Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-21 01:12 UTC

zoharbabin / due-diligence-agents

Legal flags a risk. Finance flags another. We connect and cite. Open-source forensic M&A due diligence: 13 AI agents read your data room across 9 domains (Legal, Finance, Commercial, Tech, Cyber, HR, Tax, Regulatory, ESG), cross-reference findings no single reviewer connects, and trace every one to an exact page & quote.

PythonApache-2.0★ 106 stars⑂ 23 forkssince Feb 2026View on GitHub ↗
KindCommand-line toolPluginLibraryhow this is determined

zoharbabin/due-diligence-agents holds a health index of 80 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (91/100) and lowest on Security (44/100). It was last updated 43 days ago. A single contributor accounts for most of its recent work.

80
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.

80
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 68 is calibrated to 80 on the published index scale (record calibration 2026-08-02).

Ownership

Zohar BabinPersonal account
94 followers118 public repossince Nov 2009Kaltura

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

Metrics by category

Vitality

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

69Good · 21% of overall
How it's scored
18/36Push recencylast push 43 days ago
8.3/36Commit cadence12/52 weeks with commits
18/18Commit volume221 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year221
human_commit_share0.88
days_since_last_push43
active_weeks_last_year12

Release discipline

100Exceptional
How it's scored
27/27Ships releases43 releases published
36/36Release recencylatest release 51 days ago
27/27Release cadencea release every ~6.2 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count43
latest_release_tagv1.18.0
releases_from_tagsno
days_since_latest_release51
mean_days_between_releases6.2

Community & Adoption

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

66Good · 17% of overall
How it's scored
32.8/60Stars106 stars
11.2/25Forks23 forks
0/15Watchers1 watchers
Inputs used
forks23
stars106
watchers1
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
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_badges10
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateyes

Sustainability & Governance

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

64Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
0.2/22.5Commit distributiontop contributor authored 99% of commits
4.1/13.5Contributor breadth3 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor1
contributors_sampled3
top_contributor_share0.989
How it's scored
39.7/42Issue resolution95% of issues closed
28.7/30PR acceptance45/47 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs45
open_issues12
closed_issues211
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.946
closed_unmerged_prs2
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.
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
14.2/25Owner reach94 followers of zoharbabin
25/25Track record118 public repos, account ~16 yr old
Inputs used
followers94
owner_typeUser
is_verified
owner_loginzoharbabin
public_repos118
account_age_days6,160
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 51 days ago
20/20Version history43 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesdd-agents
ecosystemspypi
any_deprecatedno
min_days_since_publish51

Engineering Quality

Are baseline engineering and documentation practices in place?

91Excellent · 19% of overall
How it's scored
24/24CI workflows3 workflow(s)
24/24Tests present
16/16Linter configpyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://zoharbabin.com/due-diligence-agents/
10/10Repository description
10/10Topics20 topics
0/10Wiki
Inputs used
topicsai-agents, claude, contract-analysis, due-diligence, mergers-and-acquisitions, python, document-analysis, legal-tech, anthropic, compliance, risk-analysis, llm, private-equity, multi-agent, cybersecurity, fintech, knowledge-graph, corp-dev, corporate-development, cross-domain-analysis
has_wikino
homepagehttps://zoharbabin.com/due-diligence-agents/
docs_sitehttps://zoharbabin.com/due-diligence-agents/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

44Weak · 16% of overall
How it's scored
30/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestspyproject.toml
has_codeql_workflowno
has_security_policyyes
has_dependabot_configno

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages73
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:dd-agents@1.18.0 runtime dependency closure — what installing the published package pulls in — 73 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
How it's scored
45/45Agent instructionsCLAUDE.md
15/15Machine-readable docs (llms.txt)llms.txt served by the project website (https://zoharbabin.com/llms.txt)
40/40Legible commit history84 of 88 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://zoharbabin.com/llms.txt
legible_history_share0.955
agent_instruction_filesCLAUDE.md
agent_instruction_max_bytes15,349
How it's scored
18/18One-command bootstrapMakefile
22/22Automated tests
11/11Lint / format configpyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environmentdevcontainer, Dockerfile
10/10Demonstrated agent practice72 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile
has_devcontaineryes
has_linter_configyes
typecheck_configs
agent_commit_share0.72
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
52.8/55Manageable file sizes16/402 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes207,451
source_files_sampled402
oversized_source_files16
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
20/20MCP server
40/40Runnable examplesexamples
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

106GitHub stars
3contributors
221commits, last 12 months
43days since last push
43releases
1bus factor
12open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'dd-agents' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Star and fork history 0 ★ / 23 ⇿
0Stars
23Forks
40Releases

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.

48121620242242026-042026-062026-09
Major 0Minor 18Patch 22
Direct dependencies 13
RegistryPackageVersion constraintManifest
PyPIclaude-agent-sdk>=0.1.56pyproject.toml
PyPIpydantic>=2.0pyproject.toml
PyPIopenpyxl>=3.1.3pyproject.toml
PyPInetworkx>=3.0pyproject.toml
PyPInumpy>=1.24pyproject.toml
PyPIrapidfuzz>=3.0pyproject.toml
PyPImarkitdown>=0.1pyproject.toml
PyPIxlrd>=2.0pyproject.toml
PyPIscikit-learn>=1.3pyproject.toml
PyPIclick>=8.0pyproject.toml
PyPIrich>=13.0pyproject.toml
PyPIprompt-toolkit>=3.0pyproject.toml
PyPIpyyaml>=6.0pyproject.toml
All dependencies 33

Full resolved dependency set from the GitHub dependency graph: 13 direct and 20 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIclaude-agent-sdkdirect
PyPIclickdirect
PyPImarkitdowndirect
PyPInetworkxdirect
PyPInumpydirect
PyPIopenpyxldirect
PyPIprompt-toolkitdirect
PyPIpydanticdirect
PyPIpyyamldirect
PyPIrapidfuzzdirect
PyPIrichdirect
PyPIscikit-learndirect
PyPIxlrddirect
PyPIagnoindirect
PyPIanthropicindirect
PyPIbinduindirect
PyPIchromadbindirect
PyPImkdocs-materialindirect
PyPImlx-vlmindirect
PyPImypyindirect
PyPIollamaindirect
PyPIpdf2imageindirect
PyPIpillowindirect
PyPIpre-commitindirect
PyPIpymupdfindirect
PyPIpypdfium2indirect
PyPIpytesseractindirect
PyPIpytestindirect
PyPIpytest-asyncioindirect
PyPIpytest-covindirect
PyPIpytest-timeoutindirect
PyPIpython-dotenvindirect
PyPIruffindirect
Dependency advisories 0

Installing pypi:dd-agents@1.18.0 pulls in 73 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.

The message is kept through sign-in.

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 statisticsPyPI.