Rapport d’audit du skill
autoskill Rapport d’audit.
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
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
Validé100
34K stars GitHub
Activité stars/forks
Validé97
34K stars et 3.3K forks; l’activité des issues n’est pas disponible dans les métadonnées actuelles
Maintenance récente
Validé100
2 jours depuis le dernier push
Clarté de licence
Validé86
MIT license
Complétude README/SKILL.md
Validé86
Les métadonnées incluent suffisamment de contexte d’usage et de workflow
Risque dépendances/runtime
Avertissement46
command execution surface, credential or environment access
Disponibilité de l’installation
Validé92
npx skills add K-Dense-AI/scientific-agent-skills --skill autoskill
Sécurité de la commande d’installation
Validé92
Chemin d’installation standard de package ou runtime
Surface de permissions
Échoué22
secrets or environment access, shell or command execution
Preuve du dépôt
Validé86
https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/autoskill
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 K-Dense-AI/scientific-agent-skills --skill autoskill
Dépôt
88
https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/autoskill
Licence
86
MIT license
Maintenance
100
2 jours depuis le dernier push
Revue IA
55
The skill depends on the screenpipe daemon and a SCREENPIPE_TOKEN; if the daemon is not running or the token is invalid, the skill will fail. Documentation covers this, but a more explicit error-handling section in SKILL.md could help.
Complétude README/SKILL.md
86
Usable description available
Risque de dépendances
46
command execution surface, credential or environment access
Sécurité de la commande d’installation
92
Chemin d’installation standard de package ou runtime
Surface de permissions
22
secrets or environment access, shell or command execution
Activité stars/forks
97
34K stars et 3.3K forks; l’activité des issues n’est pas disponible dans les métadonnées actuelles
Adoption
88
34K stars GitHub
Avertissements
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- The skill depends on the screenpipe daemon and a SCREENPIPE_TOKEN; if the daemon is not running or the token is invalid, the skill will fail. Documentation covers this, but a more explicit error-handling section in SKILL.md could help.
- Cloud backends (Claude/Foundry) are optional but require the user to supply API keys; the skill does not perform additional runtime validation of the endpoint beyond the cleartext check, so a misconfigured (but HTTPS) remote endpoint could receive data. This is acceptable given the user explicitly configures it, but the documentation could emphasise the privacy implications more strongly.
- Permission surface needs review: secrets or environment access, shell or command execution
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
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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