runapi-ai/midjourney-sdk holds a health index of 53 out of 100, placing it in the Moderate band. It scores highest on Vitality (66/100) and lowest on Sustainability & Governance (36/100). It was last updated 8 days ago. A single contributor accounts for most of its recent work.
53
overall / 100
Moderate
Software health index
Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.
53
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
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.
The weighted overall 52 is calibrated to 53 on the published index scale (record calibration 2026-08-02).
Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25
Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40
No advisories left outstanding — no advisory carries a publication date
Inputs used
source
osv
advisories
0
affected_packages
0
assessed_packages
1
unassessed_packages
0
affected_by_severity
none
direct_affected_packages
0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the npm:@runapi.ai/midjourney@0.3.2 runtime dependency closure — what installing the published package pulls in — 1 packages. Reachability is not analyzed.
How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.
Manageable file sizes — 1/161 source files over 60KB
Inputs used
primary_language
Java
largest_source_bytes
225,256
source_files_sampled
161
oversized_source_files
1
Key facts
0GitHub stars
1contributors
13commits, last 12 months
8days since last push
25releases
1bus factor
0open issues
Go, Maven, npm, PyPI, RubyGemspackage ecosystems
Data collection warnings
GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
More detail
OpenSSF Scorecard 5.0 / 10
5.0aggregate
Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-09-12 13:36 UTC
The resolved dependency set could not be collected for this report: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
Dependency advisories 0
Installing npm:@runapi.ai/midjourney@0.3.2 pulls in 1 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
Feedback
Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.
Related records
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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 v2.10.0, schema v0.34.0 — full methodology · metrics wiki.
