Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-12 01:30 UTC

VonderVuflya / Yggdrasil

Durable, local-first memory for AI coding agents over MCP — zero-dependency, curated & semantically de-duped, you own the data (SQLite + Markdown). Works with Claude Code, Codex & any MCP host.

PythonAGPL-3.0★ 21 stars⑂ 2 forkssince Jun 2026View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

VonderVuflya/Yggdrasil holds a health index of 10 out of 100, placing it in the Critical band. It scores highest on Vitality (74/100) and lowest on Security (1/100). It was last updated 8 days ago. A single contributor accounts for most of its recent work.

10
overall / 100
Critical

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.

10
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 49 is calibrated to 48 on the published index scale (record calibration 2026-08-02). High-Risk Jurisdiction Policy applies a 20% multiplier to weighted overall health and gives it an At Risk ceiling of 34.

Ownership

Sergey CherkashinPersonal account
16 followers33 public repossince Mar 2016

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIyggdrasil-memory0.15.07772836 days agoai-agentsclaudecodexlocal-firstmcpmemoryobsidianrag

Metrics by category

Vitality

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

74Good · 21% of overall
How it's scored
28.8/36Push recency — last push 8 days ago
4.2/36Commit cadence — 6/52 weeks with commits
18/18Commit volume — 237 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year237
human_commit_share1
days_since_last_push8
active_weeks_last_year6

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 24 releases published
36/36Release recency — latest release 36 days ago
27/27Release cadence — a release every ~3.6 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count24
latest_release_tagv0.15.0
releases_from_tagsno
days_since_latest_release36
mean_days_between_releases3.6

Community & Adoption

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

38Weak · 17% of overall
How it's scored
21.1/60Stars — 21 stars
0/25Forks — 2 forks
0/15Watchers — 1 watchers
Inputs used
forks2
stars21
watchers1
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (AGPL-3.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges5
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno
How it's scored
38.5/80Monthly downloads — 777 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesyggdrasil-memory
dependents—
ecosystemspypi
total_downloads—
monthly_downloads777
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?

49Weak · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0/22.5Commit distribution — top contributor authored 100% of commits
1.4/13.5Contributor breadth — 1 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled1
top_contributor_share1
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
8.8/25Owner reach — 16 followers of VonderVuflya
23.2/25Track record — 33 public repos, account ~10 yr old
Inputs used
followers16
owner_typeUser
is_verified—
owner_loginVonderVuflya
public_repos33
account_age_days3,845
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 36 days ago
20/20Version history — 28 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesyggdrasil-memory
ecosystemspypi
any_deprecatedno
min_days_since_publish36

Engineering Quality

Are baseline engineering and documentation practices in place?

70Good · 19% of overall
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

85Excellent
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics — 16 topics
10/10Wiki
Inputs used
topicsai-agents, ai-memory, claude, claude-code, cli, codex, coding-assistant, developer-tools, embeddings, llm, local-first, mcp, memory, model-context-protocol, rag, sqlite
has_wikiyes
homepage—
docs_site—
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

1Critical · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
high_risk_jurisdiction_cap34
high_risk_jurisdiction_multiplier20
security_posture_after_multiplier1
security_posture_before_jurisdiction1
High-Risk Jurisdiction Policy applies a 20% multiplier and gives Security posture an At risk ceiling of 34.

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.

54Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 86 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.86
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environment — Dockerfile
10/10Demonstrated agent practice — 84 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
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configno
typecheck_configs—
agent_commit_share0.84
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
53.3/55Manageable file sizes — 2/65 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes81,610
source_files_sampled65
oversized_source_files2
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
20/20MCP server
40/40Runnable examples — examples
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

21GitHub stars
1contributors
237commits, last 12 months
8days since last push
24releases
1bus factor
0open 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
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Star and fork history 0 ★ / 2 ⇿
0Stars
2Forks
5Releases

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.

111222212026-062026-062026-06
Major 0Minor 2Patch 3
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

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.