Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-24 20:47 UTC

thinking-machines-lab / tinker-cookbook

Post-training with Tinker

PythonApache-2.0★ 4,214 stars⑂ 553 forkssince Jul 2025View on GitHub ↗

thinking-machines-lab/tinker-cookbook holds a health index of 88 out of 100, placing it in the Excellent band. It scores highest on Vitality (99/100) and lowest on Security (16/100). It was last updated 2 days ago. 3 contributors account for most of its recent work.

88
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.

88
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 74 is calibrated to 88 on the published index scale (record calibration 2026-08-02).

Ownership

Thinking MachinesOrganization
1,897 followers9 public repossince Jul 2025

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPItinker_cookbook0.5.8359,058193 days ago

Metrics by category

Vitality

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

99Exceptional · 21% of overall
How it's scored
36/36Push recency — last push 2 days ago
35.3/36Commit cadence — 51/52 weeks with commits
18/18Commit volume — 503 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year503
human_commit_share0.85
days_since_last_push2
active_weeks_last_year51

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 11 releases published
36/36Release recency — latest release 3 days ago
27/27Release cadence — a release every ~23 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count11
latest_release_tagv0.5.8
releases_from_tagsno
days_since_latest_release3
mean_days_between_releases23

Community & Adoption

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

84Excellent · 17% of overall
How it's scored
58.8/60Stars — 4,214 stars
22.9/25Forks — 553 forks
8.3/15Watchers — 32 watchers
Inputs used
forks553
stars4,214
watchers32
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges4
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno
How it's scored
74.1/80Monthly downloads — 359,058 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagestinker_cookbook
dependents—
ecosystemspypi
total_downloads—
monthly_downloads359,058
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?

75Good · 23% of overall
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
15.5/22.5Commit distribution — top contributor authored 31% of commits
13.5/13.5Contributor breadth — 72 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor3
contributors_sampled72
top_contributor_share0.311
How it's scored
32.4/42Issue resolution — 77% of issues closed
22.2/30PR acceptance — 510/688 decided PRs merged
4.3/13Newcomer PR acceptance — 1/3 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs510
open_issues43
closed_issues146
prs_merged_7d0
prs_decided_7d4
prs_merged_30d3
prs_decided_30d9
issue_closed_ratio0.772
closed_unmerged_prs178
first_time_authors_30d3
first_time_prs_merged_30d1
first_time_prs_decided_30d3
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
23.6/25Owner reach — 1,897 followers of thinking-machines-lab
9.8/25Track record — 9 public repos, account ~1 yr old
Inputs used
followers1,897
owner_typeOrganization
is_verifiedno
owner_loginthinking-machines-lab
public_repos9
account_age_days469

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 3 days ago
20/20Version history — 19 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagestinker_cookbook
ecosystemspypi
any_deprecatedno
min_days_since_publish3

Engineering Quality

Are baseline engineering and documentation practices in place?

81Excellent · 19% of overall
How it's scored
24/24CI workflows — 13 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
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://tinker-docs.thinkingmachines.ai/
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics—
has_wikiyes
homepage—
docs_sitehttps://tinker-docs.thinkingmachines.ai/
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

16Critical · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.
How it's scored
13.2/35Direct dependencies free of known advisories — 1 affected: transformers 5.5.4 (high 7.8)
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
40/40No advisories left outstanding — no advisory has been public longer than 90 days
Inputs used
sourceosv
advisories4
affected_packages1
assessed_packages98
unassessed_packages0
affected_by_severityhigh 1
direct_affected_packages1
Matched the pypi:tinker_cookbook@0.5.8 runtime dependency closure — what installing the published package pulls in — 98 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.

81Excellent · 4% of overall
How it's scored
45/45Agent instructions — AGENTS.md, CLAUDE.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.anthropic.com/llms.txt)
40/40Legible commit history — 79 of 85 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.anthropic.com/llms.txt
legible_history_share0.929
agent_instruction_filesAGENTS.md, CLAUDE.md
agent_instruction_max_bytes8,029
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — tinker_cookbook/py.typed
0/10Reproducible environment
10/10Demonstrated agent practice — 37 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_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configstinker_cookbook/py.typed
agent_commit_share0.37
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (tinker_cookbook/py.typed)
54.6/55Manageable file sizes — 4/503 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes89,268
source_files_sampled503
oversized_source_files4
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, recipes
Inputs used
example_dirsexamples, recipes
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

4,214GitHub stars
72contributors
503commits, last 12 months
2days since last push
11releases
3bus factor
43open 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 did not return a usable result (killed by SIGKILL — most likely the container's memory limit); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 553 ⇿
0Stars
553Forks
10Releases

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.

0100200300400500600546152025-102026-042026-10
Major 0Minor 3Patch 7
Direct dependencies 20
RegistryPackageVersion constraintManifest
PyPIaiohttp>=3.9.0pyproject.toml
PyPIanyio>=4.0.0pyproject.toml
PyPIblobfile>=3.0.0pyproject.toml
PyPIchz>=0.4.0pyproject.toml
PyPIcloudpickle>=3.0.0pyproject.toml
PyPIdatasets>=2.14.0pyproject.toml
PyPIhuggingface_hub>=0.20.0pyproject.toml
PyPInumpy>=1.24.0pyproject.toml
PyPIpillow>=10.0.0pyproject.toml
PyPIpydantic>=2.0.0pyproject.toml
PyPIpyrage>=1.4.0pyproject.toml
PyPIrich>=13.0.0pyproject.toml
PyPIsafetensors>=0.4.0pyproject.toml
PyPItermcolor>=2.0.0pyproject.toml
PyPItiktoken>=0.12.0pyproject.toml
PyPItinker>=0.34.0pyproject.toml
PyPItml-renderers>=0.0.1pyproject.toml
PyPItorch>=2.10pyproject.toml
PyPItqdm>=4.60.0pyproject.toml
PyPItransformers>=4.57.6,!=5.4.*,!=5.5.0,!=5.5.1,!=5.5.2,!=5.5.3,<=5.5.4pyproject.toml
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

Dependency advisories 1

Installing pypi:tinker_cookbook@0.5.8 pulls in 98 packages, direct and transitive: 1 carry known advisories, of which 1 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
transformers5.5.4directhigh45.10.0

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.