Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 22:05 UTC

Parallels / rq-dashboard

Flask-based web front-end for monitoring RQ queues

Python · JavaScript · HTMLCustom license★ 1,523 stars⑂ 333 forkssince Jan 2012View on GitHub ↗
KindLibraryCommand-line toolhow this is determined

Parallels/rq-dashboard holds a health index of 81 out of 100, placing it in the Excellent band. It scores highest on Sustainability & Governance (81/100) and lowest on AI Readiness (52/100). It was last updated 71 days ago. 3 contributors account for most of its recent work.

81
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.

81
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 69 is calibrated to 81 on the published index scale (record calibration 2026-08-02).

Ownership

Parallels, Inc.Organization · verified domain
139 followers51 public repossince Dec 2013

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIrq-dashboard0.9.0322,8023577 days ago

Metrics by category

Vitality

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

60Moderate · 21% of overall
How it's scored
18/36Push recency — last push 71 days ago
5.5/36Commit cadence — 8/52 weeks with commits
10/18Commit volume — 12 commits in the last year
4/10OpenSSF Scorecard: Maintained — 3 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 4
Inputs used
commits_last_year12
human_commit_share0.88
days_since_last_push71
active_weeks_last_year8
How it's scored
27/27Ships releases — 26 releases published
36/36Release recency — latest release 77 days ago
19.8/27Release cadence — a release every ~81.5 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count26
latest_release_tagv0.9.0
releases_from_tagsno
days_since_latest_release77
mean_days_between_releases81.5
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?

73Good · 17% of overall
How it's scored
51.6/60Stars — 1,523 stars
21/25Forks — 333 forks
8.7/15Watchers — 38 watchers
Inputs used
forks333
stars1,523
watchers38
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges4
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateyes
How it's scored
73.5/80Monthly downloads — 322,802 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesrq-dashboard
dependents—
ecosystemspypi
total_downloads—
monthly_downloads322,802
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?

81Excellent · 23% of overall
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
16.7/22.5Commit distribution — top contributor authored 26% of commits
13.5/13.5Contributor breadth — 69 contributors
10/10OpenSSF Scorecard: Contributors — project has 21 contributing companies or organizations
Inputs used
bus_factor3
contributors_sampled69
top_contributor_share0.259
How it's scored
31.6/42Issue resolution — 75% of issues closed
19.1/30PR acceptance — 190/299 decided PRs merged
0/13Newcomer PR acceptance — 0/1 first-time contributors' PRs merged in 30d
12/15OpenSSF Scorecard: Code-Review — Found 15/17 approved changesets -- score normalized to 8
Inputs used
merged_prs190
open_issues53
closed_issues162
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d1
issue_closed_ratio0.753
closed_unmerged_prs109
first_time_authors_30d1
first_time_prs_merged_30d0
first_time_prs_decided_30d1
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
15.4/25Owner reach — 139 followers of Parallels
24.5/25Track record — 51 public repos, account ~12 yr old
Inputs used
followers139
owner_typeOrganization
is_verifiedyes
owner_loginParallels
public_repos51
account_age_days4,656

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 77 days ago
20/20Version history — 35 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesrq-dashboard
ecosystemspypi
any_deprecatedno
min_days_since_publish77

Engineering Quality

Are baseline engineering and documentation practices in place?

73Good · 19% of overall
How it's scored
24/24CI workflows — 3 workflow(s)
24/24Tests present
16/16Linter config — setup.cfg ([flake8]), tox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
8/20OpenSSF Scorecard: CI-Tests — 10 out of 25 merged PRs checked by a CI test -- score normalized to 4
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — http://python-rq.org/
10/10Repository description
10/10Topics — 2 topics
10/10Wiki
Inputs used
topicspython, rq
has_wikiyes
homepagehttp://python-rq.org/
docs_sitehttp://python-rq.org/
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

60Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
1/2.5CI-Tests — 10 out of 25 merged PRs checked by a CI test -- score normalized to 4
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
6/7.5Code-Review — Found 15/17 approved changesets -- score normalized to 8
2.5/2.5Contributors — project has 21 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
2.2/2.5License — license file detected
3/7.5Maintained — 3 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 4
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 17 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5
Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. 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_packages15
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:rq-dashboard@0.9.0 runtime dependency closure — what installing the published package pulls in — 15 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.

52Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
30.3/40Legible commit history — 50 of 88 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.568
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — setup.cfg ([flake8]), tox.ini
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 5 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.05
How it's scored
0/45Type-checkable code — Python without a type-check config
48.1/55Manageable file sizes — 3/24 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes230,599
source_files_sampled24
oversized_source_files3

Key facts

1,523GitHub stars
69contributors
12commits, last 12 months
71days since last push
26releases
3bus factor
53open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 333 ⇿
0Stars
333Forks
26Releases

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.

012525037532352012-052019-072026-08
Major 0Minor 6Patch 19

Each point covers 13 days.

OpenSSF Scorecard 5.0 / 10
5.0aggregate

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 · 2026-09-17 22:05 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
4CI-Tests10 out of 25 merged PRs checked by a CI test -- score normalized to 4
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
8Code-ReviewFound 15/17 approved changesets -- score normalized to 8
10Contributorsproject has 21 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
4Maintained3 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 4
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities17 existing vulnerabilities detected
All dependencies 20

Full resolved dependency set from the GitHub dependency graph: 0 direct and 20 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIarrow—indirect
PyPIarrow1.2.3indirect
PyPIbackports-functools-lru-cache1.5indirect
PyPIblack—indirect
PyPIclick8.1.7indirect
PyPIflask—indirect
PyPIflask2.3.2indirect
PyPIisort—indirect
PyPIitsdangerous2.1.2indirect
PyPIjinja23.1.2indirect
PyPImarkupsafe2.1.3indirect
PyPIpython-dateutil2.8.2indirect
PyPIredis—indirect
PyPIredis5.2.0indirect
PyPIredis-sentinel-url—indirect
PyPIredis-sentinel-url1.0.1indirect
PyPIrq—indirect
PyPIrq2.0.0indirect
PyPIsix1.16.0indirect
PyPIwerkzeug2.3.7indirect
Dependency advisories 0

Installing pypi:rq-dashboard@0.9.0 pulls in 15 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.