Skill audit report

Plip audit report.

Protein-Ligand Interaction Profiler - Analyze and visualize non-covalent protein-ligand interactions in PDB files according to 馃摑 Schake, Bolz, et al. (2025), https://doi.org/10.1093/nar/gkaf361

REVIEWEDREVIEWNeeds reviewGenerated Jun 17, 2026Heuristic metadata audit
77
Audit
82
Trust
68
Quality
87
Security
62
Maintain
92
Install

OpenAgentSkill Trust Score

82
Strong shortlist

Stars, maintenance, license, docs, install safety, permission surface, and installability.

The Trust Score is OpenAgentSkill's adoption layer. It is designed to help an agent decide whether a skill is safe enough to shortlist before installation.

GitHub adoption

INFO

76

689 GitHub stars

Stars/forks activity

INFO

71

689 stars, 139 forks; issue activity unavailable in current metadata

Recent maintenance

INFO

62

8mo since push

License clarity

PASS

86

GPL-2.0

README/SKILL.md completeness

PASS

90

Metadata includes enough usage and workflow context

Dependency/runtime risk

INFO

80

external package install surface

Install availability

PASS

92

npx skills add pharmai/plip

Install command safety

PASS

92

standard package or runtime install path

Permission surface

PASS

86

filesystem or document access

Repository evidence

PASS

86

https://github.com/pharmai/plip

Review status

PASS

88

AI review data available

Checks

Install and adoption review

9 passed 路 3 review

Install path

92

PASS

npx skills add pharmai/plip

Repository

88

PASS

https://github.com/pharmai/plip

License

86

PASS

GPL-2.0

Maintenance

62

CHECK

8mo since push

AI review

88

PASS

Approved with no listed issues

README/SKILL.md completeness

90

PASS

Usable description available

Dependency risk

80

PASS

external package install surface

Install command safety

92

PASS

standard package or runtime install path

Permission surface

86

PASS

filesystem or document access

Stars/forks activity

71

CHECK

689 stars, 139 forks; issue activity unavailable in current metadata

Adoption

88

PASS

689 GitHub stars

Warnings

  • Quality score needs review

Method

This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.

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