Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-15 20:27 UTC

AmbiqAI / neuralspotx

NSX workspace-first Python tooling and CLI for bare-metal Ambiq applications

PythonBSD-3-Clause★ 3 stars⑂ 1 forksince Mar 2026View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

AmbiqAI/neuralspotx holds a health index of 83 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (95/100) and lowest on Community & Adoption (34/100). It was last updated 5 days ago. A single contributor accounts for most of its recent work.

83
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.

83
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 70 is calibrated to 83 on the published index scale (record calibration 2026-08-02).

Ownership

Ambiq AIOrganization
77 followers70 public repossince Mar 2022

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIneuralspotx0.8.13,149265 days agoembeddedfirmwareambiqneuralspotbuild-system

Metrics by category

Vitality

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

87Excellent · 21% of overall
How it's scored
36/36Push recency — last push 5 days ago
15.9/36Commit cadence — 23/52 weeks with commits
18/18Commit volume — 339 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year339
human_commit_share0.77
days_since_last_push5
active_weeks_last_year23

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 35 releases published
36/36Release recency — latest release 5 days ago
27/27Release cadence — a release every ~4.4 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count35
latest_release_tagneuralspotx-v0.8.1
releases_from_tagsno
days_since_latest_release5
mean_days_between_releases4.4

Community & Adoption

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

34At Risk · 17% of overall
How it's scored
4.9/60Stars — 3 stars
0/25Forks — 1 forks
0/15Watchers — 0 watchers
Inputs used
forks1
stars3
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (BSD-3-Clause)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com
has_pull_request_templateno
How it's scored
46.6/80Monthly downloads — 3,149 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesneuralspotx
dependents—
ecosystemspypi
total_downloads—
monthly_downloads3,149
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?

63Moderate · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.9/22.5Commit distribution — top contributor authored 96% of commits
6.8/13.5Contributor breadth — 5 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled5
top_contributor_share0.96
How it's scored
29.7/42Issue resolution — 71% of issues closed
28.9/30PR acceptance — 207/215 decided PRs merged
13/13Newcomer PR acceptance — 1/1 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs207
open_issues10
closed_issues24
prs_merged_7d0
prs_decided_7d0
prs_merged_30d14
prs_decided_30d15
issue_closed_ratio0.706
closed_unmerged_prs8
first_time_authors_30d1
first_time_prs_merged_30d1
first_time_prs_decided_30d1
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
13.6/25Owner reach — 77 followers of AmbiqAI
22.1/25Track record — 70 public repos, account ~4 yr old
Inputs used
followers77
owner_typeOrganization
is_verifiedno
owner_loginAmbiqAI
public_repos70
account_age_days1,657

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 5 days ago
20/20Version history — 26 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesneuralspotx
ecosystemspypi
any_deprecatedno
min_days_since_publish5

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall
How it's scored
24/24CI workflows — 3 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
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://ambiqai.github.io/neuralspotx/
10/10Repository description
10/10Topics — 6 topics
10/10Wiki
Inputs used
topicsambiq, bare-metal, cmake, embedded, firmware, python
has_wikiyes
homepagehttps://ambiqai.github.io/neuralspotx/
docs_sitehttps://ambiqai.github.io/neuralspotx/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

64Moderate · 16% of overall
How it's scored
30/30Security policy (SECURITY.md)
0/25Dependabot config
25/25Dependency lockfiles — uv.lock
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfilesuv.lock
manifestspyproject.toml
has_codeql_workflowno
has_security_policyyes
has_dependabot_configno

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_packages3
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:neuralspotx@0.8.1 runtime dependency closure — what installing the published package pulls in — 3 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.

81Excellent · 4% of overall
How it's scored
45/45Agent instructions — AGENTS.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 75 of 77 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.974
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes14,161
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — src/neuralspotx/py.typed
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 60 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configssrc/neuralspotx/py.typed
agent_commit_share0.6
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (src/neuralspotx/py.typed)
54.5/55Manageable file sizes — 2/200 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes341,915
source_files_sampled200
oversized_source_files2
How it's scored
40/40API schema (OpenAPI/GraphQL/proto) — examples/usb_rpc/proto/nsx_rpc.proto
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_filesexamples/usb_rpc/proto/nsx_rpc.proto
interfaces_expected_of—
Excluded from scoring (no data or not applicable): MCP server. Remaining weights renormalized.

Key facts

3GitHub stars
5contributors
339commits, last 12 months
5days since last push
35releases
1bus factor
10open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'neuralspotx' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • pypi download figures for neuralspotx 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 (exit code 0; Skipping github.com/AmbiqAI/neuralspotx: run: repo unreachable: Get "https://api.github.com/repos/AmbiqAI/neuralspotx": internal error: innerTransport.RoundTrip: internal error: innerTransport.RoundTrip: error in HTTP: dial tcp 140.82.121.6:443: i/o timeout); skipping Scorecard checks

More detail

Direct dependencies 2
RegistryPackageVersion constraintManifest
PyPIJinja2>=3.1pyproject.toml
PyPIPyYAML>=6.0pyproject.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:neuralspotx@0.8.1 pulls in 3 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 statistics — PyPI.