Skill-Audit-Bericht
david-silver Audit-Bericht.
Applies the reasoning of David Silver, lead researcher on AlphaGo and AlphaZero at DeepMind, to problems of AI design, reinforcement learning, and open-ended discovery. Use this skill whenever you are designing AI systems, evaluating learning algorithms, balancing exploration vs. exploitation, choosing research problems, or discussing how to break past human performance ceilings. Reach for this whenever the user asks about self-play, Monte-Carlo Tree Search, tabula rasa learning, AGI, or moving from human-curated data to autonomous experience. It helps shift the focus from hardcoding human knowledge to building systems that learn for themselves.
OpenAgentSkill Trust Score
OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub-Akzeptanz
Info62
122 GitHub-Stars
Star-/Fork-Aktivität
Warnung57
122 Stars und 18 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbar
Aktuelle Wartung
Bestanden88
2 Monate seit dem letzten Push
Lizenzklarheit
Bestanden86
MIT
README/SKILL.md-Vollständigkeit
Info76
Öffentliche Metadaten benötigen mehr README/SKILL.md-Kontext
Abhängigkeits-/Laufzeitrisiko
Bestanden90
Keine wesentlichen Abhängigkeitsrisikohinweise in öffentlichen Metadaten
Installationsverfügbarkeit
Bestanden92
npx skills add K-Dense-AI/mimeographs --skill david-silver
Sicherheit des Installationsbefehls
Bestanden92
Standard-Paket- oder Laufzeit-Installationspfad
Berechtigungsumfang
Bestanden100
Keine Hochrisiko-Berechtigungsfläche in öffentlichen Metadaten
Repository-Nachweis
Bestanden86
https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/david-silver
Prüfstatus
Info66
KI-Prüfdaten verfügbar
Agent-validierte Ergebnisse
Info54
Noch keine Agent-Ergebnisdaten
Prüfungen
Installations- und Adoptionsprüfung
Installationspfad
92
npx skills add K-Dense-AI/mimeographs --skill david-silver
Repository
88
https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/david-silver
Lizenz
86
MIT
Wartung
88
2 Monate seit dem letzten Push
KI-Prüfung
55
Core principles contain duplication: the first three principles all express the same 'discard human data to break performance ceiling' idea with cosmetic rewording.
README/SKILL.md-Vollständigkeit
84
Usable description available
Abhängigkeitsrisiko
90
Keine wesentlichen Abhängigkeitsrisikohinweise in öffentlichen Metadaten
Sicherheit des Installationsbefehls
92
Standard-Paket- oder Laufzeit-Installationspfad
Berechtigungsumfang
100
Keine Hochrisiko-Berechtigungsfläche in öffentlichen Metadaten
Star-/Fork-Aktivität
57
122 Stars und 18 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbar
Akzeptanz
68
122 GitHub-Stars
Warnungen
- Core principles contain duplication: the first three principles all express the same 'discard human data to break performance ceiling' idea with cosmetic rewording.
- The skill lacks concrete RL engineering heuristics (e.g., baseline subtraction, eligibility traces, exploration scaling) that are present in the source corpus, making it less actionable for actual ML engineering.
- Frameworks and heuristics sections omit source attributions (src_XXX tags) that would improve traceability and credibility.
- Quality score needs review
- Stars/forks activity: 122 stars, 18 forks; issue activity unavailable in current metadata
Methode
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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