Laporan audit skill
david-silver Laporan audit.
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.
Trust Score OpenAgentSkill
Trust Score OpenAgentSkill
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
Adopsi GitHub
Info62
122 star GitHub
Aktivitas star/fork
Peringatan57
122 star dan 18 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Pemeliharaan terbaru
Lulus88
2 bulan sejak push
Kejelasan lisensi
Lulus86
MIT
Kelengkapan README/SKILL.md
Info76
Metadata publik memerlukan konteks README/SKILL.md yang lebih kuat
Risiko dependensi/runtime
Lulus90
Tidak ada petunjuk risiko dependensi besar dalam metadata publik
Ketersediaan pemasangan
Lulus92
npx skills add K-Dense-AI/mimeographs --skill david-silver
Keamanan perintah pemasangan
Lulus92
Jalur pemasangan paket atau runtime standar
Cakupan izin
Lulus100
Tidak ada cakupan izin berisiko tinggi dalam metadata publik
Bukti repositori
Lulus86
https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/david-silver
Status peninjauan
Info66
Data tinjauan AI tersedia
Hasil terbukti Agent
Info54
Belum ada data hasil Agent
Pemeriksaan
Tinjauan pemasangan dan adopsi
Jalur pemasangan
92
npx skills add K-Dense-AI/mimeographs --skill david-silver
Repositori
88
https://github.com/K-Dense-AI/mimeographs/tree/main/mimeographs/david-silver
Lisensi
86
MIT
Pemeliharaan
88
2 bulan sejak push
Tinjauan AI
55
Core principles contain duplication: the first three principles all express the same 'discard human data to break performance ceiling' idea with cosmetic rewording.
Kelengkapan README/SKILL.md
84
Usable description available
Risiko dependensi
90
Tidak ada petunjuk risiko dependensi besar dalam metadata publik
Keamanan perintah pemasangan
92
Jalur pemasangan paket atau runtime standar
Cakupan izin
100
Tidak ada cakupan izin berisiko tinggi dalam metadata publik
Aktivitas star/fork
57
122 star dan 18 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Adopsi
68
122 star GitHub
Peringatan
- 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
Metode
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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