Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-12 20:37 UTC

cool-RR / PySnooper

Never use print for debugging again

PythonMIT★ 16,578 stars⑂ 961 forkssince Apr 2019View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

cool-RR/PySnooper holds a health index of 50 out of 100, placing it in the Moderate band. It scores highest on Community & Adoption (76/100) and lowest on Security (32/100). It was last updated 65 days ago. A single contributor accounts for most of its recent work.

50
overall / 100
Moderate

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.

50
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 50 is calibrated to 50 on the published index scale (record calibration 2026-08-02).

Ownership

Ram RachumPersonal account
547 followers238 public repossince Feb 2009University of California, Berkeley

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 publish
PyPIPySnooper1.2.3-62437 days ago

Metrics by category

Vitality

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

36Weak · 21% of overall
How it's scored
18/36Push recencylast push 65 days ago
2.8/36Commit cadence4/52 weeks with commits
7.6/18Commit volume6 commits in the last year
2/10OpenSSF Scorecard: Maintained3 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 2
Inputs used
commits_last_year6
human_commit_share1
days_since_last_push65
active_weeks_last_year4
How it's scored
27/27Ships releases1 releases published
0/36Release recencylatest release 950 days ago
12.6/27Release cadencecadence unknown (single release)
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count1
latest_release_tag1.2.0
releases_from_tagsno
days_since_latest_release950
mean_days_between_releases
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?

76Good · 17% of overall
How it's scored
60/60Stars16,578 stars
24.9/25Forks961 forks
13.1/15Watchers228 watchers
Inputs used
forks961
stars16,578
watchers228
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_services
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

61Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
9.5/22.5Commit distributiontop contributor authored 58% of commits
13.5/13.5Contributor breadth27 contributors
10/10OpenSSF Scorecard: Contributorsproject has 11 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled27
top_contributor_share0.579
How it's scored
33.6/42Issue resolution80% of issues closed
18.4/30PR acceptance75/122 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
1.5/15OpenSSF Scorecard: Code-ReviewFound 3/29 approved changesets -- score normalized to 1
Inputs used
merged_prs75
open_issues28
closed_issues111
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.799
closed_unmerged_prs47
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
19.7/25Owner reach547 followers of cool-RR
25/25Track record238 public repos, account ~17 yr old
Inputs used
followers547
owner_typeUser
is_verified
owner_logincool-RR
public_repos238
account_age_days6,380
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.
How it's scored
25/25Published & resolvable1 package(s) on pypi
14/35Publish recencylatest publish 437 days ago
20/20Version history62 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesPySnooper
ecosystemspypi
any_deprecatedno
min_days_since_publish437

Engineering Quality

Are baseline engineering and documentation practices in place?

44Weak · 19% of overall
How it's scored
0/24CI workflows
24/24Tests present
16/16Linter configtox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests0 out of 8 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_cino
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

50Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics5 topics
0/10Wiki
Inputs used
topicspython, debug, debugger, introspection, logging
has_wikino
homepage
docs_site
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

32At Risk · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionbranch protection not enabled on development/release branches
0/2.5CI-Tests0 out of 8 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-ReviewFound 3/29 approved changesets -- score normalized to 1
2.5/2.5Contributorsproject has 11 contributing companies or organizations
0/10Dangerous-Workflowno data
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
1.5/7.5Maintained3 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 2
0/5Packagingno data
0/5Pinned-Dependenciesno data
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsno data
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated13
scorecard_versionv5.5.0
checks_inconclusive5
scorecard_aggregate3.2
Excluded from scoring (no data or not applicable): Dangerous-Workflow, Packaging, Pinned-Dependencies, Signed-Releases, Token-Permissions. Remaining weights renormalized.

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.

39Weak · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
4.3/40Legible commit history8 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.08
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configtox.ini
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Pinned-Dependencies. Remaining weights renormalized.
How it's scored
0/45Type-checkable codePython without a type-check config
53.1/55Manageable file sizes1/29 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes64,835
source_files_sampled29
oversized_source_files1
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplessamples
Inputs used
example_dirssamples
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

16,578GitHub stars
27contributors
6commits, last 12 months
65days since last push
1releases
1bus factor
28open 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 ★ / 961 ⇿
0Stars
961Forks
1Releases

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.

2004006008001,0009553472019-042022-122026-08
Major 0Minor 1Patch 0

Each point covers 7 days.

OpenSSF Scorecard 3.2 / 10
3.2aggregate

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 · 2026-08-12 20:36 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
0CI-Tests0 out of 8 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 3/29 approved changesets -- score normalized to 1
10Contributorsproject has 11 contributing companies or organizations
n/aDangerous-Workflowno workflows found
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
2Maintained3 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 2
n/aPackagingpackaging workflow not detected
n/aPinned-Dependenciesno dependencies found
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
n/aToken-PermissionsNo tokens found
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 1
RegistryPackageVersion constraintManifest
PyPIfile: requirements.insetup.cfg
All dependencies 0

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

RegistryPackageVersionRelation
Dependency advisories not assessed

Advisory matching could not run for this report: No resolved dependencies to assess

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 v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.