mooreneural / ProteinTensor
AI-native tensor format (.ptt) for structural biology ML. Convert a protein/nucleic-acid/ligand structure or sequence once; load backbone, MSA, ESM embeddings, sparse pair features, and ligand pockets with zero parsing. Feeds Boltz-2 (verified), AlphaFold 3, Chai-1, Nesso. Zarr-backed, PyTorch/JAX, streams from S3/GCS.
mooreneural/ProteinTensor holds a health index of 53 out of 100, placing it in the Moderate band. It scores highest on Engineering Quality (76/100) and lowest on AI Readiness (31/100). It was last updated 90 days ago. A single contributor accounts for most of its recent work.
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.
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).
Ownership
This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.
Package ecosystems
| Registry | Package | Version | Downloads / mo | Versions | Last publish | Tags |
|---|---|---|---|---|---|---|
| PyPI | proteintensor | 0.4.0 | 27 | 4 | 105 days ago | protein-structurestructural-biologymachine-learningtensorzarralphafoldboltz |
Metrics by category
Vitality
Is the project alive — is code being written and are releases shipping?
Development activity
How it's scored
| 18/36 | Push recency — last push 90 days ago |
| 3.5/36 | Commit cadence — 5/52 weeks with commits |
| 16/18 | Commit volume — 59 commits in the last year |
| 0/10 | OpenSSF Scorecard: Maintained — no data |
Inputs used
| commits_last_year | 59 |
| human_commit_share | 1 |
| days_since_last_push | 90 |
| active_weeks_last_year | 5 |
Release discipline
How it's scored
| 27/27 | Ships releases — 4 releases published |
| 27/36 | Release recency — latest release 105 days ago |
| 27/27 | Release cadence — a release every ~11.6 days |
| 0/10 | OpenSSF Scorecard: Signed-Releases — no data |
Inputs used
| releases_count | 4 |
| latest_release_tag | v0.4.0 |
| releases_from_tags | no |
| days_since_latest_release | 105 |
| mean_days_between_releases | 11.6 |
Community & Adoption
Does the project have users, downloads, attention, and a welcoming setup for contributors?
Popularity & adoption
How it's scored
| 0/60 | Stars — 1 stars |
| 0/25 | Forks — 0 forks |
| 0/15 | Watchers — 0 watchers |
Inputs used
| forks | 0 |
| stars | 1 |
| watchers | 0 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community health
How it's scored
| 22.5/22.5 | README |
| 22.5/22.5 | License — recognized license (MIT) |
| 18/18 | CONTRIBUTING guide |
| 13.5/13.5 | Code of conduct |
| 0/7.2 | Issue template |
| 6.3/6.3 | PR template |
Inputs used
| has_readme | yes |
| has_license | yes |
| readme_badges | 0 |
| has_contributing | yes |
| has_issue_template | no |
| has_code_of_conduct | yes |
| readme_badge_services | — |
| has_pull_request_template | yes |
Ecosystem adoption (downloads)
How it's scored
| 19.3/80 | Monthly downloads — 27 downloads/month across pypi |
| 0/20 | Registry dependents — not reported by this ecosystem |
Inputs used
| packages | proteintensor |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 27 |
| unverified_packages_excluded | — |
Sustainability & Governance
Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?
Maintainer resilience (bus factor)
How it's scored
| 9/54 | Bus factor — 1 contributor(s) cover half of all commits |
| 0/22.5 | Commit distribution — top contributor authored 100% of commits |
| 1.4/13.5 | Contributor breadth — 1 contributors |
| 0/10 | OpenSSF Scorecard: Contributors — no data |
Inputs used
| bus_factor | 1 |
| contributors_sampled | 1 |
| top_contributor_share | 1 |
Ownership & stewardship
How it's scored
| 10/30 | Ownership backing — personal (user) account |
| 0/20 | Verified domain — not applicable to user accounts |
| 10.1/25 | Owner reach — 24 followers of mooreneural |
| 15.1/25 | Track record — 22 public repos, account ~2 yr old |
Inputs used
| followers | 24 |
| owner_type | User |
| is_verified | — |
| owner_login | mooreneural |
| public_repos | 22 |
| account_age_days | 941 |
Package maintenance
How it's scored
| 25/25 | Published & resolvable — 1 package(s) on pypi |
| 35/35 | Publish recency — latest publish 105 days ago |
| 12/20 | Version history — 4 published versions |
| 20/20 | Not deprecated — active, not deprecated or yanked |
Inputs used
| packages | proteintensor |
| ecosystems | pypi |
| any_deprecated | no |
| min_days_since_publish | 105 |
Engineering Quality
Are baseline engineering and documentation practices in place?
Engineering practices
How it's scored
| 24/24 | CI workflows — 1 workflow(s) |
| 24/24 | Tests present |
| 0/16 | Linter config |
| 0/9.6 | Pre-commit hooks |
| 0/6.4 | .editorconfig |
| 0/20 | OpenSSF Scorecard: CI-Tests — no data |
Inputs used
| has_ci | yes |
| has_tests | yes |
| has_editorconfig | no |
| has_linter_config | no |
| has_precommit_config | no |
Documentation
How it's scored
| 30/30 | README |
| 25/25 | Documentation directory |
| 15/15 | Documentation / homepage site — https://zenodo.org/records/21266859 |
| 10/10 | Repository description |
| 10/10 | Topics — 3 topics |
| 10/10 | Wiki |
Inputs used
| topics | computational-biology, protein-language-model, tensor-format |
| has_wiki | yes |
| homepage | https://zenodo.org/records/21266859 |
| docs_site | https://zenodo.org/records/21266859 |
| has_readme | yes |
| has_docs_dir | yes |
| has_description | yes |
Security
Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?
Security posture
How it's scored
| 30/30 | Security policy (SECURITY.md) |
| 0/25 | Dependabot config |
| 0/25 | Dependency lockfiles |
| 0/20 | CodeQL workflow |
Inputs used
| source | file_signals |
| lockfiles | — |
| manifests | pyproject.toml |
| has_codeql_workflow | no |
| has_security_policy | yes |
| has_dependabot_config | no |
Dependency advisories
How it's scored
| 35/35 | 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 | 13 |
| unassessed_packages | 0 |
| affected_by_severity | none |
| direct_affected_packages | 0 |
AI Readiness
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.
Agent context & guidance
How it's scored
| 0/45 | Agent instructions — no CLAUDE.md / AGENTS.md / editor rules |
| 0/15 | Machine-readable docs (llms.txt) |
| 28.9/40 | Legible commit history — 32 of 59 human commits state their intent (structured subject or explanatory body) |
Inputs used
| has_llms_txt | no |
| llms_txt_url | — |
| legible_history_share | 0.542 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
How it's scored
| 0/18 | One-command bootstrap |
| 22/22 | Automated tests |
| 0/11 | Lint / format config |
| 0/11 | Static type checking |
| 0/10 | Reproducible environment |
| 0/10 | Demonstrated agent practice — no agent-authored commits among the last 59 |
| 0/8 | Automated maintenance — no automated dependency updates observed |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — no data |
Inputs used
| has_nix | no |
| has_tests | yes |
| lockfiles | — |
| has_dockerfile | no |
| typed_language | no |
| bootstrap_files | — |
| has_devcontainer | no |
| has_linter_config | no |
| typecheck_configs | — |
| agent_commit_share | 0 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0 |
Code legibility for models
How it's scored
| 0/45 | Type-checkable code — Python without a type-check config |
| 55/55 | Manageable file sizes — 0/47 source files over 60KB |
Inputs used
| primary_language | Python |
| largest_source_bytes | 21,315 |
| source_files_sampled | 47 |
| oversized_source_files | 0 |
Key facts
Data collection warnings
- Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
- GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
- OpenSSF Scorecard did not return a usable result (exit code 0; Skipping github.com/mooreneural/ProteinTensor: run: internal error: ListCommits:error during graphqlHandler.setup: internal error: githubv4.Query: Something went wrong while executing your query on 2026-10-22T16:46:39Z. Please include `B0C2:3C3A8C:91439F1:D2F8C4A:6ADA3DEC` when reporting this issue.); skipping Scorecard checks
More detail
Direct dependencies 7
| Registry | Package | Version constraint | Manifest |
|---|---|---|---|
| PyPI | zarr | >=2.18,<3 | pyproject.toml |
| PyPI | numcodecs | >=0.12 | pyproject.toml |
| PyPI | numpy | >=1.24 | pyproject.toml |
| PyPI | gemmi | >=0.6 | pyproject.toml |
| PyPI | click | >=8.1 | pyproject.toml |
| PyPI | rich | >=13 | pyproject.toml |
| PyPI | pyyaml | >=6 | pyproject.toml |
All dependencies not collected
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 pypi:proteintensor@0.4.0 pulls in 13 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.
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.
How one result sits in the wider record: aggregate statistics — PyPI.