How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption. Grade D reflects the trust score band: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Full methodology →
Signals refreshed2026-06-22 00:01 UTC·Repo last pushed 2 days ago
Rank Trend
2026-06-202026-06-21
Activity & Reach
Stars
23.3k
Forks
1.4k
Last Push
2026-06-20
2 days ago
Commits (4 wk)
1324
Downloads (7d)
—
HN mentions (30d)
—
Open Issues
699
Rank Change
=
was #255
Analysis
HVTrust Dimensions
25.6 / 100 · 50.0% confidence
Safety / IntegrityOSSF, provenance, signatures
1.6 / 25
Identity / ProvenanceListing and build link
10.8 / 18
TransparencyLicense and public checks
8.5 / 17
MaintenanceFreshness and commits
19.9 / 20
AdoptionStars and downloads
10.5 / 20
Activity Inputs
90.8 / 100
StarsRepository reach
26.2 / 30
FreshnessLast push recency
24.7 / 25
ActivityRecent commits
25 / 25
CommunityFork signal
14.6 / 20
Supply Chain Trust
Package Provenance
None
No package attestations found
OSSF Scorecard
—
Not available
Signed Commits
31%
of last 100 commits verified
Is DeepSeek-Reasonix safe?
Public trust evidence for DeepSeek-Reasonix is thin: several supply-chain signals are missing or weak. This does not mean the project is unsafe — it means an outside observer cannot easily verify the usual integrity checks. Treat with extra scrutiny.
Does DeepSeek-Reasonix publish package provenance?
No published build provenance is currently detected for DeepSeek-Reasonix. This is common for open-source projects but means consumers cannot independently verify that the package on the registry matches the GitHub source.
Does DeepSeek-Reasonix have an OpenSSF Scorecard?
No OpenSSF Scorecard data is currently published for DeepSeek-Reasonix. Maintainers can enable the Scorecard GitHub Action to get a public score; without it, automated supply-chain hygiene is harder for outsiders to verify.
Is DeepSeek-Reasonix actively maintained?
Actively maintained. The repository was pushed to within the last 2 day(s).
What license does DeepSeek-Reasonix use?
DeepSeek-Reasonix ships under MIT. A declared, OSI-approved license is one of the transparency signals HVTrust scores.
Are DeepSeek-Reasonix's commits signed?
31% of the last 100 commits to DeepSeek-Reasonix are verified-signed (GPG, SSH, S/MIME, or GitHub's signing flow). Signed commits help confirm that code was authored by who the commit claims.
Not a safety endorsement. HVTracker describes what public signals show, not whether a project is safe for your use case. Run your own security review before adopting in production.
AI agent surface
Profile context only
HVTrust currently ranks supply-chain and project-integrity trust only. This public view shows a compact AI-agent surface snapshot from repo docs and manifests. These fields are descriptive context and do not affect the production HVTrust rank. An experimental local preview remains available in Score Lab →, and the policy boundary is tracked on the roadmap →
MCP Server Support
None detected
No MCP server signal detected.
Detailed evidence is not shown in the public view.
External Service Dependencies
medium confidence
1 detected
Public provider/service dependencies detected.
OpenAI
Credential signal:
API keys or service config markers documented.
Tool / Plugin Surface
high confidence
Declared
Declared plugin/integration surface detected.
code
shell
Detailed evidence is not shown in the public view.
Package Provenance Drift
N/A
No package source configured
Detailed evidence is not shown in the public view.
MCP signal live
External deps live
Tool / plugin surface live
Package provenance drift live
Maintain DeepSeek-Reasonix?
HVTrust scores DeepSeek-Reasonix from public signals only — we never contact maintainers first. If a signal is wrong, stale, or missing (provenance you publish, a Scorecard you run, signed releases), tell us and we'll review it. Corrections are public and tracked on GitHub.
Data sources
GitHub REST API (repo, commits, stars, forks, license)
Each agent's signals refresh once daily across 6 staggered batches. Methodology v3.2 · Raw JSON
