Public record
Software health reportschema 0.27.0 · metrics 1.13.0 · 2026-07-25 08:21 UTC

Akegarasu / lora-scripts

SD-Trainer. LoRA & Dreambooth training scripts & GUI use kohya-ss's trainer, for diffusion model.

PythonAGPL-3.0★ 6,087 stars⑂ 696 forkssince Jan 2023View on GitHub ↗

Akegarasu/lora-scripts holds a health index of 36 out of 100, placing it in the At risk band. It scores highest on Community & Adoption (72/100) and lowest on Security (21/100). It was last updated 319 days ago. A single contributor accounts for most of its recent work.

36
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.

36
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

秋葉杏Personal account
3,598 followers62 public repossince Feb 2018

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 publish
PyPIlibrarypoints to another repo — not scored3.1.1-7011 days ago

Metrics by category

Vitality

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

34At risk · 22% of overall
How it's scored
3.6/36Push recency — last push 319 days ago
2.8/36Commit cadence — 4/52 weeks with commits
8.6/18Commit volume — 8 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year8
human_commit_share1
days_since_last_push319
active_weeks_last_year4
How it's scored
27/27Ships releases — 12 releases published
7.2/36Release recency — latest release 658 days ago
19.8/27Release cadence — a release every ~54.7 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count12
latest_release_tagv1.10.0
releases_from_tagsno
days_since_latest_release658
mean_days_between_releases54.7

Community & Adoption

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

72Good · 18% of overall
How it's scored
60/60Stars — 6,087 stars
23.7/25Forks — 696 forks
8.6/15Watchers — 36 watchers
Inputs used
forks696
stars6,087
watchers36
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (AGPL-3.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
has_contributingno
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?

56Moderate · 24% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
2.1/22.5Commit distribution — top contributor authored 90% of commits
13.5/13.5Contributor breadth — 30 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled30
top_contributor_share0.905
How it's scored
37.5/46.8Issue resolution — 80% of issues closed
23.2/38.3PR acceptance — 46/76 decided PRs merged
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs46
open_issues132
closed_issues533
issue_closed_ratio0.802
closed_unmerged_prs30
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
25/25Owner reach — 3,598 followers of Akegarasu
25/25Track record — 62 public repos, account ~8 yr old
Inputs used
followers3,598
owner_typeUser
is_verified
owner_loginAkegarasu
public_repos62
account_age_days3,079
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

25Critical · 20% of overall
How it's scored
0/24CI workflows
0/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_cino
has_testsno
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

60Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics — 4 topics
10/10Wiki
Inputs used
topicsdreambooth, finetune, lora, stable-diffusion
has_wikiyes
homepage
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

21Critical · 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
manifestsrequirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages64
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:library@3.1.1 runtime dependency closure — what installing the published package pulls in — 64 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.

32At risk · 0% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
16/40Legible commit history — 30 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
legible_history_share0.3
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
0/22Automated tests
0/11Lint / format config
11/11Static type checking — mikazuki/tsconfig.json
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsno
lockfiles
has_dockerfileyes
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configno
typecheck_configsmikazuki/tsconfig.json
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (mikazuki/tsconfig.json)
50.9/55Manageable file sizes — 17/229 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes288,821
source_files_sampled229
oversized_source_files17
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — notebooks
Inputs used
example_dirsnotebooks
has_mcp_signalno
api_schema_files

Key facts

6,087GitHub stars
30contributors
8commits, last 12 months
319days since last push
12releases
1bus factor
132open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'library' points at a different repository (https://github.com/chapmanjacobd/library/); excluded from ecosystem scoring
  • OpenSSF Scorecard did not return a usable result (2026/07/25 08:21:14 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 696 ⇿
0Stars
696Forks
12Releases

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.

0125250375500625750677172023-012024-102026-07
Major 0Minor 9Patch 3

Each point covers 4 days.

All dependencies 44

Full resolved dependency set from the GitHub dependency graph: 0 direct and 44 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaccelerate0.30.0indirect
PyPIaccelerate0.33.0indirect
PyPIaltair4.2.2indirect
PyPIbitsandbytes0.44.0indirect
PyPIbitsandbytes0.46.0indirect
PyPIdadaptation3.1indirect
PyPIdiffusers0.25.0indirect
PyPIeasygui0.98.3indirect
PyPIeinops0.7.0indirect
PyPIfastapi0.95.1indirect
PyPIftfy6.1.1indirect
PyPIgradio3.44.2indirect
PyPIhttpx0.24.1indirect
PyPIhuggingface-hub0.24.5indirect
PyPIimagesize1.4.1indirect
PyPIlion-pytorch0.0.6indirect
PyPIlion-pytorch0.1.2indirect
PyPIlycoris-lora3.2.0.post2indirect
PyPInumpyindirect
PyPInumpy1.26.4indirect
PyPIopen-clip-torch2.20.0indirect
PyPIopencv-python4.8.1.78indirect
PyPIpandasindirect
PyPIpillowindirect
PyPIprodigy-plus-schedule-freeindirect
PyPIprodigy-plus-schedule-free1.9.0indirect
PyPIprodigyopt1.0indirect
PyPIprodigyopt1.1.2indirect
PyPIpytorch-lightning1.9.0indirect
PyPIpytorch-optimizer3.7.0indirect
PyPIrequestsindirect
PyPIrich13.7.0indirect
PyPIsafetensors0.4.2indirect
PyPIsafetensors0.4.4indirect
PyPIschedulefree1.4indirect
PyPIscipyindirect
PyPIsentencepiece0.2.0indirect
PyPItensorboardindirect
PyPItensorboard2.10.1indirect
PyPItoml0.10.2indirect
PyPItransformers4.44.0indirect
PyPIuvicorn0.22.0indirect
PyPIvoluptuous0.13.1indirect
PyPIwandb0.16.2indirect
Dependency advisories 0

Installing pypi:library@3.1.1 pulls in 64 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

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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        ],
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      {
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          },
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            "inputs": {
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            ]
          },
          {
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          }
        ],
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                "key": "machine_readable_docs_llms_txt",
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              {
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                "detail": "30 of 100 human commits state their intent (structured subject or explanatory body)",
                "points": 16,
                "status": "partial",
                "details": [
                  {
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                "max_points": 40
              }
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              "dependency_bot_commit_share": 0
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            "components": [
              {
                "key": "one_command_bootstrap",
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                "points": 0,
                "status": "missed",
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                "max_points": 10
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                "details": [
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                "points": 27,
                "status": "partial",
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                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
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                "detail": "17/229 source files over 60KB",
                "points": 50.9,
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                "max_points": 55
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            ]
          },
          {
            "key": "ai_interfaces",
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            "value": 40,
            "inputs": {
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              "has_mcp_signal": false,
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            },
            "components": [
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              },
              {
                "key": "mcp_server",
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                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
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              },
              {
                "key": "runnable_examples",
                "name": "Runnable examples",
                "detail": "notebooks",
                "points": 40,
                "status": "met",
                "details": [
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                    "code": "file_list",
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                ],
                "max_points": 40
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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 'library' points at a different repository (https://github.com/chapmanjacobd/library/); excluded from ecosystem scoring",
    "OpenSSF Scorecard did not return a usable result (2026/07/25 08:21:14 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-25T08:21:53.286852Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/a/Akegarasu/lora-scripts.svg",
  "full_name": "Akegarasu/lora-scripts",
  "license_state": "standard",
  "license_spdx": "AGPL-3.0"
}

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.