy_exposure_multiplier",
                "name": "Policy exposure multiplier",
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                "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": "at_risk",
        "name": "AI Readiness",
        "value": 38,
        "weight": 0,
        "metrics": [
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            "note": null,
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              "agent_instruction_files": [],
              "agent_instruction_max_bytes": null
            },
            "components": [
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                "key": "agent_instructions",
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                "detail": "no CLAUDE.md / AGENTS.md / editor rules",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_agent_instructions",
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                  }
                ],
                "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",
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                "points": 4.3,
                "status": "partial",
                "details": [
                  {
                    "code": "legible_history",
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                      "sampled": 25
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                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
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            "name": "Verify loop (build / test / typecheck)",
            "note": null,
            "notes": [],
            "value": 57,
            "inputs": {
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              "has_tests": true,
              "lockfiles": [],
              "has_dockerfile": false,
              "typed_language": false,
              "bootstrap_files": [
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                "docs/Makefile"
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              "has_devcontainer": false,
              "has_linter_config": true,
              "typecheck_configs": [],
              "agent_commit_share": 0,
              "toolchain_manifests": [],
              "dependency_bot_commit_share": 0
            },
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                "points": 18,
                "status": "met",
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                "max_points": 18
              },
              {
                "key": "automated_tests",
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                "points": 22,
                "status": "met",
                "details": [],
                "max_points": 22
              },
              {
                "key": "lint_format_config",
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                "max_points": 11
              },
              {
                "key": "static_type_checking",
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                "points": 0,
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                "details": [],
                "max_points": 11
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                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 10
              },
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                "key": "demonstrated_agent_practice",
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                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_agent_authored_commits",
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              },
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                "key": "automated_maintenance",
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                    "code": "no_dependency_automation",
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                ],
                "max_points": 8
              },
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                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
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                "status": "excluded",
                "details": [
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                    "code": "no_data",
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                  }
                ],
                "max_points": 10
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            "key": "ai_code_legibility",
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            "name": "Code legibility for models",
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            "notes": [],
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              "source_files_sampled": 10,
              "oversized_source_files": 0
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                "points": 0,
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                "details": [
                  {
                    "code": "no_typecheck_config_language",
                    "params": {
                      "language": "Python"
                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
                "detail": "0/10 source files over 60KB",
                "points": 55,
                "status": "met",
                "details": [
                  {
                    "code": "oversized_source_files",
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                      "oversized": 0
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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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    ],
    "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"
}