Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-09 05:30 UTC

GiacomoSaccaggi / scomp_link

End-to-end ML toolkit automating the complete workflow — data profiling, preprocessing, feature engineering, model selection, training, validation, explainability, drift monitoring, fairness checks, and interactive HTML reporting. Includes a full CLI for zero-code ML pipelines. Python 3.10+.

PythonMIT★ 12 stars⑂ 1 forksince Feb 2026View on GitHub ↗
KindCommand-line toolLibraryMCP serverhow this is determined
#anomaly-detection#automl#cli#data-science#explainability#machine-learning#nlp#python#scikit-learn#time-series#agent-skills#agentic-workflow#mcp#mcp-server#ai-agent-tool#ai-tools#cli-app#cli-tool#claude-tool#kiro-tool

GiacomoSaccaggi/scomp_link holds a health index of 86 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (95/100) and lowest on Sustainability & Governance (47/100). It was last updated 4 days ago. A single contributor accounts for most of its recent work.

86
overall / 100
Excellent

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.

86
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)
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.

The weighted overall 72 is calibrated to 86 on the published index scale (record calibration 2026-08-02).

Ownership

Giacomo SaccaggiPersonal account
48 followers29 public repossince Oct 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 publishTags
PyPIscomp-link2.2.12,3132812 days agomachine-learningdata-scienceautomationpipelineautomlscikit-learnregressionclassificationclusteringdeep-learningnlptext-classificationcontrastive-learninganomaly-detectiontime-seriesensemble-learninghyperparameter-tuningmodel-selectioncross-validationfeature-selectionberttransformerstensorflowpytorchcomputer-visionimage-classificationhtml-reportplotlyedapreprocessingmcpmodel-context-protocolagent-toolai-agentdata-driftmodel-serving

Metrics by category

Vitality

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

83Excellent · 21% of overall

Development activity

71Good
How it's scored
36/36Push recency — last push 4 days ago
9.7/36Commit cadence — 14/52 weeks with commits
18/18Commit volume — 153 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year153
human_commit_share0.92
days_since_last_push4
active_weeks_last_year14

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 22 releases published
36/36Release recency — latest release 12 days ago
27/27Release cadence — a release every ~5.4 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count22
latest_release_tagv2.2.1
releases_from_tagsno
days_since_latest_release12
mean_days_between_releases5.4

Community & Adoption

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

54Moderate · 17% of overall

Popularity & adoption

20At Risk
How it's scored
16.9/60Stars — 12 stars
0/25Forks — 1 forks
3.3/15Watchers — 5 watchers
Inputs used
forks1
stars12
watchers5
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges19
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicescodecov.io, github.com, shields.io
has_pull_request_templateyes

Ecosystem adoption (downloads)

56Moderate
How it's scored
44.9/80Monthly downloads — 2,313 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesscomp-link
dependents—
ecosystemspypi
total_downloads—
monthly_downloads2,313
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

47Weak · 23% of overall

Maintainer resilience (bus factor)

13Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.2/22.5Commit distribution — top contributor authored 99% of commits
2.7/13.5Contributor breadth — 2 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.993

Issue & PR responsiveness

38Weak
How it's scored
21/42Issue resolution — 50% of issues closed
6.7/30PR acceptance — 11/49 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs11
open_issues1
closed_issues1
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d1
issue_closed_ratio0.5
closed_unmerged_prs38
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

56Moderate
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
12.2/25Owner reach — 48 followers of GiacomoSaccaggi
22.8/25Track record — 29 public repos, account ~7 yr old
Inputs used
followers48
owner_typeUser
is_verified—
owner_loginGiacomoSaccaggi
public_repos29
account_age_days2,887
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 12 days ago
20/20Version history — 28 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesscomp-link
ecosystemspypi
any_deprecatedno
min_days_since_publish12

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall

Engineering practices

92Excellent
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff]), tox.ini
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://giacomosaccaggi.github.io/scomp_link/
10/10Repository description
10/10Topics — 20 topics
10/10Wiki
Inputs used
topicsanomaly-detection, automl, cli, data-science, explainability, machine-learning, nlp, python, scikit-learn, time-series, agent-skills, agentic-workflow, mcp, mcp-server, ai-agent-tool, ai-tools, cli-app, cli-tool, claude-tool, kiro-tool
has_wikiyes
homepagehttps://giacomosaccaggi.github.io/scomp_link/
docs_sitehttps://giacomosaccaggi.github.io/scomp_link/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

84Excellent · 16% of overall

Security posture

80Excellent
How it's scored
30/30Security policy (SECURITY.md)
25/25Dependabot config
25/25Dependency lockfiles — uv.lock
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfilesuv.lock
manifestsmcpb/pyproject.toml, pyproject.toml, requirements.txt, requirements/requirements-dev.txt, requirements/requirements-img-py310.txt, requirements/requirements-img-py311.txt, requirements/requirements-img-py312.txt, requirements/requirements-img-py313.txt, requirements/requirements-nlp-py310.txt, requirements/requirements-nlp-py311.txt, requirements/requirements-nlp-py312.txt, requirements/requirements-nlp-py313.txt, requirements/requirements-py310.txt, requirements/requirements-py311.txt, requirements/requirements-py312.txt, requirements/requirements-py313.txt, requirements/requirements-utils.txt, setup.py
has_codeql_workflowno
has_security_policyyes
has_dependabot_configyes

Dependency advisories

100Exceptional
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_packages41
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:scomp-link@2.2.1 runtime dependency closure — what installing the published package pulls in — 41 packages. Reachability is not analyzed.

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.

85Excellent · 4% of overall

Agent context & guidance

100Exceptional
How it's scored
45/45Agent instructions — AGENTS.md
15/15Machine-readable docs (llms.txt) — llms.txt present
40/40Legible commit history — 90 of 92 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_url—
legible_history_share0.978
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes7,175

Verify loop (build / test / typecheck)

69Good
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff]), tox.ini
11/11Static type checking — pyrightconfig.json, scomp_link/py.typed
10/10Reproducible environment — Dockerfile, lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 8 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configspyrightconfig.json, scomp_link/py.typed
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.08

Code legibility for models

80Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (pyrightconfig.json, scomp_link/py.typed)
53.1/55Manageable file sizes — 4/118 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes103,406
source_files_sampled118
oversized_source_files4

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
20/20MCP server
40/40Runnable examples — examples
Inputs used
example_dirsexamples
has_mcp_signalyes
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto). Remaining weights renormalized.

Key facts

12GitHub stars
2contributors
153commits, last 12 months
4days since last push
22releases
1bus factor
1open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'scomp-link' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • Could not fetch pypi package 'scomp-link-mcpb' from its registry
  • pypi download figures for scomp-link carried forward from the previous scan (stats endpoint unavailable this scan)
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/09/09 05:29:06 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Direct dependencies 33
RegistryPackageVersion constraintManifest
PyPIscomp-link>=2.1.0mcpb/pyproject.toml
PyPInumpy>=1.22.0,<2.3.0pyproject.toml
PyPInumpy>=1.24.0,<3.0.0pyproject.toml
PyPInumpy>=1.26.0,<3.0.0pyproject.toml
PyPInumpy>=1.26.0,<3.0.0pyproject.toml
PyPInumpy>=2.0.0,<3.0.0pyproject.toml
PyPIpandas>=1.4.0,<2.4.0pyproject.toml
PyPIpandas>=2.0.0,<3.0.0pyproject.toml
PyPIpandas>=2.1.0,<3.0.0pyproject.toml
PyPIpandas>=2.1.0,<3.0.0pyproject.toml
PyPIpandas>=2.2.0,<3.0.0pyproject.toml
PyPIscipy>=1.8.0,<1.12.0pyproject.toml
PyPIscipy>=1.10.0,<2.0.0pyproject.toml
PyPIscipy>=1.11.0,<2.0.0pyproject.toml
PyPIscipy>=1.11.0,<2.0.0pyproject.toml
PyPIscipy>=1.14.0,<2.0.0pyproject.toml
PyPIscikit-learn>=1.2.0,<1.8.0pyproject.toml
PyPIscikit-learn>=1.3.0,<2.0.0pyproject.toml
PyPIscikit-learn>=1.3.0,<2.0.0pyproject.toml
PyPIscikit-learn>=1.3.0,<2.0.0pyproject.toml
PyPIscikit-learn>=1.5.0,<2.0.0pyproject.toml
PyPImatplotlib>=3.6.0,<3.9.0pyproject.toml
PyPImatplotlib>=3.7.0,<4.0.0pyproject.toml
PyPImatplotlib>=3.8.0,<4.0.0pyproject.toml
PyPImatplotlib>=3.8.0,<4.0.0pyproject.toml
PyPImatplotlib>=3.9.0,<4.0.0pyproject.toml
PyPIplotly>=5.0.0,<6.0.0pyproject.toml
PyPIseaborn>=0.11.0,<0.14.0pyproject.toml
PyPIpolars>=0.20.0,<2.0.0pyproject.toml
PyPIpyarrow>=12.0.0pyproject.toml
PyPIoptuna>=3.0.0,<5.0.0pyproject.toml
PyPItqdm>=4.50.0,<5.0.0pyproject.toml
PyPIstatsmodels>=0.13.0,<1.0.0pyproject.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:scomp-link@2.2.1 pulls in 41 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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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.