Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-05 19:12 UTC

physiclaw / PhysiClaw

The AI agent that interacts with you in the real world.

PythonMIT★ 369 stars⑂ 37 forkssince Mar 2026View on GitHub ↗
KindCommand-line toolMCP serverLibraryhow this is determined

physiclaw/PhysiClaw holds a health index of 77 out of 100, placing it in the Good band. It scores highest on Engineering Quality (88/100) and lowest on Security (44/100). It was last updated today. A single contributor accounts for most of its recent work.

77
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.

77
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 66 is calibrated to 77 on the published index scale (record calibration 2026-08-02).

Ownership

QIAOQIANPersonal account
13 followers28 public repossince Oct 2020

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
PyPIphysiclaw0.5.0-1205 days agogrblllm-agentmcpphone-automationroboticsstylusvision

Metrics by category

Vitality

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

85Excellent · 21% of overall
How it's scored
36/36Push recencylast push 0 days ago
16.6/36Commit cadence24/52 weeks with commits
18/18Commit volume1,694 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year1,694
human_commit_share1
days_since_last_push0
active_weeks_last_year24
How it's scored
27/27Ships releases20 releases published
36/36Release recencylatest release 31 days ago
27/27Release cadencea release every ~3.5 days
0/10OpenSSF Scorecard: Signed-ReleasesProject has not signed or included provenance with any releases.
Inputs used
releases_count20
latest_release_tagphysiclaw-hardware-v0.17
releases_from_tagsno
days_since_latest_release31
mean_days_between_releases3.5

Community & Adoption

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

54Moderate · 17% of overall
How it's scored
41.6/60Stars369 stars
13/25Forks37 forks
2.7/15Watchers4 watchers
Inputs used
forks37
stars369
watchers4
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?

54Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
0/22.5Commit distributiontop contributor authored 100% of commits
1.4/13.5Contributor breadth1 contributors
0/10OpenSSF Scorecard: Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
Inputs used
bus_factor1
contributors_sampled1
top_contributor_share1
How it's scored
42/42Issue resolution100% of issues closed
0/30PR acceptanceno decided pull requests or no data
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs0
open_issues0
closed_issues1
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio1
closed_unmerged_prs0
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): PR acceptance, Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
8.2/25Owner reach13 followers of physiclaw
22.4/25Track record28 public repos, account ~5 yr old
Inputs used
followers13
owner_typeUser
is_verified
owner_loginphysiclaw
public_repos28
account_age_days2,147
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 5 days ago
20/20Version history120 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesphysiclaw
ecosystemspypi
any_deprecatedno
min_days_since_publish5

Engineering Quality

Are baseline engineering and documentation practices in place?

88Excellent · 19% of overall
How it's scored
24/24CI workflows4 workflow(s)
24/24Tests present
16/16Linter configpyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno
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 sitehttps://physiclaw.ai
10/10Repository description
10/10Topics11 topics
10/10Wiki
Inputs used
topicsagentic-ai, ai-agent, gui-agent, gui-agents, phone-automation, phone-use-agent, robot-arm, embodied-agent, embodied-ai, phone-use, phone-use-agents
has_wikiyes
homepagehttps://physiclaw.ai
docs_sitehttps://physiclaw.ai
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

44Weak · 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-Testsno data
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 0/30 approved changesets -- score normalized to 0
0/2.5Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
7.5/7.5Maintained30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
0/5Packagingno data
1.5/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 3
0/5SASTno SAST tool detected
0/5Security-Policysecurity policy file not detected
0/7.5Signed-ReleasesProject has not signed or included provenance with any releases.
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities47 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate3
Excluded from scoring (no data or not applicable): CI-Tests, Packaging. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages68
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:physiclaw@0.5.0 runtime dependency closure — what installing the published package pulls in — 68 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.

64Moderate · 4% of overall
How it's scored
45/45Agent instructionssrc/physiclaw/agent/claude/CLAUDE.md, src/physiclaw/agent/context/AGENT.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share1
agent_instruction_filessrc/physiclaw/agent/claude/CLAUDE.md, src/physiclaw/agent/context/AGENT.md
agent_instruction_max_bytes11,350
How it's scored
18/18One-command bootstrapMakefile
22/22Automated tests
11/11Lint / format configpyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environmentlockfile
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
3/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 3
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
55/55Manageable file sizes0/635 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes55,937
source_files_sampled635
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
20/20MCP server
0/40Runnable examples
Inputs used
example_dirs
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

369GitHub stars
1contributors
1,694commits, last 12 months
0days since last push
20releases
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 'physiclaw' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

More detail

Star and fork history 0 ★ / 37 ⇿
0Stars
37Forks
7Releases

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.

01325383792026-072026-082026-09
Major 0Minor 0Patch 0
OpenSSF Scorecard 3.0 / 10
3.0aggregate

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 · 2026-09-05 19:12 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
0Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
3Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 3
0SASTno SAST tool detected
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities47 existing vulnerabilities detected
Direct dependencies 13
RegistryPackageVersion constraintManifest
PyPIpyserial>=3.5pyproject.toml
PyPIopencv-python>=4.8pyproject.toml
PyPInumpy>=1.26pyproject.toml
PyPImcp>=2.1,<3pyproject.toml
PyPIhttpx>=0.27pyproject.toml
PyPIhttpx2>=2.5pyproject.toml
PyPIcroniter>=6.2.2pyproject.toml
PyPIrapidocr>=3.7.0pyproject.toml
PyPIonnxruntime>=1.24.0pyproject.toml
PyPItyper>=0.12pyproject.toml
PyPItomlkit>=0.13pyproject.toml
PyPIanthropic>=0.40pyproject.toml
PyPIruamel-yaml>=0.18pyproject.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 0

Installing pypi:physiclaw@0.5.0 pulls in 68 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 statisticsPyPI.