Rapport d’audit du skill
alterlab-alphafold Rapport d’audit.
Predict protein 3D structures with AlphaFold2 via ColabFold — MMseqs2-accelerated MSAs, monomer and AlphaFold2-Multimer complex folding, and confidence-based validation (pLDDT, pTM/ipTM, PAE). Use when folding a protein sequence or complex from FASTA, generating a predicted structure with confidence metrics, ranking models, or checking self-consistency of a design. For co-folding a protein WITH a small-molecule ligand or predicting binding affinity prefer alterlab-boltz; for antibody–antigen or one-FASTA multi-entity complexes prefer alterlab-chai; to LOOK UP an already-computed structure prefer alterlab-alphafold-db; for ESM embeddings or inverse folding prefer alterlab-esm. Part of the AlterLab Academic Skills suite.
Trust Score OpenAgentSkill
Trust Score OpenAgentSkill
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
Adoption GitHub
Avertissement48
66 stars GitHub
Activité stars/forks
Avertissement48
66 stars et 13 forks; l’activité des issues n’est pas disponible dans les métadonnées actuelles
Maintenance récente
Validé88
1 mois depuis le dernier push
Clarté de licence
Validé86
MIT
Complétude README/SKILL.md
Validé86
Les métadonnées incluent suffisamment de contexte d’usage et de workflow
Risque dépendances/runtime
Avertissement56
command execution surface, network or browser surface
Disponibilité de l’installation
Validé92
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-alphafold
Sécurité de la commande d’installation
Validé92
Chemin d’installation standard de package ou runtime
Surface de permissions
Avertissement50
shell or command execution, network or browser access
Preuve du dépôt
Validé86
https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/tree/main/skills/bioinformatics/alterlab-alphafold
État de revue
Info66
Données de revue IA disponibles
Résultats prouvés par Agent
Info54
Pas encore de données de résultats Agent
Vérifications
Revue d’installation et d’adoption
Chemin d’installation
92
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-alphafold
Dépôt
88
https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/tree/main/skills/bioinformatics/alterlab-alphafold
Licence
86
MIT
Maintenance
88
1 mois depuis le dernier push
Revue IA
55
allowed-tools grants unrestricted Bash(python:*) and Bash(uv:*), which permits arbitrary code execution; this is broad even if common for bioinformatics tooling.
Complétude README/SKILL.md
86
Usable description available
Risque de dépendances
56
command execution surface, network or browser surface
Sécurité de la commande d’installation
92
Chemin d’installation standard de package ou runtime
Surface de permissions
50
shell or command execution, network or browser access
Activité stars/forks
48
66 stars et 13 forks; l’activité des issues n’est pas disponible dans les métadonnées actuelles
Adoption
68
66 stars GitHub
Avertissements
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- allowed-tools grants unrestricted Bash(python:*) and Bash(uv:*), which permits arbitrary code execution; this is broad even if common for bioinformatics tooling.
- SKILL.md contains TODO(verify) placeholders for exact ColabFold installation pins and flag names, reducing reproducibility and clarity for agent execution.
- The MSA step sends sequences to a public MMseqs2 API by default; the skill mentions disclosure for sensitive sequences but does not enforce or strongly gate this in the workflow.
- Setup instructions are incomplete: no exact package version, CUDA/JAX pin, or verified command sequence is provided for installing and running ColabFold.
- Quality score needs review
- Permission surface needs review: shell or command execution, network or browser access
- GitHub adoption: 66 GitHub stars
- Stars/forks activity: 66 stars, 13 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, network or browser surface
- Permission surface: shell or command execution, network or browser access
Méthode
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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