Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-13 01:39 UTC

cisco-ai-defense / skill-scanner

Security Scanner for Agent Skills

PythonCustom license★ 2,423 stars⑂ 305 forkssince Jan 2026View on GitHub ↗
KindCommand-line toolLibraryNetwork serviceTerminal interfacehow this is determined

cisco-ai-defense/skill-scanner holds a health index of 95 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (93/100) and lowest on AI Readiness (68/100). It was last updated 8 days ago. 5 contributors account for most of its recent work.

95
overall / 100
Exceptional

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.

95
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 82 is calibrated to 95 on the published index scale (record calibration 2026-08-02).

Ownership

Cisco AI DefenseOrganization
533 followers17 public repossince Jul 2025

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

Metrics by category

Vitality

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

81Excellent · 21% of overall
How it's scored
28.8/36Push recencylast push 8 days ago
11.8/36Commit cadence17/52 weeks with commits
17.8/18Commit volume95 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year95
human_commit_share1
days_since_last_push8
active_weeks_last_year17

Release discipline

100Exceptional
How it's scored
27/27Ships releases18 releases published
36/36Release recencylatest release 9 days ago
27/27Release cadencea release every ~15.9 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count18
latest_release_tag2.0.13
releases_from_tagsno
days_since_latest_release9
mean_days_between_releases15.9
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

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

84Excellent · 17% of overall
How it's scored
54.9/60Stars2,423 stars
20.7/25Forks305 forks
6/15Watchers13 watchers
Inputs used
forks305
stars2,423
watchers13
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

86Excellent
How it's scored
22.5/22.5README
16.9/22.5Licenselicense file present, not a recognized license
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_badges7
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?

83Excellent · 23% of overall
How it's scored
45.9/54Bus factor5 contributor(s) cover half of all commits
14.2/22.5Commit distributiontop contributor authored 37% of commits
13.5/13.5Contributor breadth41 contributors
3/10OpenSSF Scorecard: Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor5
contributors_sampled41
top_contributor_share0.368
How it's scored
37.7/42Issue resolution90% of issues closed
23.9/30PR acceptance87/109 decided PRs merged
13/13Newcomer PR acceptance10/10 first-time contributors' PRs merged in 30d
10.5/15OpenSSF Scorecard: Code-ReviewFound 22/30 approved changesets -- score normalized to 7
Inputs used
merged_prs87
open_issues5
closed_issues44
prs_merged_7d0
prs_decided_7d1
prs_merged_30d19
prs_decided_30d21
issue_closed_ratio0.898
closed_unmerged_prs22
first_time_authors_30d7
first_time_prs_merged_30d10
first_time_prs_decided_30d10
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
19.6/25Owner reach533 followers of cisco-ai-defense
11.3/25Track record17 public repos, account ~1 yr old
Inputs used
followers533
owner_typeOrganization
is_verified
owner_logincisco-ai-defense
public_repos17
account_age_days393
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 9 days ago
20/20Version history17 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagescisco-ai-skill-scanner
ecosystemspypi
any_deprecatedno
min_days_since_publish9

Engineering Quality

Are baseline engineering and documentation practices in place?

93Exceptional · 19% of overall
How it's scored
24/24CI workflows6 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
14/20OpenSSF Scorecard: CI-Tests21 out of 30 merged PRs checked by a CI test -- score normalized to 7
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 sitehttps://cisco-ai-defense.github.io/docs/skill-scanner
10/10Repository description
10/10Topics3 topics
10/10Wiki
Inputs used
topicsagent, agent-skills, security
has_wikiyes
homepagehttps://cisco-ai-defense.github.io/docs/skill-scanner
docs_sitehttps://cisco-ai-defense.github.io/docs/skill-scanner
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

70Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
6/7.5Branch-Protectionbranch protection is not maximal on development and all release branches
1.8/2.5CI-Tests21 out of 30 merged PRs checked by a CI test -- score normalized to 7
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
5.2/7.5Code-ReviewFound 22/30 approved changesets -- score normalized to 7
0.8/2.5Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.2/2.5Licenselicense file detected
7.5/7.5Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5Packagingpackaging workflow detected
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
1.5/5SASTSAST tool is not run on all commits -- score normalized to 3
5/5Security-Policysecurity policy file detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate6.2
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

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_packages89
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:cisco-ai-skill-scanner@2.0.13 runtime dependency closure — what installing the published package pulls in — 89 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.

68Good · 4% of overall
How it's scored
45/45Agent instructions.cursor/rules/codeguard-0-additional-cryptography.mdc, .cursor/rules/codeguard-0-framework-and-languages.mdc, .cursor/rules/codeguard-0-iac-security.mdc, .cursor/rules/codeguard-0-mobile-apps.mdc, .cursor/rules/codeguard-0-supply-chain-security.mdc, .cursor/rules/codeguard-1-crypto-algorithms.mdc, .cursor/rules/codeguard-1-digital-certificates.mdc, .cursor/rules/codeguard-1-hardcoded-credentials.mdc, .cursor/rules/no-cursor-co-author.mdc
0/15Machine-readable docs (llms.txt)
40/40Legible commit history91 of 95 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.958
agent_instruction_files.cursor/rules/codeguard-0-additional-cryptography.mdc, .cursor/rules/codeguard-0-framework-and-languages.mdc, .cursor/rules/codeguard-0-iac-security.mdc, .cursor/rules/codeguard-0-mobile-apps.mdc, .cursor/rules/codeguard-0-supply-chain-security.mdc, .cursor/rules/codeguard-1-crypto-algorithms.mdc, .cursor/rules/codeguard-1-digital-certificates.mdc, .cursor/rules/codeguard-1-hardcoded-credentials.mdc, .cursor/rules/no-cursor-co-author.mdc
agent_instruction_max_bytes6,285
How it's scored
18/18One-command bootstrapMakefile
22/22Automated tests
11/11Lint / format config
0/11Static type checking
10/10Reproducible environmentlockfile
10/10Demonstrated agent practice11 of the last 95 commits agent-authored or agent-credited
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0.116
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
54.8/55Manageable file sizes1/222 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes128,195
source_files_sampled222
oversized_source_files1
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examplesexamples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_files
interfaces_expected_ofnetwork-service

Key facts

2,423GitHub stars
41contributors
95commits, last 12 months
8days since last push
18releases
5bus factor
5open issues
PyPIpackage ecosystems

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

More detail

Star and fork history 0 ★ / 305 ⇿
0Stars
305Forks
18Releases

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.

0125250375305172026-012026-052026-08
Major 2Minor 0Patch 15
OpenSSF Scorecard 6.2 / 10
6.2aggregate

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 · 2026-08-13 01:39 UTC

10Binary-Artifactsno binaries found in the repo
8Branch-Protectionbranch protection is not maximal on development and all release branches
7CI-Tests21 out of 30 merged PRs checked by a CI test -- score normalized to 7
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
7Code-ReviewFound 22/30 approved changesets -- score normalized to 7
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
3SASTSAST tool is not run on all commits -- score normalized to 3
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 20
RegistryPackageVersion constraintManifest
PyPIPyYAML>=6.0,<7pyproject.toml
PyPIpython-frontmatter>=1.1,<2pyproject.toml
PyPIrich>=14.0,<15pyproject.toml
PyPItextual>=7.0,<8pyproject.toml
PyPItabulate>=0.9,<1pyproject.toml
PyPIpydantic>=2.10,<3pyproject.toml
PyPIfastapi>=0.115,<1pyproject.toml
PyPIuvicorn>=0.34,<1pyproject.toml
PyPIpython-multipart>=0.0.31,<0.1pyproject.toml
PyPIyara-x>=1.10,<2pyproject.toml
PyPIpython-dotenv>=1.0,<2pyproject.toml
PyPIhttpx>=0.27,<1pyproject.toml
PyPImagika>=1.0,<2pyproject.toml
PyPIpdfid>=1.1,<2pyproject.toml
PyPIoletools>=0.60,<1pyproject.toml
PyPIconfusable-homoglyphs>=3.3,<4pyproject.toml
PyPIanthropic>=0.50,<1pyproject.toml
PyPIopenai>=2.0,<3pyproject.toml
PyPIlitellm>=1.84.0,<2pyproject.toml
PyPIclick>=8.3.3pyproject.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:cisco-ai-skill-scanner@2.0.13 pulls in 89 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 v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.