Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-13 23:17 UTC

sayak-sarkar / contextlake

A local context layer for AI tools: mirror your repositories, index them into a knowledge graph, and serve it over MCP so agents answer from real source instead of guessing.

Python · HTMLMIT★ 2 stars⑂ 1 forksince Jun 2026View on GitHub ↗
KindCommand-line toolLibraryhow this is determined
#ai#cli#code-search#context#gitlab#knowledge-graph#mcp#developer-tools#git#offline-first#python#semantic-search

sayak-sarkar/contextlake holds a health index of 75 out of 100, placing it in the Good band. It scores highest on Engineering Quality (95/100) and lowest on Sustainability & Governance (43/100). It was last updated today. A single contributor accounts for most of its recent work.

75
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.

75
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 65 is calibrated to 75 on the published index scale (record calibration 2026-08-02).

Ownership

Sayak SarkarPersonal account
101 followers108 public repossince Feb 2012Accelya Inc.

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
PyPIcontextlake9.2.1-1586 days agocontextknowledge-graphmcpaicode-searchgitlabgitmirrorcliglabworkspaceoffline-firstsemantic-searchdeveloper-tools

Metrics by category

Vitality

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

83Excellent · 21% of overall

Development activity

78Good
How it's scored
36/36Push recency — last push 0 days ago
23.5/36Commit cadence — 34/52 weeks with commits
18/18Commit volume — 1,355 commits in the last year
0/10OpenSSF Scorecard: Maintained — project was created within the last 90 days. Please review its contents carefully
Inputs used
commits_last_year1,355
human_commit_share1
days_since_last_push0
active_weeks_last_year34

Release discipline

90Excellent
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 6 days ago
27/27Release cadence — a release every ~0.9 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count100
latest_release_tagv9.2.1
releases_from_tagsno
days_since_latest_release6
mean_days_between_releases0.9

Community & Adoption

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

43Weak · 17% of overall

Popularity & adoption

1Critical
How it's scored
0/60Stars — 2 stars
0/25Forks — 1 forks
0/15Watchers — 0 watchers
Inputs used
forks1
stars2
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
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_badges5
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?

43Weak · 23% of overall

Maintainer resilience (bus factor)

20At Risk
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
10/10OpenSSF Scorecard: Contributors — project has 18 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled1
top_contributor_share1

Issue & PR responsiveness

7Critical
How it's scored
0/42Issue resolution — no issues or no data
3.3/30PR acceptance — 1/9 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs1
open_issues0
closed_issues0
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio—
closed_unmerged_prs8
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Issue resolution, Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

62Moderate
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
14.4/25Owner reach — 101 followers of sayak-sarkar
25/25Track record — 108 public repos, account ~14 yr old
Inputs used
followers101
owner_typeUser
is_verified—
owner_loginsayak-sarkar
public_repos108
account_age_days5,331
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 6 days ago
20/20Version history — 158 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagescontextlake
ecosystemspypi
any_deprecatedno
min_days_since_publish6

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall

Engineering practices

92Excellent
How it's scored
24/24CI workflows — 5 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
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — http://sayak.in/contextlake/
10/10Repository description
10/10Topics — 12 topics
10/10Wiki
Inputs used
topicsai, cli, code-search, context, gitlab, knowledge-graph, mcp, developer-tools, git, offline-first, python, semantic-search
has_wikiyes
homepagehttp://sayak.in/contextlake/
docs_sitehttp://sayak.in/contextlake/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

62Moderate · 16% of overall

Security posture

53Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — no data
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/30 approved changesets -- score normalized to 0
2.5/2.5Contributors — project has 18 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
0/7.5Maintained — project was created within the last 90 days. Please review its contents carefully
5/5Packaging — packaging workflow detected
2/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
5/5SAST — SAST tool detected: CodeQL
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5.3
Excluded from scoring (no data or not applicable): CI-Tests. Remaining weights renormalized.

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_packages1
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:contextlake@9.2.1 runtime dependency closure — what installing the published package pulls in — 1 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.

67Good · 4% of overall

Agent context & guidance

55Moderate
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt present
40/40Legible commit history — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_url—
legible_history_share1
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

70Good
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
4/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifeststests/kb/fixtures/fuzz/manifest_csproj_repeated_prefix.csproj
dependency_bot_commit_share0

Code legibility for models

53Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
52.7/55Manageable file sizes — 21/502 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes3,565,023
source_files_sampled502
oversized_source_files21

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

2GitHub stars
1contributors
1,355commits, last 12 months
0days since last push
100releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'contextlake' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced

More detail

OpenSSF Scorecard 5.3 / 10
5.3aggregate

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 · 2026-09-13 23:17 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
10Contributorsproject has 18 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintainedproject was created within the last 90 days. Please review its contents carefully
10Packagingpackaging workflow detected
4Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 4
10SASTSAST tool detected: CodeQL
10Security-Policysecurity policy file detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 1
RegistryPackageVersion constraintManifest
PyPIargcomplete>=3.0pyproject.toml
All dependencies 550

Full resolved dependency set from the GitHub dependency graph: 1 direct and 549 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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PyPIav—indirect
PyPIbuild—indirect
PyPIfastembed—indirect
PyPIfaster-whisper—indirect
PyPIhuggingface-hub—indirect
PyPIhypothesis—indirect
PyPImcp—indirect
PyPImodel2vec—indirect
PyPIopenvino-genai—indirect
PyPIpre-commit—indirect
PyPIpydantic—indirect
PyPIpypdf—indirect
PyPIpytest—indirect
PyPIpytest-cov—indirect
PyPIpytest-timeout—indirect
PyPIpytest-xdist—indirect
PyPIrapidocr-onnxruntime—indirect
PyPIruff—indirect
PyPIsetuptools—indirect
PyPIsqlite-vec—indirect
PyPItomlkit—indirect
PyPItree-sitter—indirect
PyPItree-sitter-bash—indirect
PyPItree-sitter-c—indirect
PyPItree-sitter-c-sharp—indirect
PyPItree-sitter-cpp—indirect
PyPItree-sitter-css—indirect
PyPItree-sitter-dart—indirect
PyPItree-sitter-dockerfile—indirect
PyPItree-sitter-elixir—indirect
PyPItree-sitter-go—indirect
PyPItree-sitter-hcl—indirect
PyPItree-sitter-html—indirect
PyPItree-sitter-java—indirect
PyPItree-sitter-javascript—indirect
PyPItree-sitter-kotlin—indirect
PyPItree-sitter-make—indirect
PyPItree-sitter-nix—indirect
PyPItree-sitter-perl—indirect
PyPItree-sitter-php—indirect
PyPItree-sitter-python—indirect
PyPItree-sitter-ruby—indirect
PyPItree-sitter-rust—indirect
PyPItree-sitter-scala—indirect
PyPItree-sitter-svelte—indirect
PyPItree-sitter-swift—indirect
PyPItree-sitter-typescript—indirect
PyPItree-sitter-zig—indirect
PyPItwine—indirect
Dependency advisories 0

Installing pypi:contextlake@9.2.1 pulls in 1 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.

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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.