pydata / bottleneck

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 2 direkte und 37 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

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                "key": "openssf_scorecard_contributors",
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                "key": "pr_acceptance",
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        ],
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                "key": "code_review",
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                "key": "contributors",
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              },
              {
                "key": "dependency_update_tool",
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                "key": "fuzzing",
                "name": "Fuzzing",
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              },
              {
                "key": "maintained",
                "name": "Maintained",
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              {
                "key": "packaging",
                "name": "Packaging",
                "detail": "packaging workflow detected",
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              },
              {
                "key": "pinned_dependencies",
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                "detail": "dependency not pinned by hash detected -- score normalized to 2",
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              },
              {
                "key": "sast",
                "name": "SAST",
                "detail": "SAST tool is not run on all commits -- score normalized to 0",
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              },
              {
                "key": "security_policy",
                "name": "Security-Policy",
                "detail": "security policy file not detected",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "signed_releases",
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                "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": "0 existing vulnerabilities detected",
                "points": 7.5,
                "status": "met",
                "details": [],
                "max_points": 7.5
              }
            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
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            "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": {}
              }
            ],
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            "inputs": {
              "meaning": "self-published location evidence; not nationality or citizenship",
              "red_flag": false,
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              "policy_countries": [
                "Russia",
                "Iran",
                "North Korea"
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              "review_only_matches": 0,
              "assessed_self_published_locations": 7
            },
            "components": [
              {
                "key": "policy_exposure_multiplier",
                "name": "Policy exposure multiplier",
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                "status": "met",
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                  {
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                  }
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                "max_points": 100
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        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
      },
      {
        "key": "ai_readiness",
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        "metrics": [
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                }
              },
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              "agent_instruction_max_bytes": null
            },
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                "status": "missed",
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                  }
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                "max_points": 45
              },
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                "key": "machine_readable_docs_llms_txt",
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                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 15
              },
              {
                "key": "legible_commit_history",
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                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
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            "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": {}
              }
            ],
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            "inputs": {
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              "has_tests": true,
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              "has_linter_config": true,
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              "toolchain_manifests": [],
              "dependency_bot_commit_share": 0
            },
            "components": [
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              },
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                "max_points": 22
              },
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                "details": [],
                "max_points": 11
              },
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                "points": 0,
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                "details": [],
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              },
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                  }
                ],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
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                "points": 0,
                "status": "excluded",
                "details": [
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                    "code": "no_data",
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                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "dependency automation configured, none observed in the sampled commits",
                "points": 5,
                "status": "partial",
                "details": [
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                ],
                "max_points": 8
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                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 2",
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                "status": "partial",
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                "max_points": 10
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          },
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            "key": "ai_code_legibility",
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            "name": "Code legibility for models",
            "note": null,
            "notes": [],
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            "inputs": {
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              "largest_source_bytes": 81180,
              "source_files_sampled": 54,
              "oversized_source_files": 1
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                "points": 0,
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                      "language": "Python"
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                ],
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              },
              {
                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
                "detail": "1/54 source files over 60KB",
                "points": 54,
                "status": "partial",
                "details": [
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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": [],
  "report_type": "repository",
  "generated_at": "2026-07-18T14:12:25.662697Z",
  "schema_version": "0.12.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/p/pydata/bottleneck.svg",
  "full_name": "pydata/bottleneck",
  "license_state": "standard",
  "license_spdx": "BSD-2-Clause"
}