Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-16 05:01 UTC

depalmar / ai_for_the_win

Build AI-powered security tools. 50+ hands-on labs covering ML, LLMs, RAG, threat detection, DFIR, and red teaming. Includes Colab notebooks, Docker environment, and CTF challenges.

Python · Jupyter NotebookCustom license★ 160 stars⑂ 25 forkssince Dec 2025View on GitHub ↗
#adversarial-ml#ai#cybersecurity#incident-response#llm#machine-learning#malware-analysis#python#siem#threat-detection#ctf#dfir#docker#threat-intelligence#hands-on-labs#security-training#blue-team#cloud-security#threat-hunting#xdr

depalmar/ai_for_the_win holds a health index of 78 out of 100, placing it in the Good band. It scores highest on Engineering Quality (95/100) and lowest on Sustainability & Governance (50/100). It was last updated 45 days ago. A single contributor accounts for most of its recent work.

78
overall / 100
Good

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.

78
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 67 is calibrated to 78 on the published index scale (record calibration 2026-08-02).

Ownership

Raymond DePalmaPersonal account
25 followers4 public repossince Oct 2019

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

Metrics by category

Vitality

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

61Moderate · 21% of overall

Development activity

49Weak
How it's scored
18/36Push recency — last push 45 days ago
8.3/36Commit cadence — 12/52 weeks with commits
18/18Commit volume — 268 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year268
human_commit_share0.85
days_since_last_push45
active_weeks_last_year12

Release discipline

78Good
How it's scored
27/27Ships releases — 11 releases published
16.2/36Release recency — latest release 248 days ago
27/27Release cadence — a release every ~0.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count11
latest_release_tagv1.9.0
releases_from_tagsno
days_since_latest_release248
mean_days_between_releases0.9

Community & Adoption

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

58Moderate · 17% of overall

Popularity & adoption

47Weak
How it's scored
35.7/60Stars — 160 stars
11.5/25Forks — 25 forks
0/15Watchers — 2 watchers
Inputs used
forks25
stars160
watchers2
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

71Good
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges60
has_contributingyes
has_issue_templateno
has_code_of_conductno
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?

50Moderate · 23% of overall

Maintainer resilience (bus factor)

17Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
3.5/22.5Commit distribution — top contributor authored 84% of commits
2.7/13.5Contributor breadth — 2 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.844

Issue & PR responsiveness

93Exceptional
How it's scored
42/42Issue resolution — 100% of issues closed
24.8/30PR acceptance — 225/272 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_prs225
open_issues0
closed_issues1
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio1
closed_unmerged_prs47
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

47Weak
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
10.2/25Owner reach — 25 followers of depalmar
17.1/25Track record — 4 public repos, account ~6 yr old
Inputs used
followers25
owner_typeUser
is_verified—
owner_logindepalmar
public_repos4
account_age_days2,516
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall

Engineering practices

92Excellent
How it's scored
24/24CI workflows — 9 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff], [tool.black], [tool.isort]), ruff.toml
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://depalmar.github.io/ai_for_the_win/
10/10Repository description
10/10Topics — 20 topics
10/10Wiki
Inputs used
topicsadversarial-ml, ai, cybersecurity, incident-response, llm, machine-learning, malware-analysis, python, siem, threat-detection, ctf, dfir, docker, threat-intelligence, hands-on-labs, security-training, blue-team, cloud-security, threat-hunting, xdr
has_wikiyes
homepagehttps://depalmar.github.io/ai_for_the_win/
docs_sitehttps://depalmar.github.io/ai_for_the_win/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

72Good · 16% of overall

Security posture

75Good
How it's scored
30/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles
20/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsdocker/requirements.txt, pyproject.toml, requirements-core.txt, requirements-llm.txt, requirements.txt
has_codeql_workflowyes
has_security_policyyes
has_dependabot_configyes

Dependency advisories

60Moderate
How it's scored
14.5/35Direct dependencies free of known advisories — 2 affected: python-dotenv 1.0.0 (moderate 6.6), requests 2.32.4 (moderate 5.5)
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
30.7/40No advisories left outstanding — 2 advisory-carrying package(s) unaddressed past 90 days; oldest published 174 days ago
Inputs used
sourceosv
advisories38
affected_packages9
assessed_packages21
unassessed_packages100
affected_by_severitycritical 2, high 3, moderate 4
direct_affected_packages2
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories. Remaining weights renormalized. Matched 21 resolved dependencies against OSV. 100 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. 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.

76Good · 4% of overall

Agent context & guidance

85Excellent
How it's scored
45/45Agent instructions — .cursorrules, CLAUDE.md, docs/CLAUDE.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 85 of 85 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_files.cursorrules, CLAUDE.md, docs/CLAUDE.md
agent_instruction_max_bytes14,724

Verify loop (build / test / typecheck)

68Good
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff], [tool.black], [tool.isort]), ruff.toml
0/11Static type checking
10/10Reproducible environment — Dockerfile
10/10Demonstrated agent practice — 39 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 15 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.39
toolchain_manifests—
dependency_bot_commit_share0.15

Code legibility for models

54Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
54.4/55Manageable file sizes — 2/183 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes137,037
source_files_sampled183
oversized_source_files2

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 — notebooks
Inputs used
example_dirsnotebooks
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

160GitHub stars
2contributors
268commits, last 12 months
45days since last push
11releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Could not fetch pypi package 'ai-for-the-win' from its registry
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 25 ⇿
0Stars
25Forks
6Releases

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.

05101520252552026-012026-042026-08
Major 0Minor 5Patch 1
Direct dependencies 16
RegistryPackageVersion constraintManifest
PyPIpandas>=2.0.0pyproject.toml
PyPInumpy>=1.24.0pyproject.toml
PyPIscikit-learn>=1.3.0pyproject.toml
PyPIlangchain>=1.0.0pyproject.toml
PyPIlangchain-core>=0.3.0pyproject.toml
PyPIlangchain-community>=0.3.0,<1.0.0pyproject.toml
PyPIlanggraph>=0.2.0pyproject.toml
PyPIchromadb>=0.5.0pyproject.toml
PyPIsentence-transformers>=2.2.0pyproject.toml
PyPInltk>=3.8.0pyproject.toml
PyPIpython-dotenv>=1.0.0pyproject.toml
PyPIrequests>=2.31.0pyproject.toml
PyPIrich>=13.0.0pyproject.toml
PyPIpydantic>=2.0.0pyproject.toml
PyPIplotly>=5.18.0pyproject.toml
PyPImatplotlib>=3.7.0pyproject.toml
All dependencies 121

Full resolved dependency set from the GitHub dependency graph: 21 direct and 100 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIchromadb—direct
PyPIlangchain—direct
PyPIlangchain-community—direct
PyPIlangchain-core—direct
PyPIlanggraph—direct
PyPImatplotlib—direct
PyPInltk—direct
PyPInumpy—direct
PyPInumpy1.26.3direct
PyPIpandas—direct
PyPIpandas2.2.0direct
PyPIplotly—direct
PyPIpydantic—direct
PyPIpython-dotenv—direct
PyPIpython-dotenv1.0.0direct
PyPIrequests—direct
PyPIrequests2.32.4direct
PyPIrich—direct
PyPIrich13.7.0direct
PyPIscikit-learn—direct
PyPIsentence-transformers—direct
npm@types/react^18.2.48indirect
npmaxios^1.6.5indirect
npmeslint^8.56.0indirect
npmlodash^4.17.21indirect
npmmoment^2.30.1indirect
npmprettier^3.2.4indirect
npmreact^18.2.0indirect
npmreact-dom^18.2.0indirect
npmreact-router-dom^6.21.2indirect
npmreact-scripts5.0.1indirect
npmtypescript^5.3.3indirect
PyPIaiohttp—indirect
PyPIaltair—indirect
PyPIanthropic—indirect
PyPIbandit—indirect
PyPIbeautifulsoup4—indirect
PyPIblack—indirect
PyPIblack24.3.0indirect
PyPIboto3—indirect
PyPIcatboost—indirect
PyPIclick—indirect
PyPIclick8.1.7indirect
PyPIcryptography46.0.5indirect
PyPIdask—indirect
PyPIdefusedxml—indirect
PyPIdocx2txt—indirect
PyPIdpkt—indirect
PyPIelasticsearch—indirect
PyPIfastapi—indirect
PyPIflake8—indirect
PyPIflask3.1.3indirect
PyPIgensim—indirect
PyPIgoogle-generativeai—indirect
PyPIgradio—indirect
PyPIhttpx—indirect
PyPIhuggingface-hub—indirect
PyPIhypothesis—indirect
PyPIimbalanced-learn—indirect
PyPIinstructor—indirect
PyPIipywidgets—indirect
PyPIisort—indirect
PyPIisort5.13.2indirect
PyPIjupyter-dash—indirect
PyPIjupyterlab—indirect
PyPIlangchain-anthropic—indirect
PyPIlangchain-google-genai—indirect
PyPIlangchain-openai—indirect
PyPIlief—indirect
PyPIlightgbm—indirect
PyPIlitellm—indirect
PyPIminio—indirect
PyPImypy—indirect
PyPImypy1.8.0indirect
PyPInetworkx—indirect
PyPInotebook—indirect
PyPIopenai—indirect
PyPIpefile—indirect
PyPIpolars—indirect
PyPIprometheus-client—indirect
PyPIpsycopg2-binary—indirect
PyPIpsycopg2-binary2.9.9indirect
PyPIpyarrow—indirect
PyPIpyjwt2.8.0indirect
PyPIpypdf—indirect
PyPIpyshark—indirect
PyPIpysigma—indirect
PyPIpytest—indirect
PyPIpytest7.4.4indirect
PyPIpytest-asyncio—indirect
PyPIpytest-cov—indirect
PyPIpytest-cov4.1.0indirect
PyPIpython-magic—indirect
PyPIpyyaml—indirect
PyPIpyyaml6.0.1indirect
PyPIredis—indirect
PyPIreqeusts1.0.0indirect
PyPIscapy—indirect
PyPIscipy—indirect
PyPIseaborn—indirect
PyPIspacy—indirect
PyPIsqlalchemy—indirect
PyPIsqlalchemy2.0.25indirect
PyPIstatsmodels—indirect
PyPIstreamlit—indirect
PyPItextblob—indirect
PyPItiktoken—indirect
PyPItorch—indirect
PyPItqdm—indirect
PyPItransformers—indirect
PyPItyper—indirect
PyPItyping-extensions—indirect
PyPIurllib3—indirect
PyPIurllib32.6.3indirect
PyPIuvicorn—indirect
PyPIvalidators—indirect
PyPIvolatility3—indirect
PyPIwerkzeug—indirect
PyPIwerkzeug3.1.5indirect
PyPIxgboost—indirect
PyPIyara-python—indirect
Dependency advisories 9

This repository publishes no package the index resolves, so its own dependency graph was assessed — 21 packages, which also include development and test pins that never ship: 9 carry known advisories, of which 2 are direct. 100 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

PackageVersionRelationSeverityAdvisoriesFixed in
black24.3.0indirectcritical326.3.1
cryptography46.0.5indirectcritical1150.0.0
click8.1.7indirecthigh18.3.3
pyjwt2.8.0indirecthigh112.13.0
urllib32.6.3indirecthigh42.7.0
python-dotenv1.0.0directmoderate21.2.2
requests2.32.4directmoderate22.33.0
pytest7.4.4indirectmoderate29.0.3
werkzeug3.1.5indirectmoderate23.1.6

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

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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.